Hyperpacked piezoelectric-powered capacitive sensor array for high-fidelity vibration detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Hyperpacked piezoelectric-powered capacitive sensor array for high-fidelity vibration detection Kilwon Cho, Kang Hyuk Cho, Jeng-Hun Lee, Seojin Yun, Siyoung Lee, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7193819/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Jan, 2026 Read the published version in Nature Sensors → Version 2 posted 10 You are reading this latest preprint version Show more versions Abstract Many physiological signals span a broad frequency spectrum, and high-fidelity capture across this range requires soft vibration sensors with exceptional performance. However, existing devices fall short of delivering uniformly high sensitivity across the full spectrum while maintaining reliable, low-power operation. Here, we present a new concept that employs a piezoelectric diaphragm as a non-contact power source for capacitive vibration sensing. Piezoelectric charges from the diaphragm establish a stable bias field and actively modulate the signal, enabling self-powered operation. To further enhance performance, we design an elegant device architecture that offers (i) in-plane air ventilation and (ii) maximized sensor array density. The resulting device exhibits remarkable linear sensitivity (626 mV g ⁻¹), flat frequency response (80–5,000 Hz), ultralow limit of detection (0.01 g ), and outstanding signal-to-noise ratio (80 dB), demonstrating notable improvements over conventional technology. We demonstrate that the hyperpacked, piezoelectric-powered capacitive sensor array enables high-fidelity detection of music, voice, and respiratory signals by capturing subtle mechanical vibrations. Physical sciences/Materials science/Materials for devices/Sensors and biosensors Physical sciences/Materials science/Materials for devices/Electronic devices Physical sciences/Engineering/Chemical engineering Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Main Vibration, a fundamental mechanical stimulus, transmits dynamic signals that extend beyond quasi-static responses such as strain 1–3 , shear 4,5 , and pressure 6–8 . Unlike these, vibration conveys unique information due to its (1) ultralow-intensity signals and (2) broad frequency range. These characteristics make it indispensable for applications including structural health monitoring, industrial maintenance, and human physiological sensing 9–11 . Notably, many vital physiological signals manifest as minute vibrations spanning a wide frequency spectrum—cardiac activity (10–250 Hz) 12 , human voice (150–450 Hz) 13 , and respiration (100–2000 Hz) 14 . Capturing these signals with high fidelity is crucial for next-generation wearable health monitoring 15,16 and human-machine interfaces 17–19 . To address this need, flexible vibration sensors capable of conforming to curved and soft surfaces, such as human skin, have garnered significant attention 20–27 . Among these, capacitive sensors stand out for their inherently flat frequency response and linear sensitivity 9,10 . Designs incorporating microstructured hydrogels 28–30 or through-hole patterned diaphragms 31–35 , for example, enable stable frequency characteristics, facilitating precise broadband physiological signal detection. However, existing designs face critical limitations: they often rely on bulky external power sources 29,31,33 or environmentally unstable electret materials 32,36,37 to provide bias voltage, which limits their practicality in wearable applications. Fabrication is also complex, often involving intricate features such as air vents, array configurations, or microscale patterning 28,29,32 . Moreover, their performance—and broader applicability—is constrained by a narrow flat frequency range, limited sensitivity, and poor signal-to-noise ratio (SNR) 29,31,32,38 . In this Article, we report a hyperpacked capacitive vibration sensor array that achieves high-fidelity broadband detection through a non-contact piezoelectric effect. Unlike conventional capacitive sensors that require external power sources or electret materials to generate a bias voltage, our design employs a piezoelectric poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) thin film as the diaphragm. Piezoelectric charges from the diaphragm sustain a built‑in bias field and drive the signal response, enabling stable and efficient self‑powered operation. To ensure scalable fabrication, we introduce a one-step photolithography process in which star-shaped supports promote in-plane air ventilation through side vent holes—eliminating the need for intricate structures and allowing an ultra-compact array configuration. As a result, the sensor exhibits a linear sensitivity of 626 mV g ⁻¹, a flat frequency response spanning 80–5,000 Hz, and an SNR of 80 dB, substantially outperforming existing vibration/acoustic sensors in key performance metrics. Demonstrated as a soft contact microphone, a laryngophone, and a wearable healthcare device, it enables high-fidelity detection of music, voice, and respiratory signals, offering new possibilities for next-generation human–machine interfaces and health monitoring. Hyperpacked vibration sensor array The hyperpacked capacitive vibration sensor array consists of a 15 μm-thick PVDF-TrFE film attached to a 40 μm-thick star-shaped patterned support layer, which is laminated onto a parylene substrate (Fig. 1a). Each unit sensor is supported by four star-shaped structures and features a central 1600 μm-diameter circular region, where the suspended piezoelectric film acts as a diaphragm to detect vibrations (Fig. 1b,c and Supplementary Fig. 1). Gold electrodes, positioned on the upper surfaces of both the piezoelectric film and the parylene substrate, detect capacitance variations induced by diaphragm motion (Fig. 1b). The star-shaped support structures are arranged in a regular array, enabling the formation of multiple unit vibration sensors densely connected in parallel (Fig. 1c and Supplementary Fig. 2). Additionally, an intermediate Au layer between the support and diaphragm layers enhances adhesion and suppresses noise from non-diaphragm regions. Our hyperpacked vibration sensor array represents a significant advancement over conventional capacitive vibration sensors, both in device architecture and material integration. From a design perspective, the star-shaped support structure plays a crucial role in enabling high-fidelity sensing, hyperpacked sensor configuration, and enhanced processability. In previous studies, air vent holes were introduced in the diaphragm or backplate to mitigate air-squeezed damping (Fig. 1d, left) 31–33,35,39,40 . In contrast, our approach eliminates air-squeezed damping by facilitating horizontal air circulation through individualized star-shaped supports (Fig. 1d, right). Numerical airflow simulations verify that this architecture provides effective, well-distributed ventilation across all unit cells (Supplementary Fig. 3 and Supplementary Video 1). This in-plane air ventilation minimizes thermal noise and improves sensitivity, as aligned airflow and diaphragm deflection can cause energy loss. Furthermore, the circular regions enclosed by the star-shaped supports function as diaphragms, allowing hyperpacked sensor configuration with zero wasted area (Fig. 1d, right). Eliminating air holes in the diaphragm or backplate not only simplifies fabrication via one‑step photolithography (Supplementary Figs. 4 and 5) but also preserves a larger proportion of the diaphragm as the active sensing area compared with conventional designs (Supplementary Fig. 6). Although removing through‑holes increases the effective stiffness of the circular diaphragm region, the star‑shaped support configuration enables greater overall displacement of the diaphragm layer within the unit cell (Supplementary Fig. 7). This architecture also yields a substantially lower initial capacitance than conventional designs. Together, these features enhance the relative capacitance change and thereby improve sensitivity (Supplementary Fig. 8). Furthermore, supporting each diaphragm with four discrete anchoring structures, rather than a single bulk frame, markedly improves flexibility under bending (Supplementary Fig. 9). From a materials standpoint, the device further distinguishes itself by incorporating a non-contact piezoelectric diaphragm—a simple yet highly effective strategy for more efficient, stable, and sensitive operation. This approach removes the need for bulky external power sources 31,33 or unstable electret layers 32,41,42 typically required for biasing in conventional capacitive sensors (Fig. 1d). Instead, remnant polarization within the piezoelectric material provides intrinsic charges that establish a stable initial bias field across the sensor. Mechanical vibrations drive diaphragm oscillations, inducing stress-dependent polarization and generating additional charges that directly contribute to the electrical output (Fig. 1e). Although the diaphragm does not physically contact the bottom electrode, the top and bottom electrodes are electrically connected through the interface circuitry (CKT), enabling charge redistribution (Δ Q ). At the same time, diaphragm motion changes the electrode spacing, producing a capacitance variation (Δ C ). The sensor’s electrical output is therefore governed by the combined effects of Δ Q and Δ C . In capacitive sensors, accumulated charge is typically required to convert small capacitance variations into measurable voltage signals—a process facilitated by an interface circuit with an amplifier, as described by V = Q / C (Supplementary Fig. 10) 29,31,32 . In this context, the sensing mechanism of our device can be described by a governing relation that incorporates simultaneous variations in both Q and C (Supplementary Note 1). In our device, piezoelectric charges from remnant polarization provide a stable bias for capacitive sensing, while motion-induced piezoelectric charges contribute as a smaller secondary term. The capacitive variation is expected to dominate the signal, with sensitivity maximized when the phases of Δ Q and Δ C are optimally aligned (Supplementary Note 1 and Supplementary Fig. 11). Characterization of the piezoelectric diaphragm The thin piezoelectric diaphragm was fabricated via spin-coating PVDF-TrFE dissolved in an organic solvent. Traditionally, highly polar solvents such as dimethylformamide (DMF) 43,44 , dimethyl sulfoxide (DMSO) 45,46 , and N-methyl-2-pyrrolidone (NMP) 47 have been predominantly used for PVDF-TrFE processing. However, these solvents pose significant health risks, including liver damage and reproductive toxicity 48 . To mitigate these concerns, we employed propyl acetate (PA), a less hazardous alternative, which fully dissolved PVDF-TrFE (Supplementary Fig. 12) and yielded a film with a high β-phase content. Fourier transform infrared (FT-IR) confirmed characteristic β-phase peaks at 1400 cm⁻¹, 1288 cm⁻¹, and 850 cm⁻¹, comparable to those observed with DMF 49–51 , demonstrating PA’s suitability as a safer solvent (Fig. 2a). Conventionally, the degree of poling in PVDF-TrFE has been assessed using one-dimensional (1D) X-ray diffraction (XRD), focusing on the peak intensity changes of the (110)/(200) β-phase diffraction peak near 20° and the (021) diffraction peak around 40.8°, which can emerge after poling 52,53 . However, this method has limited precision in evaluating the α-to-β phase transition and the alignment of β-phase crystals, as 1D XRD integrates diffraction data from all orientations into a single spectrum (Supplementary Fig. 13). To overcome the limitations of 1D XRD in assessing the α-to-β phase transition and crystallite alignment, we employed two-dimensional (2D) transmittance wide-angle X-ray scattering (TrWAXS) in both vertical and horizontal X-ray exposure directions (Supplementary Fig. 14). Vertical TrWAXS analysis revealed that, prior to poling, both α- and β-phases were present, whereas after poling, a clear α-to-β phase transition was observed (Fig. 2b, top). This transition was further corroborated by out-of-plane d -spacing measurements, which revealed that the polymer chains in the α-phase initially exhibited a d -spacing of 4.92 Å, while those in the β-phase exhibited 4.59 Å 54,55 . After poling, the α-phase disappeared, and the d -spacing of the β-phase decreased a little bit to 4.57 Å, indicating a slightly denser molecular arrangement characteristic of the (110)/(200) β-phase diffraction (Supplementary Fig. 15 and Supplementary Table 1) 56,57 . Meanwhile, horizontal TrWAXS analysis confirmed the well-aligned β-phase crystallites, demonstrating enhanced molecular ordering along the poling direction (Fig. 2b, bottom). Additionally, azimuthal angle-dependent intensity analysis revealed a pronounced increase in out-of-plane orientation, indicating preferential β-phase domain alignment (Supplementary Fig. 16). These results confirm that the poling process not only facilitated the α-to-β phase transition but also promoted β-phase crystallite alignment along the external electric field, which is perpendicular to the film thickness.To further validate this structural transition, we measured the piezoelectric coefficient ( d ₃₁) of the poled PVDF-TrFE film, which exhibited a significantly higher d ₃₁ value of approximately 15 pC N⁻¹ compared to the unpoled sample (Fig. 2c and Supplementary Fig. 17). This value falls within the range of previously reported d ₃₁ values for poled PVDF-TrFE (∼10–20 pC N⁻¹) 58–61 , reinforcing the reliability of our results (Supplementary Table 2). Although some β-phase was present in the unpoled PVDF-TrFE film, and thermal annealing may have induced partial dipole alignment, the dipoles within the β-phase domains largely remained randomly oriented (Fig. 2b, bottom). As a result, their contributions tended to cancel out, leading to the observed low d 31 value. Similar contrasts in piezoelectric performance between poled and unpoled (thermally annealed) PVDF-TrFE have been reported in previous studies 58,62,63 , further supporting the validity of our results. This enhancement in d ₃₁ strongly correlates with the observed structural transition, confirming the efficacy of the corona poling process. To evaluate the environmental stability of the piezoelectric diaphragm in comparison with a conventional electret diaphragm, we measured the relative output voltage of the PVDF-TrFE film and the relative surface potential of the CYTOP film before and after exposure to deionized water, artificial sweat, and direct skin contact (Fig. 2d). The PVDF-TrFE film retained most of its initial output voltage under all conditions, whereas the electret CYTOP film lost over 90% of its surface potential. The PVDF-TrFE film also maintained a stable d 31 across all conditions (Supplementary Fig. 18). This stark contrast arises from their fundamentally different charge mechanisms: piezoelectric materials retain stable remnant polarization and generate dynamic charges through lattice distortion, both inherently resistant to moisture and skin contact, whereas electret materials rely primarily on surface-trapped charges that readily degrade under environmental exposure. As a result, PVDF-TrFE maintains long-term performance without requiring additional protective measures, while electrets like CYTOP necessitate careful handling to prevent charge loss. Its exceptional moisture and skin-contact stability make PVDF-TrFE particularly well-suited for skin-attachable electronics. Vibration sensing performance of the hyperpacked sensor array The hyperpacked vibration sensor array was optimized by carefully tuning key structural parameters: the number of diaphragms and the thicknesses of both the support layer and the diaphragm (Fig. 3a,b). Increasing the diaphragm count and reducing the support layer thickness improved sensitivity (Fig. 3b, left and middle), with a maximum of 64 diaphragms accommodated within the 16 × 18 mm sensor area. Because the diaphragms are connected in parallel, their capacitance variations add linearly, producing a proportional increase in signal (Supplementary Fig. 2) 31,64,65 . Furthermore, since the first‑mode resonant frequency lies well above the sensor’s operating bandwidth, all diaphragms oscillate in the first vibration mode with in‑phase motion (Supplementary Fig. 19), ensuring that this proportional relationship between diaphragm count and sensitivity is fully realized. However, thinning the support layer below 40 µm proved impractical, as it caused the diaphragm to adhere to the bottom electrode during fabrication and drastically reduced production yield (Supplementary Fig. 20). Interestingly, the optimal diaphragm thickness for maximum sensitivity was found to be ~15 μm (Fig. 3b, right), contradicting previous reports that associate thinner diaphragms with higher sensitivity due to reduced stiffness and increased displacement 31,66 . This discrepancy arises because the piezoelectric diaphragm must retain sufficient thickness to provide enough piezoelectric charge; beyond 15 μm, excessive stiffness counteracts this effect, reducing sensitivity. The optimized device exhibited a high sensitivity of 626 mV g ⁻¹ while maintaining excellent linearity over a broad vibration acceleration range of 0.1–2.5 g (Fig. 3c). This superior linearity was attributed to the device structure, which features thin diaphragms; each diaphragm, with fixed boundaries set by four star-shaped supports, undergoes vertical displacement linearly proportional to the vibration amplitude (Supplementary Fig. 21) 35,67,68 . Furthermore, the sensor demonstrated outstanding long-term stability and durability, retaining both sensitivity and linearity even after 210 days under identical vibration conditions with 1,000 cycles (Supplementary Fig. 22). The sensor also showed remarkable environmental stability, preserving its sensitivity even after immersion in water (Supplementary Fig. 23). Beyond its high sensitivity and stability, the sensor featured an exceptionally flat frequency response within ±3 dB over a broad frequency range of 80–5,000 Hz (Fig. 3d). Although this flat response was not maintained below 50 Hz, the sensor still reliably detected static and low-frequency vibrations (Supplementary Fig. 24). In contrast, sensors fabricated using unpoled PVDF-TrFE and epoxy-based SU-8 diaphragms produced unstable signals, with outputs nearly indistinguishable from noise. Similarly, a control sensor with a PTFE diaphragm (a highly tribonegative material) generated only unstable, noise-like signals, ruling out triboelectric effects as the primary sensing mechanism (Supplementary Fig. 25). When the PVDF-TrFE diaphragm was slightly poled ( d ₃₁ = 4 pC N -1 ), the sensor immediately produced a stable and clearly readable signal, further confirming the critical role of piezoelectric charge in its operation (Supplementary Fig. 26). Additionally, air ventilation between the star-shaped supports effectively suppressed air-squeezed damping, as evidenced by the absence of the high-frequency roll-off characteristic of such damping (Supplementary Fig. 27) 31,69,70 . This ensured both high sensitivity and a stable, flat frequency response across a wide range. Moreover, the sensor exhibited an ultralow limit of detection (LOD), detecting minute vibration inputs as low as 0.01 g —a level corresponding to skin vibrations produced by soft breathing (Fig. 3e) 71 . Furthermore, the sensor achieved an exceptionally high SNR of ~80 dB across broad frequencies (Fig. 3f). Even when the noise power spectral density within the auditory frequency range was considered, the sensor maintained a high SNR of ~43 dB(A) under A-weighted conditions (Supplementary Fig. 28). This remarkable performance was attributed to the inherently low mechanical and electrical noise of the capacitive device structure 72 , and the exceptional sensitivity of our piezoelectric-powered capacitive sensor. These results indicate that the sensor is tailored for high-fidelity vibration detection across a wide amplitude and frequency range, capable of precisely measuring signals from extremely small to large amplitudes, even beyond the telephonic range (~3,000 Hz). This breakthrough opens new possibilities for a diverse range of vibration-based applications. High-fidelity audio recording via vibration sensing A sound-producing object generates subtle vibrations that propagate through solid media and can be captured for audio recording. Leveraging this principle, our sensor enables high-fidelity sound acquisition by precisely detecting mechanical vibrations. We validated its performance by benchmarking it against a commercial high-resolution accelerometer (352C33, PCB Piezotronics).Both devices were placed on a vibration speaker during recording (Supplementary Fig. 29), and the volume of the played music was kept constant to ensure a fair comparison. For the recordings, we used Spring (Movement 1) from The Four Seasons by Vivaldi, which features distinct cello and violin solo passages. The short-time Fourier transform (STFT) spectrogram of the original music revealed characteristic differences between the two instruments: the cello exhibited strong energy concentration in the lower frequency range with clear harmonic overtones, while the violin showed energy concentrated in the higher frequency range with finer, more detailed spectral features (Fig. 4a) 73 . The audio recording obtained with our sensor closely matched the original music, exhibiting an almost identical time-domain output waveform and STFT spectrogram while clearly capturing the distinct characteristics of both the cello and violin (Fig. 4b). More importantly, even when compared to the commercial accelerometer, our sensor demonstrated comparable performance, producing a similar output waveform and STFT spectrogram across a broad frequency range of up to 5,000 Hz (Fig. 4b,c). The recorded music exhibited minimal distortion and maintained high sound quality across the detection frequency range (Supplementary Video 2). While achieving similar audio recording performance to the commercial high-resolution accelerometer, our sensor offers distinct advantages: it is thinner, smaller, and, most importantly, highly flexible rather than rigid. Acoustic sensing performance of hyperpacked vibration sensor array Owing to its exceptional flexibility (Supplementary Fig. 9), our sensor conforms seamlessly to the neck, ensuring excellent skin contact and efficient vibration transfer. The amplitude of neck skin vibrations induced by vocalization has been reported to exhibit a linear correlation with vocal sound pressure 31,74 . Using this relationship, the sensor’s vibration sensitivity was mapped to an inferred acoustic sensitivity (Fig. 5a). The output voltage was recorded for input vibrations up to 2.5 g at 200, 300, and 400 Hz—within the human fundamental vocal frequency range (150–450 Hz) 13 . To account for inter-individual variability, we used the lower bound of the measured correlation between skin acceleration and vocal sound pressure (Supplementary Fig. 30), yielding an inferred acoustic sensitivity of 6.07 V Pa⁻¹ with a linear response. We emphasize that this inferred value is not an intrinsic sensor property and will vary between users. When attached to the neck, the sensor captured high-fidelity voice signals with clear waveform and spectral features (Fig. 5b and Supplementary Fig. 31). Since capacitive sensors are inherently susceptible to electromagnetic interference (EMI) 75 , we encapsulated the sensor within a compact, flexible shielding case (Supplementary Fig. 32) before attachment to minimize EMI effects. To evaluate its performance, we recorded a complex sentence— "Hi, my name is Kang Hyuk Cho. I am researching vibration sensors at POSTECH." —and analyzed the resulting waveforms and harmonic structures using STFT 76,77 . The spectral characteristics closely matched those captured by a commercial microphone (Fig. 5b, top: our sensor; bottom: commercial microphone). Moreover, even in acoustically challenging environments, the sensor effectively captured voice signals with minimal interference from ambient noise (Supplementary Fig. 33). This was achieved by detecting voice through neck-skin vibrations rather than airborne sound waves, while the flexible EMI shielding case further blocked external acoustic waves from reaching the diaphragm, ensuring that background noise had minimal impact. Importantly, for effective use in speech communication, the sensor must maintain high sensitivity across the dynamic range corresponding to normal conversation levels (40–60 dB SPL ). However, most state-of-the-art vibration and acoustic sensors exhibit low sensitivity in this range, or worse, their dynamic range does not fully cover it, instead focusing on levels above 60 dB SPL (Fig. 5c and Supplementary Table 3). Many of these sensors are characterized at sound levels exceeding 90 dB SPL , which are classified as harmful and can cause hearing damage with prolonged exposure. Measuring sensitivity at such high levels is impractical for everyday speech applications. In contrast, our sensor features a broad dynamic range, spanning from approximately 20 dB SPL (equivalent to whispering) to 90 dB SPL (comparable to the noise of a heavy truck), while maintaining consistently high linear sensitivity throughout (Fig. 5c). In addition to a broad dynamic range, maintaining a high and stable SNR across a wide frequency spectrum is essential for accurately capturing broadband physiological signals beyond voice. However, many existing sensors exhibit highly non-flat frequency responses, often reporting SNR only at their resonant frequency rather than across the full spectrum (Fig. 5d and Supplementary Table 3). In real-world applications, this results in significant signal distortion, requiring extensive post-processing for high-fidelity detection. While some recent designs incorporate multichannel resonant frequency arrays to address this limitation, they still experience a gradual SNR decline at higher frequencies, resembling damping effects. Capacitive sensors provide a more stable SNR, but their absolute values remain relatively low. In contrast, our sensor achieves an exceptionally high and consistent SNR across a broad frequency range (80–5,000 Hz), ensuring high-fidelity broadband signal acquisition. Respiratory disease diagnosis via vibration sensing Respiratory signals provide critical diagnostic information, yet their detection is often hindered by ambient noise and the weak signal strength of breath sounds 78,79 . A vibration sensor placed on the skin near the vocal cords offers a direct and reliable approach for capturing these signals by detecting subtle laryngeal vibrations. As the vocal cords open during inhalation and partially close during exhalation, they generate airflow-induced vibrations that propagate through surrounding tissue 80 . Positioning the sensor between the vocal cords and ribcage facilitates the detection of respiratory sounds associated with disorders such as stridor and crackles. Recent studies have demonstrated the feasibility of this method, underscoring its potential for diagnosing respiratory disease 81,82 . Notably, our sensor’s high-fidelity vibration detection allows for precise differentiation of respiratory sounds, improving diagnostic accuracy. To evaluate this capability, the sensor was placed on the skin near the vocal cords (Fig. 6a), and natural conversation was recorded for 20 seconds. The recorded output signals were transformed into a spectrogram using STFT to visually distinguish speech from respiratory sounds (Fig. 6b). Speech signals exhibited multiple harmonics spanning a broad frequency range, originating from the fundamental frequency of vocal cord vibrations. In contrast, respiratory sounds appeared as short, low-amplitude harmonic signals concentrated below 1 kHz with minimal spectral complexity. The sensor could detect breathing sounds at different respiration rates (slow, normal, and fast), with clear inhale and exhale events recorded for all conditions (Supplementary Fig. 34). This contrast enabled a clear separation between speech and respiratory signals. In normal breathing, inhalation and exhalation waveforms followed a regular pattern (Fig. 6c), with power spectral density (PSD) analysis showing no significant differences in frequency distribution between the two phases (Fig. 6d). In contrast, breathing signals from patients with stridor exhibited distinct differences in pitch between inhalation and exhalation, with sharp waveform peaks observed during the inhalation phase (Fig. 6e) 83,84 . PSD analysis of stridor patients' signals revealed a distinct inhalation spectral peak at ~350 Hz—a hallmark of turbulent airflow resulting from airway narrowing (Fig. 6f) 85 . The vibration sensor also captured a distinctive spectral peak characteristic of coarse crackles, another class of abnormal respiratory sounds (Supplementary Fig. 35). These results demonstrate that our sensor can effectively detect pathological respiratory signals, with observations closely aligning with previously reported clinical findings. This consistency suggests that the sensor may serve as a non-invasive and reliable tool for respiratory disease diagnostics. Conclusions We have reported a hyperpacked, piezoelectric-powered capacitive sensor array for broadband mechanical stimuli detection. The device architecture is based on a star-shaped support structure fabricated via a facile one-step photolithography process. This design facilitates air ventilation through side vent holes—eliminating the need for intricate structures—and enables an ultra-compact array configuration. Distinguishing itself from existing approaches, the sensor incorporates a non-contact piezoelectric diaphragm—a simple yet highly effective strategy for boosting sensitivity, stability, and energy efficiency. As a result, the device demonstrates a significant leap in sensing performance compared to previously reported vibration/acoustic sensors, with a linear sensitivity of 626 mV g ⁻¹, a flat frequency response spanning 80–5,000 Hz, an SNR of 80 dB, and excellent temporal and environmental stability—all without requiring an external bias voltage. We demonstrated high-fidelity recording of music, voice, and respiratory signals by detecting subtle mechanical vibrations. This architecture- and materials-driven approach opens new possibilities for broadband mechanical sensing, offering progressive solutions for voice-enabled Internet of Things systems and personalized mobile healthcare technologies. Methods Fabrication of hyperpacked vibration sensor array A PVDF-TrFE solution (18 wt%) was prepared by dissolving PVDF-TrFE powder (FC30, Piezotech®) in PA and stirring for 24 h. A glass wafer was sequentially coated with a Ti/Al (15/100 nm) sacrificial layer via thermal evaporation and a 50 nm Au top electrode via sputter deposition (E-1030 Ion Sputter, Hitachi). The PVDF-TrFE solution was then spin-coated to form a ~ 15 µm-thick diaphragm. To induce β-phase crystallization, thermal annealing was performed on a hotplate at 60°C for 10 min, 80°C for 60 min, and 130°C for 180 min. A second Au layer was sputtered onto the diaphragm to enhance adhesion with the support structure and suppress noise from non-diaphragm regions. A negative photoresist (SU-8 3050, Micro Chem) was spin-coated and patterned to define a 40 µm-thick star-shaped support. The Au layer not covered by the SU-8 support, forming the diaphragm region, was then etched (Gold Etchant, Standard, Sigma Aldrich). The piezoelectric diaphragm was subsequently poled via corona poling (see separate section for details). The Al sacrificial layer was removed using an Al etchant (aqueous solution: CuCl 2 , H₂O, HCl) 86 , completing the electrode/diaphragm/support stack. For the bottom electrode, a Ti/Au (5/50 nm) layer was thermally deposited onto a 20 µm-thick Parylene-C substrate, which had been pre-coated with Ti/Al sacrificial layers. A diluted SU-8 2000 solution (17.8 wt%) was used to form an ultrathin adhesive layer on the bottom electrode. The top stack was laminated onto the bottom electrode/substrate assembly on a hot plate at 60°C, while mechanical pressing was applied simultaneously to ensure strong bonding. Final etching of the Al sacrificial layer yielded the free-standing, hyperpacked vibration sensor array. Corona poling of piezoelectric diaphragm The diaphragm was poled using a corona discharge method. The experimental setup was constructed within a plastic chamber to ensure electrical isolation from the surrounding environment. Inside the chamber, a conductive needle array and a grounded metal plate were positioned 4 cm apart, facing each other. Poling was performed by placing the diaphragm directly beneath the needle array. The needle, fabricated from stainless steel, had a tip curvature radius of approximately 50 µm and a cone angle of ~ 6°. A direct current (DC) voltage was supplied by a function generator (33510B, KEYSIGHT) and subsequently amplified using a high-voltage amplifier (20/20C-HS, Trek). The amplified voltage was applied to the conductive needle, while the metal plate was grounded. The poling process was carried out at room temperature by applying a DC voltage of 10 kV for 30 min. Evalutation of piezoelectric properties To compare the degree of α- and β-phase formation in PVDF-TrFE diaphragms depending on solvent selection, free-standing PVDF-TrFE films prepared using DMF and PA as solvents were analyzed via FT-IR (Vertex 70V, Bruker). The transition to the β-phase and the degree of molecular alignment following poling were further investigated using XRD (D/MAX-2500-PC, Rigaku) with CuKα radiation over a 2θ range of 5°–50°, as well as TrWAXS. TrWAXS measurements were conducted at the 3C SAXS-I beamline of the Pohang Accelerator Laboratory (PLS-II, PAL), Korea, and the acquired images were processed using p-GIXS software 87 . The piezoelectric coefficient was measured following a previously reported method 58 , using a universal tensile machine (LS1, AMETEK) in conjunction with an electrometer (B2987A, Keysight). Device characterization The diaphragm thickness was measured using an optical profilometer (Alpha-step D-500, KLA). To ensure accurate thickness determination, measurements were taken at three different locations on more than three samples. OM and SEM images were acquired using an optical microscope (ZEISS) and an SEM (S-4800, HITACHI), respectively. Numerical simulations were performed using COMSOL Multiphysics 6.2, incorporating solid mechanics, electrostatics, pressure acoustics, and nonlinear physics modules. The output voltage and real-time fast Fourier transform (FFT) data were recorded using an oscilloscope (TDS 3054C, Tektronix) and a signal analyzer (SR785, Stanford Research Systems), respectively, while mechanical input vibrations were generated using a mini vibration exciter (Type 4810, Brüel & Kjær) (Supplementary Fig. 36). Vibration sensitivity was defined as the output voltage of the device relative to that of a reference accelerometer (PCB Piezotronics, 352C33), which has a constant sensitivity of 100 mV g ⁻¹ across 10 Hz to 10 kHz. To eliminate potential interference from electromagnetic waves emitted by nearby electronic devices, the sensor and circuit were enclosed in an aluminum shielding box. During the frequency sweep (80–5000 Hz), over 800 measurement points were acquired, uniformly distributed on a logarithmic scale. Vibration-based audio recording A vibration speaker (VBT-001, Newadin Technology) was used for music playback, with both a commercial accelerometer (352C33, PCB Piezotronics) and the fabricated sensor positioned on the vibrating disk to capture the music signal. To ensure a fair performance comparison, the playback volume was kept constant across all measurements. The fabricated sensor was covered with a Ni/Al (10/100 nm)-coated polyimide film (thickness: 150 µm) and electrically connected to ground to provide EMI shielding. For the recording demonstration, excerpts from orchestral music ( Spring , Movement 1, The Four Seasons , Vivaldi) were used. To comply with copyright regulations, the original audio was obtained from Musopen ( https://musopen.org/music/ ), which distributes content under the Creative Commons Public Domain Dedication 1.0. The recorded signals were acquired using an external oscilloscope (TBS 2000 Series, Tektronix) and processed in MATLAB (R2023a, MathWorks) to generate STFT spectrograms and reconstruct the audio signal. Attachment to skin for voice and respiratory signal detection Prior to application on the human neck, the sensor was encapsulated with the aforementioned flexible EMI shielding case. To ensure secure adhesion to the skin, a biocompatible adhesive (LP-001, ABLE C&C Co., Ltd.), commonly used in cosmetics and easily removable with a makeup remover, was applied to the bottom substrate. Additionally, an ultrathin, transparent medical film (Tegaderm Film 1622W, 3M) was placed over the sensor and electrical connections to enhance stability and minimize motion artifacts. For voice and respiratory signal acquisition, the sensor was positioned near the vocal cords (Supplementary Fig. 22). A commercial microphone (Mic-12, Actto) simultaneously recorded speech for comparative analysis. To evaluate sensor performance in a noisy environment, artificial background noise at 80 dB SPL was introduced during one of the recordings. For normal respiratory signal acquisition, data were directly obtained from the sensor attached near the vocal cords. For abnormal respiratory signals, open-access pathological breathing sounds were played through the vibration speaker. A rubber layer was added to the vibrating disk to mimic the mechanical properties of soft human tissue, such as the larynx. Pathological breathing sounds like stridor have been recorded in the laryngeal region 85 , 88 . All signals were recorded using the oscilloscope and processed in MATLAB. Declarations Ethical approval This study was approved by the Institutional Review Board of Pohang University of Science and Technology (PIRB-2025-052). All procedures were conducted in accordance with the approved guidelines and regulations. Informed consent was obtained from the participants prior to their involvement in the study. Additionally, all individuals depicted in the images provided written consent for publication. Author information These authors contributed equally: Kang Hyuk Cho, Dr. Jeng-Hun Lee Competing interests K.H.C., J.-H. L., and K.C. are inventors of a KR patent related to this work. All other authors declare no completing interests. Author contributions K.H.C. and J.-H.L. conceived the idea, performed the experiments, analyzed the data, created the figures, and wrote the manuscript. S.Y., S.L., and S.C. conducted material characterizations and numerical simulations. W.K. and W.M. designed the experimental setups. Y.K. and Y.C. developed the interface circuit for the sensor. K.C. supervised the entire research project. Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Ministry of Science and ICT (MSIT) (2021M3C1C3097512). Data availability The data that support the findings of this study are available from the corresponding authors upon reasonable request. References Lee, J. et al. Stretchable and suturable fibre sensors for wireless monitoring of connective tissue strain. Nat . Electron . 4 , 291–301 (2021). Kim, K. K. et al. A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition. Nat . Electron . 6 , 64–75 (2023). Zhou, Z. et al. Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays. Nat . Electron . 3 , 571–578 (2020). Aksoy, B. et al. Shielded soft force sensors. Nat . Commun . 13 , 4649 (2022). Peng, Y., Peng, H., Chen, Z. & Zhang, J. Ultrasensitive Soft Sensor from Anisotropic Conductive Biphasic Liquid Metal-Polymer Gels. Adv . Mater . 36 , 2305707 (2024). Kim, S. W. et al. Mechanically Robust and Linearly Sensitive Soft Piezoresistive Pressure Sensor for a Wearable Human-Robot Interaction System. ACS Nano 18 , 3151–3160 (2024). Lee, J. H. et al. Rational Design of All Resistive Multifunctional Sensors with Stimulus Discriminability. Adv . Funct . Mater . 32 , 2107570 (2022). Chun, S. et al. An artificial neural tactile sensing system. Nat Electron 4 , 429–438 (2021). Lee, J. H., Cho, K. H. & Cho, K. Emerging Trends in Soft Electronics: Integrating Machine Intelligence with Soft Acoustic/Vibration Sensors. Adv . Mater . 35 , 2209673 (2023). Lin, Z. et al. Insights into Materials, Physics, and Applications in Flexible and Wearable Acoustic Sensing Technology. Adv . Mater . 36 , 2306880 (2024). Shen, R. et al. High-sensitivity and high-resolution triboelectric acoustic sensor for mechanical equipment monitoring. Nano Energy 133 , 110450 (2025). Nayeem, O. G. et al. All-nanofiber-based, ultrasensitive, gas-permeable mechanoacoustic sensors for continuous long-term heart monitoring. Proc. Natl. Acad. Sci. U.S.A. 117 , 7063–7070 (2020). Melton, J., Bradford, Z. & Lee, J. Acoustic Characteristics of Vocal Sounds Used by Professional Actors Performing Classical Material Without Microphones in Outdoor Theatre. J. Voice 36 , 733.e23–733.e29 (2022). Sabry, A. H., Dallal Bashi, O. I., Nik Ali, N. H. & Al Kubaisi, Y. M. Lung disease recognition methods using audio-based analysis with machine learning. Heliyon 10 , e26218 (2024). Wang, R. et al. Molecular Ferroelectrics for Highly Sensitive Detection Toward Low-Frequency Sound Recognition. Adv . Mater . 37 , 2409251 (2025). Hui, X. et al. Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis. Adv . Mater . 36 , 2401508 (2024). Qiao, W. et al. A Highly-Sensitive Omnidirectional Acoustic Sensor for Enhanced Human–Machine Interaction. Adv . Mater . 36 , 2413086 (2024). Lan, B. et al. Multichannel Gradient Piezoelectric Transducer Assisted with Deep Learning for Broadband Acoustic Sensing. ACS Appl . Mater . Interfaces 15 , 12146–12153 (2023). Xiang, Z. et al. High-performance microcone-array flexible piezoelectric acoustic sensor based on multicomponent lead-free perovskite rods. Matter 6 , 554–569 (2023). Gao, S. et al. High-bandwidth self-powered vibration sensors based on triboelectric particle-surface interactions. Nano Energy 119 , 109060 (2024). Yu, Z. et al. Integrated piezo-tribo hybrid acoustic-driven nanogenerator based on porous MWCNTs/PVDF-TrFE aerogel bulk with embedded PDMS tympanum structure for broadband sound energy harvesting. Nano Energy 97 , 107205 (2022). Jin, B. et al. Flexible Hair-Like Piezoelectric Acoustic Particle Velocity Sensor with Enhanced Sensitivity for Speaker Recognition. Adv . Funct . Mater . 35 , 2417164 (2024). Che, Z. et al. Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system. Nat . Commun . 15 , 1873 (2024). Shao, H. et al. Efficient conversion of sound noise into electric energy using electrospun polyacrylonitrile membranes. Nano Energy 75 , 104956 (2020). Zhao, X. et al. A self-filtering liquid acoustic sensor for voice recognition. Nat . Electron . 7 , 924–932 (2024). Gong, S. et al. A Soft Resistive Acoustic Sensor Based on Suspended Standing Nanowire Membranes with Point Crack Design. Adv . Funct . Mater . 30 , (2020). Wang, H. S. et al. Biomimetic and Flexible Piezoelectric Mobile Acoustic Sensors with Multiresonant Ultrathin Structures for Machine Learning Biometrics. Sci. Adv . 7 , eabe5683 (2021). Zhao, J. et al. Capacitive Low-Frequency Hydrophone Based on Micronanostructured Iontronic Hydrogel for Underwater Monitoring. ACS Nano 18 , 22010–22020 (2024). Guo, H. et al. Iontronic Dynamic Sensor with Broad Bandwidth and Flat Frequency Response Using Controlled Preloading Strategy. ACS Nano 18 , 5599–5608 (2024). Yang, R. et al. Iontronic pressure sensor with high sensitivity over ultra-broad linear range enabled by laser-induced gradient micro-pyramids. Nat . Commun . 14 , 2907 (2023). Lee, S. et al. An ultrathin conformable vibration-responsive electronic skin for quantitative vocal recognition. Nat . Commun . 10 , 2468 (2019). Lee, S. et al. An Electret-Powered Skin-Attachable Auditory Sensor that Functions in Harsh Acoustic Environments. Adv . Mater . 34 , 2205537 (2022). Lee, S. et al. A High-Fidelity Skin-Attachable Acoustic Sensor for Realizing Auditory Electronic Skin. Adv . Mater . 34 , 2109545 (2022). Peng, T. H. & Huang, J. H. The Effect of Compliant Backplate on Capacitive MEMS Microphones. IEEE Sens . J . 24 , 17803–17811 (2024). Naderyan, V., Raspet, R. & Hickey, C. Analytical, computational, and experimental study of thermoviscous acoustic damping in perforated micro-electro-mechanical systems with flexible diaphragm. J . Acoust . Soc . Am . 150 , 2749–2756 (2021). Lin, S. et al. Multiplying the Stable Electrostatic Field of Electret Based on the Heterocharge-Synergy and Superposition Effect. Adv . Sci . 9 , 2203150 (2022). Li, H. et al. Nanocomposite electret with surface potential self-recovery from water dipping for environmentally stable energy harvesting. Nano Energy 64 , 103913 (2019). Wang, Y. et al. Highly stable and ultra-fast vibration-responsive flexible iontronic sensors for accurate acoustic signal recognition. Nanoscale 16 , 22021–22028 (2024). Lee, J. P. et al. Encoding of multi-modal emotional information via personalized skin-integrated wireless facial interface. Nat . Commun . 15 , 530 (2024). Sun, H. et al. Graphene-based dual-function acoustic transducers for machine learning-assisted human–robot interfaces. InfoMat 5 , e12385 (2023). Ren, C. et al. Electret mechano-sensor array integrated with tribopotential-modulated thin film transistors for precise spatiotemporal pressure perception. Nano Energy 132 , 110351 (2024). Yasuda, T. et al. Ultra-Rapidly Responsive Electret-Based Flexible Pressure Sensor via Functional Polymeric Nanoparticle Synthesis. Adv . Funct . Mater . 34 , 2402064 (2024). Jiang, Y. et al. Ultrathin Eardrum-Inspired Self-Powered Acoustic Sensor for Vocal Synchronization Recognition with the Assistance of Machine Learning. Small 18 , 2106960 (2022). Lee, S., Kim, W., Park, N. C. & Park, J. W. Frequency Selectivity via Inner Boundary Conditions for A Self-Powered Multiresonant Acoustic Sensing Array with Broad Bandwidth. Adv . Funct . Mater . 33 , 2306026 (2023). Li, B. et al. Ultrasensitive mechanical/thermal response of a P(VDF-TrFE) sensor with a tailored network interconnection interface. Nat . Commun . 14 , 4000 (2023). Park, J. et al. Frequency-Selective Acoustic and Haptic Smart Skin for Dual-Mode Dynamic/Static Human-Machine Interface. Sci. Adv . 8 , abj9220 (2022). Zhang, Z. et al. Enhanced flexible piezoelectric sensor by the integration of P(VDF-TrFE)/AgNWs Film with a-IGZO TFT. IEEE Electron Device Lett. 40 , 111–114 (2019). Sherwood, J., Albericio, F. & de la Torre, B. G. N,N-Dimethyl Formamide European Restriction Demands Solvent Substitution in Research and Development. ChemSusChem 17 , e202301639 (2024). Resende, P. M., Isasa, J. D., Hadziioannou, G. & Fleury, G. Deciphering TrFE Fingerprints in P(VDF-TrFE) by Raman Spectroscopy: Defect Quantification and Morphotropic Phase Boundary. Macromolecules 56 , 9673–9684 (2023). Mao, D., Binh, E. & Madani, A. Ferroelectric Properties and Polarization Switching Kinetic of Poly (vinylidene fluoride-trifluoroethylene) Copolymer. in Ferroelectrics - Physical Effects (InTech, 2011). Apelt, S. et al. Poly(vinylidene fluoride-co-trifluoroethylene) Thin Films after Dip- and Spin-Coating. Macromol . Mater . Eng . 307 , 2200296 (2022). Zhang, N. et al. Ultra-high electrostriction and ferroelectricity in poly (vinylidene fluoride) by ‘printing of charge’ throughout the film. Nat . Commun . 16 , 744 (2025). Ryu, J., No, K., Kim, Y., Park, E. & Hong, S. Synthesis and Application of Ferroelectric Poly(Vinylidene Fluoride-co-Trifluoroethylene) Films using Electrophoretic Deposition. Sci . Rep . 6 , 36176 (2016). Stolichnov, I. et al. Cold-field switching in PVDF-TrFE ferroelectric polymer nanomesas. Phys . Rev . Lett . 108 , 027603 (2012). Shehzad, M., Shehzad, M. & Wang, Y. Structural Tailing and Pyroelectric Energy Harvesting of P(VDF-TrFE) and P(VDF-TrFE-CTFE) Ferroelectric Polymer Blends. ACS Omega 5 , 13712–13718 (2020). Li, Y. et al. Investigation on in-situ sprayed, annealed and corona poled PVDF-TrFE coatings for guided wave-based structural health monitoring: From crystallization to piezoelectricity. Mater. Des. 199 , (2021). Roy, D., Chakraborty, M., Pattader, P. S. G., Islam, A. K. M. M. & Bandyopadhyay, D. Role of annealing with electric field toward improvement of ferroelectric and electroactive properties of PVDF copolymer and terpolymer thin films. Macromol. Rapid Commun. 45 , 2400496 (2024). Yan, W. et al. Single fibre enables acoustic fabrics via nanometre-scale vibrations. Nature 603 , 616–623 (2022). Wang, K., Godfroid, T., Robert, D. & Preumont, A. Electrostrictive PVDF-TrFE thin film actuators for the control of adaptive thin shell reflectors. Actuators 9 , 53 (2020). Ducrot, P.-H., Dufour, I. & Ayela, C. Optimization of PVDF-TrFE processing conditions for the fabrication of organic MEMS resonators. Sci. Rep. 6 , 19426 (2016). Wang, H., Zhang, Q. M., Cross, L. E. & Sykes, A. O. Piezoelectric, dielectric, and elastic properties of poly(vinylidene fluoride/trifluoroethylene). J. Appl. Phys. 74 , 3394–3398 (1993). Hu, X., You, M., Yi, N., Zhang, X. & Xiang, Y. Enhanced piezoelectric coefficient of PVDF-TrFE films via in situ polarization. Front. Energy Res. 9 , 621540 (2021). Wen, D., Chen, X., Huang, F., Zhang, J., Yang, P., Li, R., Lu, Y. & Liu, Y. Piezoelectric and magnetoelectric effects of flexible magnetoelectric heterostructure PVDF-TrFE/FeCoSiB. Int. J. Mol. Sci. 23 , 15992 (2022). Kronast, W., Muller, B., Siedel, W. & Stoffel, A. Single-chip condenser microphone using porous silicon as sacrificial layer for the air gap. Proc. MEMS 98, Eleventh Annual Int. Workshop on Micro Electro Mechanical Systems (25–29 January 1998), 123–128 (1998). Wang, W., Yang, T., Chen, X. & Yao, X. Vibration energy harvesting using a piezoelectric circular diaphragm array. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 59 , 2022–2026 (2012). Beranek, L. L. & Mellow, T. J. Cellphone acoustics. in Acoustics: Sound Fields and Transducers 391–406 (Elsevier, 2012). doi:10.1016/b978-0-12-391421-7.00008-7. Kanekal, D. & Jindal, S. K. Prefabrication design, theoretical framework and simulation demonstration of a meander-shaped MEMS piezoresistive pressure sensor implanted on silicon substrate circular diaphragm for enhancement of key performance parameters utilized for low-pressure applications. J . Comput . Electron . 23 , 433–447 (2024). Kang, D. hee et al. A Self-Powered, Highly Sensitive, and Frequency-Tunable Triboelectric Acoustic Sensor Inspired by the Human Cochlea. Adv . Funct . Mater . 34 , 2408344 (2024). Yang, Y.-J. & Senturia, S. D. Numerical simulation of compressible squeezed-film damping. Proc. Solid-State Sensors, Actuators, and Microsystems Workshop (Hilton Head Island, South Carolina, 2–6 June 1996), 76–79 (1996). Ranjbar, A., Mehrabani, M. T. & Pary, F. T. A numerical study on the viscous damping effect for a condenser microphone. IEEE Sens. J. 11 , 1307–1316 (2011). Kano, S. & Mekaru, H. Preliminary comparison of respiratory signals using acceleration on neck and humidity in exhaled air. Microsyst. Technol. 27 , 1–9 (2021). Martin, D. T. et al. A micromachined dual-backplate capacitive microphone for aeroacoustic measurements. J. Microelectromech. Syst. 16 , 1289–1302 (2007). Slizovskaia, O., Haro, G. & Gómez, E. Conditioned Source Separation for Musical Instrument Performances. IEEE/ACM Trans. Audio Speech Lang. Process 29 , 2083–2095 (2021). Švec, J. G., Titze, I. R. & Popolo, P. S. Estimation of sound pressure levels of voiced speech from skin vibration of the neck. J. Acoust. Soc. Am. 117 , 1386–1394 (2005). Yoo, D., Won, D. J., Cho, W., Lim, J. & Kim, J. Double Side Electromagnetic Interference-Shielded Bending-Insensitive Capacitive-Type Flexible Touch Sensor with Linear Response over a Wide Detection Range. Adv . Mater . Technol . 6 , 2100358 (2021). Han, J. H. et al. Machine learning-based self-powered acoustic sensor for speaker recognition. Nano Energy 53 , 658–665 (2018). Jung, Y. H. et al. Deep learning-based noise robust flexible piezoelectric acoustic sensors for speech processing. Nano Energy 101 , 107610 (2022). Mitsuke, T., Shimakawa, H. & Harada, H. Respiratory Disease Diagnosis through Comprehensive Analysis of Spectrograms of Lung Sounds. in Human Interaction & Emerging Technologies (IHIET 2023): Artificial Intelligence & Future Applications vol. 111 (AHFE International, 2023). Kim, Y. et al. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci . Rep . 11 , 17186 (2021). Brancatisano, T., Collett, P. W. & Engel, L. A. Respiratory movements of the vocal cords. J . Appl . Physiol . 54 , 1269–1276 (1983). Gong, S. et al. Hierarchically resistive skins as specific and multimetric on-throat wearable biosensors. Nat . Nanotechnol . 18 , 889–897 (2023). Yoo, J. Y. et al. Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring. Nat . Med . 29 , 3137–3148 (2023). Andrs, E. Advances and perspectives in the field of auscultation, with a special focus on the contribution of new intelligent communicating stethoscope systems in clinical practice, in teaching and telemedicine. In eHealth and Remote Monitoring (InTech, 2012). Lee, K.-R. et al. A Wearable Stethoscope for Accurate Real-Time Lung Sound Monitoring and Automatic Wheezing Detection Based on an AI Algorithm. Engineering (2025) doi:10.1016/j.eng.2024.12.031. Ozawa, T., Sekiya, K., Aizawa, N., Terajima, K. & Nishizawa, M. Laryngeal stridor in multiple system atrophy: Clinicopathological features and causal hypotheses. J. Neurol. Sci. 361 , 243–249 (2016). Cakir, O. Copper etching with cupric chloride and regeneration of waste etchant. J. Mater. Process. Technol. 175 , 63–68 (2006). Kim, Y. Y., Kim, J. & Kim, N. pGIXS: PLS-II 3C SAXS beamline data plot program with MATLAB (0.1 version) . Zenodo (2022). https://doi.org/10.5281/zenodo.7042272 Douros, K., Grammeniatis, V. & Loukou, I. Crackles and other lung sounds. In Breath Sounds: From Basic Science to Clinical Practice (eds Priftis, K. N., Hadjileontiadis, L. J. & Everard, M. L.) 225–236 (Springer International Publishing, Cham, 2018). Additional Declarations Yes there is potential Competing Interest. K.H.C., J.-H. L., and K.C. are inventors of a KR patent related to this work. All other authors declare no completing interests. Supplementary Files RevisedSupplementaryVideo1NATSENSORS25050262KCho.mp4 Air flow simulation during diaphragm fluctuation RevisedSupplementaryVideo2NATSENSORS25050262KCho.mp4 Demonstration of high-fidelity audio recording via vibration sensing RevisedSupplementaryInformationNATSENSORS25050262KCho.docx Supplementary information for hyperpacked piezoelectric-powered capacitive sensor array for high-fidelity vibration detection ReportingSummaryNATSENSORS25050262KCho.pdf Reporting Summary Cite Share Download PDF Status: Published Journal Publication published 15 Jan, 2026 Read the published version in Nature Sensors → Version 2 posted Editorial decision: revise 08 Sep, 2025 Review # 2 received at journal 07 Sep, 2025 Review # 1 received at journal 02 Sep, 2025 Reviewer # 2 agreed at journal 22 Aug, 2025 Reviewers invited by journal 22 Aug, 2025 Reviewer # 1 agreed at journal 22 Aug, 2025 Submission checks completed at journal 19 Aug, 2025 First submitted to journal 18 Aug, 2025 Unknown event 18 Aug, 2025 Editor assigned by journal 18 Aug, 2025 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7193819","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2025-07-25 05:00:46","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"
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array.\u003cstrong\u003e b,\u003c/strong\u003e Schematic of a unit sensor and its diaphragm structure. \u003cem\u003eC\u003c/em\u003e\u003csub\u003egap\u003c/sub\u003e represents the variable capacitance formed between the diaphragm and the bottom electrode. \u003cstrong\u003ec,\u003c/strong\u003e Optical microscope (OM) and scanning electron microscope (SEM) images of the star-shaped support architecture, which enables the hyperpacked sensor configuration (OM scale bar: 2 mm; SEM scale bar: 1 mm). \u003cstrong\u003e\u0026nbsp;d,\u003c/strong\u003e Schematic highlighting the key differences between a conventional capacitive sensor (left) and the proposed hyperpacked sensor array (right).\u003cstrong\u003e e,\u003c/strong\u003e Schematic illustration of the operating mechanism in the proposed sensor (left), and simulated piezopotential distribution during diaphragm oscillation (right).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/8a0fa818728f417d61596536.png"},{"id":90154105,"identity":"cdd7f0c3-0f11-4a7a-a163-8e733085eec4","added_by":"auto","created_at":"2025-08-29 07:50:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of the piezoelectric diaphragm.\u003c/strong\u003e \u003cstrong\u003ea,\u003c/strong\u003eFT-IR spectra of PVDF-TrFE films fabricated using DMF and PA solvents. \u003cstrong\u003eb,\u003c/strong\u003e TrWAXS spectra of unpoled and poled PVDF-TrFE films. \u003cstrong\u003ec,\u003c/strong\u003e Measured \u003cem\u003ed\u003c/em\u003e₃₁ of unpoled and poled PVDF-TrFE films. \u003cstrong\u003ed,\u003c/strong\u003e Comparison of contact stability between the electret film (CYTOP, left) and the piezoelectric film (PVDF-TrFE, right). Stability was evaluated following direct contact with water, artificial sweat, and human skin, with measurements taken two hours after contact removal. The piezoelectric output voltage was measured at 300 Hz under the same vibrational amplitude.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/081dbc0bbe4521b3eed17385.png"},{"id":90153845,"identity":"5a3c2fcf-5c50-4bc3-9153-63e8cb2877f5","added_by":"auto","created_at":"2025-08-29 07:42:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":162428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensing performance of the hyperpacked vibration sensor array.\u003c/strong\u003e \u003cstrong\u003ea,\u003c/strong\u003e Schematic of the hyperpacked vibration sensor, highlighting key parameters optimized in this study. \u003cstrong\u003eb,\u003c/strong\u003e Effects of the number of diaphragms, support thickness, and diaphragm thickness on vibration sensitivity. \u003cstrong\u003ec,\u003c/strong\u003e Vibration sensitivity measured at 500, 1,000, 1,500, and 2,000 Hz under input amplitudes ranging from 0.1 to 2.5 \u003cem\u003eg\u003c/em\u003e. \u003cstrong\u003ed,\u003c/strong\u003e Frequency response showing the effect of piezoelectric-induced charges on sensor performance, comparing diaphragms made of poled and unpoled PVDF-TrFE and SU-8. \u003cstrong\u003ee,\u003c/strong\u003e LOD of the hyperpacked vibration sensor under a 500 Hz input. \u003cstrong\u003ef,\u003c/strong\u003e SNR of the hyperpacked vibration sensor under a 1 \u003cem\u003eg\u003c/em\u003e input vibration at 500, 1,000, 1,500, 2,000, and 2,500 Hz.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/566026b26641590917159dc5.png"},{"id":90153843,"identity":"7f64736b-3da1-4079-8e06-11909ac314f7","added_by":"auto","created_at":"2025-08-29 07:42:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":469796,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVibration-based audio recording using the hyperpacked vibration sensor array. a, \u003c/strong\u003eOutput waveforms and STFT spectrograms of \u003cstrong\u003ea, \u003c/strong\u003ethe original music, \u003cstrong\u003eb,\u003c/strong\u003ethe recording captured by the hyperpacked sensor array, and \u003cstrong\u003ec,\u003c/strong\u003e the recording captured by a commercial high-resolution accelerometer. All recordings were obtained by attaching the sensor and accelerometer to a vibration speaker at the same sound pressure level, with no post-processing applied.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/d513f2b524d50c890c06b3d6.png"},{"id":90154106,"identity":"1770e405-28c3-4be7-a849-ea838e73e0aa","added_by":"auto","created_at":"2025-08-29 07:50:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":225713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAcoustic sensing performance of the hyperpacked vibration sensor array. a,\u003c/strong\u003e Inferred acoustic sensitivity of the hyperpacked vibration sensor array. \u003cem\u003eS\u003c/em\u003e\u003csub\u003eac\u003c/sub\u003e represents the sensitivity calculated from output voltage and voice pressure at fundamental human vocalization frequencies of 200, 300, and 400 Hz. \u003cstrong\u003eb, \u003c/strong\u003eComparison of voice detection capabilities between a commercial microphone and the hyperpacked vibration sensor array attached to the neck. \u003cstrong\u003ec, \u003c/strong\u003eComparison of dynamic range and sensitivity with previously reported sensors. The dynamic range of our sensor reflects the measurements from Supplementary Fig. 30. Human vocalizations, including whispers, typically range from 25 to 60 dB\u003csub\u003eSPL\u003c/sub\u003e, while sound levels above 85 dB\u003csub\u003eSPL\u003c/sub\u003e may negatively affect hearing. \u003cstrong\u003ed,\u003c/strong\u003e Comparison of frequency range and SNR with existing sensors.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/232a4832bb6f50fd5e52a6cc.png"},{"id":90153858,"identity":"ffa01c5f-d64b-461d-802d-6dc75ddcebc9","added_by":"auto","created_at":"2025-08-29 07:42:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":226708,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRespiratory disease diagnosis using the hyperpacked vibration sensor array. a,\u003c/strong\u003e Schematic of the vibration sensor attached to the neck for monitoring voice and respiration signals. \u003cstrong\u003eb,\u003c/strong\u003e STFT spectrogram of recorded voice and respiration signals. \u003cstrong\u003ec,\u003c/strong\u003e Output waveform of normal breathing, with inhale and exhale phases labeled. \u003cstrong\u003ed,\u003c/strong\u003eSTFT spectrogram and PSD of normal respiration during a single inhalation–exhalation cycle. \u003cstrong\u003ee,\u003c/strong\u003e Output waveform of abnormal breathing. \u003cstrong\u003ef,\u003c/strong\u003eSTFT spectrogram and PSD of stridor—a pathological breathing sound—during a single inhalation–exhalation cycle.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/2d844fbcaa06de57726dcd9d.png"},{"id":100398695,"identity":"11011ca0-95cd-44d3-8607-874fc61dfb3e","added_by":"auto","created_at":"2026-01-16 11:54:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2596249,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/c82dccd3-ffbb-4920-81eb-6005a51ca213.pdf"},{"id":90153849,"identity":"a60ec86c-e2db-4516-9116-39fdebeabff6","added_by":"auto","created_at":"2025-08-29 07:42:23","extension":"mp4","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17829067,"visible":true,"origin":"","legend":"Air flow simulation during diaphragm fluctuation","description":"","filename":"RevisedSupplementaryVideo1NATSENSORS25050262KCho.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/798f6c8def30710d330cdffd.mp4"},{"id":90153846,"identity":"aa02c3f4-58d5-47f8-9fa8-5e9fe60901e6","added_by":"auto","created_at":"2025-08-29 07:42:23","extension":"mp4","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3797585,"visible":true,"origin":"","legend":"Demonstration of high-fidelity audio recording via vibration sensing","description":"","filename":"RevisedSupplementaryVideo2NATSENSORS25050262KCho.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/8dab978a049bf41e4449090d.mp4"},{"id":90153867,"identity":"89689032-e4b5-497f-ac36-952319f96c07","added_by":"auto","created_at":"2025-08-29 07:42:24","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":57118567,"visible":true,"origin":"","legend":"Supplementary information for hyperpacked piezoelectric-powered capacitive sensor array for high-fidelity vibration detection","description":"","filename":"RevisedSupplementaryInformationNATSENSORS25050262KCho.docx","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/1121b235351f963adeb60cbf.docx"},{"id":90153851,"identity":"48554914-107c-4c90-96ae-2714a548152c","added_by":"auto","created_at":"2025-08-29 07:42:23","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1665565,"visible":true,"origin":"","legend":"\u003cp\u003eReporting Summary\u003c/p\u003e","description":"","filename":"ReportingSummaryNATSENSORS25050262KCho.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7193819/v2/de76fdbd5b85e90777812775.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nK.H.C., J.-H. L., and K.C. are inventors of a KR patent related to this work. All other authors declare no completing interests.","formattedTitle":"Hyperpacked piezoelectric-powered capacitive sensor array for high-fidelity vibration detection","fulltext":[{"header":"Main","content":"\u003cp\u003eVibration, a fundamental mechanical stimulus, transmits dynamic signals that extend beyond quasi-static responses such as strain\u003csup\u003e1–3\u003c/sup\u003e, shear\u003csup\u003e4,5\u003c/sup\u003e, and pressure\u003csup\u003e6–8\u003c/sup\u003e. Unlike these, vibration conveys unique information due to its (1) ultralow-intensity signals and (2) broad frequency range. These characteristics make it indispensable for applications including structural health monitoring, industrial maintenance, and human physiological sensing\u003csup\u003e9–11\u003c/sup\u003e. Notably, many vital physiological signals manifest as minute vibrations spanning a wide frequency spectrum—cardiac activity (10–250 Hz)\u003csup\u003e12\u003c/sup\u003e, human voice (150–450 Hz)\u003csup\u003e13\u003c/sup\u003e, and respiration (100–2000 Hz)\u003csup\u003e14\u003c/sup\u003e.\u0026nbsp;Capturing these signals with high fidelity is crucial for next-generation wearable health monitoring\u003csup\u003e15,16\u003c/sup\u003e and human-machine interfaces\u003csup\u003e17–19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo address this need, flexible vibration sensors capable of conforming to curved and soft surfaces, such as human skin, have garnered significant attention\u003csup\u003e20–27\u003c/sup\u003e. Among these, capacitive sensors stand out for their inherently flat frequency response and linear sensitivity\u003csup\u003e9,10\u003c/sup\u003e. Designs incorporating microstructured hydrogels\u003csup\u003e28–30\u003c/sup\u003e or through-hole patterned diaphragms\u003csup\u003e31–35\u003c/sup\u003e, for example, enable stable frequency characteristics, facilitating precise broadband physiological signal detection. However, existing designs face critical limitations: they often rely on bulky external power sources\u003csup\u003e29,31,33\u003c/sup\u003e or environmentally unstable electret materials\u003csup\u003e32,36,37\u003c/sup\u003e to provide bias voltage, which limits their practicality in wearable applications. Fabrication is also complex, often involving intricate features such as air vents, array configurations, or microscale patterning\u003csup\u003e28,29,32\u003c/sup\u003e. Moreover, their performance—and broader applicability—is constrained by a narrow flat frequency range, limited sensitivity, and poor signal-to-noise ratio (SNR)\u003csup\u003e29,31,32,38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this Article, we report a hyperpacked capacitive vibration sensor array that achieves high-fidelity broadband detection through a non-contact piezoelectric effect. Unlike conventional capacitive sensors that require external power sources or electret materials to generate a bias voltage, our design employs a piezoelectric poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) thin film as the diaphragm. Piezoelectric charges from the diaphragm sustain a built‑in bias field and drive the signal response, enabling stable and efficient self‑powered operation. To ensure scalable fabrication, we introduce a one-step photolithography process in which star-shaped supports promote in-plane air ventilation through side vent holes—eliminating the need for intricate structures and allowing an ultra-compact array configuration. As a result, the sensor exhibits a linear sensitivity of 626 mV \u003cem\u003eg\u003c/em\u003e⁻¹, a flat frequency response spanning 80–5,000 Hz, and an SNR of 80 dB, substantially outperforming existing vibration/acoustic sensors in key performance metrics. Demonstrated as a soft contact microphone, a laryngophone, and a wearable healthcare device, it enables high-fidelity detection of music, voice, and respiratory signals, offering new possibilities for next-generation human–machine interfaces and health monitoring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHyperpacked vibration sensor array\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hyperpacked capacitive vibration sensor array consists of a 15 μm-thick PVDF-TrFE film attached to a 40 μm-thick star-shaped patterned support layer, which is laminated onto a parylene substrate (Fig. 1a). Each unit sensor is supported by four star-shaped structures and features a central 1600 μm-diameter circular region, where the suspended piezoelectric film acts as a diaphragm to detect vibrations (Fig. 1b,c and Supplementary Fig. 1). Gold electrodes, positioned on the upper surfaces of both the piezoelectric film and the parylene substrate, detect capacitance variations induced by diaphragm motion (Fig. 1b). The star-shaped support structures are arranged in a regular array, enabling the formation of multiple unit vibration sensors densely connected in parallel (Fig. 1c and Supplementary Fig. 2). Additionally, an intermediate Au layer between the support and diaphragm layers enhances adhesion and suppresses noise from non-diaphragm regions.\u003c/p\u003e\n\u003cp\u003eOur hyperpacked vibration sensor array represents a significant advancement over conventional capacitive vibration sensors, both in device architecture and material integration. From a design perspective, the star-shaped support structure plays a crucial role in enabling high-fidelity sensing, hyperpacked sensor configuration, and enhanced processability. In previous studies, air vent holes were introduced in the diaphragm or backplate to mitigate air-squeezed damping (Fig. 1d, left)\u003csup\u003e31–33,35,39,40\u003c/sup\u003e. In contrast, our approach eliminates air-squeezed damping by facilitating horizontal air circulation through individualized star-shaped supports (Fig. 1d, right). Numerical airflow simulations verify that this architecture provides effective, well-distributed ventilation across all unit cells (Supplementary Fig. 3 and Supplementary Video 1). This in-plane air ventilation minimizes thermal noise and improves sensitivity, as aligned airflow and diaphragm deflection can cause energy loss.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the circular regions enclosed by the star-shaped supports function as diaphragms, allowing hyperpacked sensor configuration with zero wasted area (Fig. 1d, right).\u0026nbsp;Eliminating air holes in the diaphragm or backplate not only simplifies fabrication \u003cem\u003evia\u003c/em\u003e one‑step photolithography (Supplementary Figs. 4 and 5) but also preserves a larger proportion of the diaphragm as the active sensing area compared with conventional designs (Supplementary Fig. 6). Although removing through‑holes increases the effective stiffness of the circular diaphragm region, the star‑shaped support configuration enables greater overall displacement of the diaphragm layer within the unit cell (Supplementary Fig. 7). This architecture also yields a substantially lower initial capacitance than conventional designs. Together, these features enhance the relative capacitance change and thereby improve sensitivity (Supplementary Fig. 8). Furthermore, supporting each diaphragm with four discrete anchoring structures, rather than a single bulk frame, markedly improves flexibility under bending (Supplementary Fig. 9).\u003c/p\u003e\n\u003cp\u003eFrom a materials standpoint, the device further distinguishes itself by incorporating a non-contact piezoelectric diaphragm—a simple yet highly effective strategy for more efficient, stable, and sensitive operation. This approach removes the need for bulky external power sources\u003csup\u003e31,33\u003c/sup\u003e or unstable electret layers\u003csup\u003e32,41,42\u003c/sup\u003e typically required for biasing in conventional capacitive sensors (Fig. 1d). Instead, remnant polarization within the piezoelectric material provides intrinsic charges that establish a stable initial bias field across the sensor. Mechanical vibrations drive diaphragm oscillations, inducing stress-dependent polarization and generating additional charges that directly contribute to the electrical output (Fig. 1e). Although the diaphragm does not physically contact the bottom electrode, the top and bottom electrodes are electrically connected through the interface circuitry (CKT), enabling charge redistribution (Δ\u003cem\u003eQ\u003c/em\u003e). At the same time, diaphragm motion changes the electrode spacing, producing a capacitance variation (Δ\u003cem\u003eC\u003c/em\u003e). The sensor’s electrical output is therefore governed by the combined effects of Δ\u003cem\u003eQ\u003c/em\u003e and Δ\u003cem\u003eC\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn capacitive sensors, accumulated charge is typically required to convert small capacitance variations into measurable voltage signals—a process facilitated by an interface circuit with an amplifier, as described by \u003cem\u003eV\u003c/em\u003e=\u003cem\u003eQ\u003c/em\u003e/\u003cem\u003eC\u003c/em\u003e (Supplementary Fig. 10)\u003csup\u003e29,31,32\u003c/sup\u003e. In this context, the sensing mechanism of our device can be described by a governing relation that incorporates simultaneous variations in both \u003cem\u003eQ\u003c/em\u003e and \u003cem\u003eC\u003c/em\u003e (Supplementary Note 1). In our device, piezoelectric charges from remnant polarization provide a stable bias for capacitive sensing, while motion-induced piezoelectric charges contribute as a smaller secondary term. The capacitive variation is expected to dominate the signal, with sensitivity maximized when the phases of Δ\u003cem\u003eQ\u003c/em\u003e and Δ\u003cem\u003eC\u003c/em\u003e are optimally aligned (Supplementary Note 1 and Supplementary Fig. 11).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of the piezoelectric diaphragm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe thin piezoelectric diaphragm was fabricated \u003cem\u003evia\u003c/em\u003e spin-coating PVDF-TrFE dissolved in an organic solvent. Traditionally, highly polar solvents such as dimethylformamide (DMF)\u003csup\u003e43,44\u003c/sup\u003e, dimethyl sulfoxide (DMSO)\u003csup\u003e45,46\u003c/sup\u003e, and N-methyl-2-pyrrolidone (NMP)\u003csup\u003e47\u003c/sup\u003e have been predominantly used for PVDF-TrFE processing. However, these solvents pose significant health risks, including liver damage and reproductive toxicity\u003csup\u003e48\u003c/sup\u003e. To mitigate these concerns, we employed propyl acetate (PA), a less hazardous alternative, which fully dissolved PVDF-TrFE (Supplementary Fig. 12) and yielded a film with a high β-phase content. Fourier transform infrared (FT-IR) confirmed characteristic β-phase peaks at 1400 cm⁻¹, 1288 cm⁻¹, and 850 cm⁻¹, comparable to those observed with DMF\u003csup\u003e49–51\u003c/sup\u003e, demonstrating PA’s suitability as a safer solvent (Fig. 2a).\u003c/p\u003e\n\u003cp\u003eConventionally, the degree of poling in PVDF-TrFE has been assessed using one-dimensional (1D) X-ray diffraction (XRD), focusing on the peak intensity changes of the (110)/(200) β-phase diffraction peak near 20° and the (021) diffraction peak around 40.8°, which can emerge after poling\u003csup\u003e52,53\u003c/sup\u003e. However, this method has limited precision in evaluating the α-to-β phase transition and the alignment of β-phase crystals, as 1D XRD integrates diffraction data from all orientations into a single spectrum (Supplementary Fig. 13).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo overcome the limitations of 1D XRD in assessing the α-to-β phase transition and crystallite alignment, we employed two-dimensional (2D) transmittance wide-angle X-ray scattering (TrWAXS) in both vertical and horizontal X-ray exposure directions (Supplementary Fig. 14). Vertical TrWAXS analysis revealed that, prior to poling, both α- and β-phases were present, whereas after poling, a clear α-to-β phase transition was observed (Fig. 2b, top). This transition was further corroborated by out-of-plane \u003cem\u003ed\u003c/em\u003e-spacing measurements, which revealed that the polymer chains in the α-phase initially exhibited a \u003cem\u003ed\u003c/em\u003e-spacing of 4.92 Å, while those in the β-phase exhibited 4.59 Å\u003csup\u003e54,55\u003c/sup\u003e. After poling, the α-phase disappeared, and the \u003cem\u003ed\u003c/em\u003e-spacing of the β-phase decreased a little bit to 4.57 Å, indicating a slightly denser molecular arrangement characteristic of the (110)/(200) β-phase diffraction (Supplementary Fig. 15 and Supplementary Table 1)\u003csup\u003e56,57\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMeanwhile, horizontal TrWAXS analysis confirmed the well-aligned β-phase crystallites, demonstrating enhanced molecular ordering along the poling direction (Fig. 2b, bottom). Additionally, azimuthal angle-dependent intensity analysis revealed a pronounced increase in out-of-plane orientation, indicating preferential β-phase domain alignment (Supplementary Fig. 16). These results confirm that the poling process not only facilitated the α-to-β phase transition but also promoted β-phase crystallite alignment along the external electric field, which is perpendicular to the film thickness.To further validate this structural transition, we measured the piezoelectric coefficient (\u003cem\u003ed\u003c/em\u003e₃₁) of the poled PVDF-TrFE film, which exhibited a significantly higher \u003cem\u003ed\u003c/em\u003e₃₁ value of approximately 15 pC N⁻¹ compared to the unpoled sample (Fig. 2c and Supplementary Fig. 17). This value falls within the range of previously reported \u003cem\u003ed\u003c/em\u003e₃₁ values for poled PVDF-TrFE (∼10–20 pC N⁻¹)\u003csup\u003e58–61\u003c/sup\u003e, reinforcing the reliability of our results (Supplementary Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough some β-phase was present in the unpoled PVDF-TrFE film, and thermal annealing may have induced partial dipole alignment, the dipoles within the β-phase domains largely remained randomly oriented (Fig. 2b, bottom). As a result, their contributions tended to cancel out, leading to the observed low \u003cem\u003ed\u003c/em\u003e\u003csub\u003e31\u003c/sub\u003e value. Similar contrasts in piezoelectric performance between poled and unpoled (thermally annealed) PVDF-TrFE have been reported in previous studies\u003csup\u003e58,62,63\u003c/sup\u003e, further supporting the validity of our results.\u0026nbsp;This enhancement in \u003cem\u003ed\u003c/em\u003e₃₁ strongly correlates with the observed structural transition, confirming the efficacy of the corona poling process.\u003c/p\u003e\n\u003cp\u003eTo evaluate the environmental stability of the piezoelectric diaphragm in comparison with a conventional electret diaphragm, we measured the relative output voltage of the PVDF-TrFE film and the relative surface potential of the CYTOP film before and after exposure to deionized water, artificial sweat, and direct skin contact (Fig. 2d). The PVDF-TrFE film retained most of its initial output voltage under all conditions, whereas the electret CYTOP film lost over 90% of its surface potential. The PVDF-TrFE film also maintained a stable \u003cem\u003ed\u003c/em\u003e\u003csub\u003e31\u003c/sub\u003e across all conditions (Supplementary Fig. 18).\u003c/p\u003e\n\u003cp\u003eThis stark contrast arises from their fundamentally different charge mechanisms: piezoelectric materials retain stable remnant polarization and generate dynamic charges through lattice distortion, both inherently resistant to moisture and skin contact, whereas electret materials rely primarily on surface-trapped charges that readily degrade under environmental exposure. As a result, PVDF-TrFE maintains long-term performance without requiring additional protective measures, while electrets like CYTOP necessitate careful handling to prevent charge loss. Its exceptional moisture and skin-contact stability make PVDF-TrFE particularly well-suited for skin-attachable electronics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVibration sensing performance of the hyperpacked sensor array\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hyperpacked vibration sensor array was optimized by carefully tuning key structural parameters: the number of diaphragms and the thicknesses of both the support layer and the diaphragm (Fig. 3a,b). Increasing the diaphragm count and reducing the support layer thickness improved sensitivity (Fig. 3b, left and middle), with a maximum of 64 diaphragms accommodated within the 16 × 18 mm sensor area. Because the diaphragms are connected in parallel, their capacitance variations add linearly, producing a proportional increase in signal (Supplementary Fig. 2)\u003csup\u003e31,64,65\u003c/sup\u003e. Furthermore, since the first‑mode resonant frequency lies well above the sensor’s operating bandwidth, all diaphragms oscillate in the first vibration mode with in‑phase motion (Supplementary Fig. 19), ensuring that this proportional relationship between diaphragm count and sensitivity is fully realized. However, thinning the support layer below 40 µm proved impractical, as it caused the diaphragm to adhere to the bottom electrode during fabrication and drastically reduced production yield (Supplementary Fig. 20). Interestingly, the optimal diaphragm thickness for maximum sensitivity was found to be ~15 μm (Fig. 3b, right), contradicting previous reports that associate thinner diaphragms with higher sensitivity due to reduced stiffness and increased displacement\u003csup\u003e31,66\u003c/sup\u003e. This discrepancy arises because the piezoelectric diaphragm must retain sufficient thickness to provide enough piezoelectric charge; beyond 15 μm, excessive stiffness counteracts this effect, reducing sensitivity.\u003c/p\u003e\n\u003cp\u003eThe optimized device exhibited a high sensitivity of 626 mV \u003cem\u003eg\u003c/em\u003e⁻¹ while maintaining excellent linearity over a broad vibration acceleration range of 0.1–2.5 \u003cem\u003eg\u003c/em\u003e (Fig. 3c). This superior linearity was attributed to the device structure, which features thin diaphragms; each diaphragm, with fixed boundaries set by four star-shaped supports, undergoes vertical displacement linearly proportional to the vibration amplitude (Supplementary Fig. 21)\u003csup\u003e35,67,68\u003c/sup\u003e. Furthermore, the sensor demonstrated outstanding long-term stability and durability, retaining both sensitivity and linearity even after 210 days under identical vibration conditions with 1,000 cycles (Supplementary Fig. 22). The sensor also showed remarkable environmental stability, preserving its sensitivity even after immersion in water (Supplementary Fig. 23).\u003c/p\u003e\n\u003cp\u003eBeyond its high sensitivity and stability, the sensor featured an exceptionally flat frequency response within ±3 dB over a broad frequency range of 80–5,000 Hz (Fig. 3d). Although this flat response was not maintained below 50 Hz, the sensor still reliably detected static and low-frequency vibrations (Supplementary Fig. 24). In contrast, sensors fabricated using unpoled PVDF-TrFE and epoxy-based SU-8 diaphragms produced unstable signals, with outputs nearly indistinguishable from noise. Similarly, a control sensor with a PTFE diaphragm (a highly tribonegative material) generated only unstable, noise-like signals, ruling out triboelectric effects as the primary sensing mechanism (Supplementary Fig. 25). When the PVDF-TrFE diaphragm was slightly poled (\u003cem\u003ed\u003c/em\u003e₃₁ = 4 pC N\u003csup\u003e-1\u003c/sup\u003e), the sensor immediately produced a stable and clearly readable signal, further confirming the critical role of piezoelectric charge in its operation (Supplementary Fig. 26). Additionally, air ventilation between the star-shaped supports effectively suppressed air-squeezed damping, as evidenced by the absence of the high-frequency roll-off characteristic of such damping (Supplementary Fig. 27)\u003csup\u003e31,69,70\u003c/sup\u003e. This ensured both high sensitivity and a stable, flat frequency response across a wide range.\u003c/p\u003e\n\u003cp\u003eMoreover, the sensor exhibited an ultralow limit of detection (LOD), detecting minute vibration inputs as low as 0.01 \u003cem\u003eg\u003c/em\u003e—a level corresponding to skin vibrations produced by soft breathing (Fig. 3e)\u003csup\u003e71\u003c/sup\u003e. Furthermore, the sensor achieved an exceptionally high SNR of ~80 dB across broad frequencies (Fig. 3f). Even when the noise power spectral density within the auditory frequency range was considered, the sensor maintained a high SNR of ~43 dB(A) under A-weighted conditions (Supplementary Fig. 28). This remarkable performance was attributed to the inherently low mechanical and electrical noise of the capacitive device structure\u003csup\u003e72\u003c/sup\u003e, and the exceptional sensitivity of our piezoelectric-powered capacitive sensor. These results indicate that the sensor is tailored for high-fidelity vibration detection across a wide amplitude and frequency range, capable of precisely measuring signals from extremely small to large amplitudes, even beyond the telephonic range (~3,000 Hz). This breakthrough opens new possibilities for a diverse range of vibration-based applications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-fidelity audio recording \u003cem\u003evia\u003c/em\u003e vibration sensing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA sound-producing object generates subtle vibrations that propagate through solid media and can be captured for audio recording. Leveraging this principle, our sensor enables high-fidelity sound acquisition by precisely detecting mechanical vibrations. We validated its performance\u0026nbsp;by benchmarking it against a commercial high-resolution accelerometer (352C33, PCB Piezotronics).Both devices were placed on a vibration speaker during recording (Supplementary Fig. 29), and the volume of the played music was kept constant to ensure a fair comparison.\u0026nbsp;For the recordings, we used \u003cem\u003eSpring\u003c/em\u003e (Movement 1) from \u003cem\u003eThe Four Seasons\u003c/em\u003e by Vivaldi, which features distinct cello and violin solo passages. The short-time Fourier transform (STFT) spectrogram of the original music revealed characteristic differences between the two instruments: the cello exhibited strong energy concentration in the lower frequency range with clear harmonic overtones, while the violin showed energy concentrated in the higher frequency range with finer, more detailed spectral features (Fig. 4a)\u003csup\u003e73\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe audio recording obtained with our sensor closely matched the original music, exhibiting an almost identical time-domain output waveform and STFT spectrogram while clearly capturing the distinct characteristics of both the cello and violin (Fig. 4b). More importantly, even when compared to the commercial accelerometer, our sensor demonstrated comparable performance, producing a similar output waveform and STFT spectrogram across a broad frequency range of up to 5,000 Hz (Fig. 4b,c). The recorded music exhibited minimal distortion and maintained high sound quality across the detection frequency range (Supplementary Video 2). While achieving similar audio recording performance to the commercial high-resolution accelerometer, our sensor offers distinct advantages: it is thinner, smaller, and, most importantly, highly flexible rather than rigid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcoustic sensing performance of hyperpacked vibration sensor array\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOwing to its exceptional flexibility (Supplementary Fig. 9), our sensor conforms seamlessly to the neck, ensuring excellent skin contact and efficient vibration transfer. The amplitude of neck skin vibrations induced by vocalization has been reported to exhibit a linear correlation with vocal sound pressure\u003csup\u003e31,74\u003c/sup\u003e. Using this relationship, the sensor’s vibration sensitivity was mapped to an inferred acoustic sensitivity (Fig. 5a). The output voltage was recorded for input vibrations up to 2.5 \u003cem\u003eg\u003c/em\u003e at 200, 300, and 400 Hz—within the human fundamental vocal frequency range (150–450 Hz)\u003csup\u003e13\u003c/sup\u003e. To account for inter-individual variability, we used the lower bound of the measured correlation between skin acceleration and vocal sound pressure (Supplementary Fig. 30), yielding an inferred acoustic sensitivity of 6.07 V Pa⁻¹ with a linear response. We emphasize that this inferred value is not an intrinsic sensor property and will vary between users.\u003c/p\u003e\n\u003cp\u003eWhen attached to the neck, the sensor captured high-fidelity voice signals with clear waveform and spectral features (Fig. 5b and Supplementary Fig. 31). Since capacitive sensors are inherently susceptible to electromagnetic interference (EMI)\u003csup\u003e75\u003c/sup\u003e, we encapsulated the sensor within a compact, flexible shielding case (Supplementary Fig. 32) before attachment to minimize EMI effects. To evaluate its performance, we recorded a complex sentence—\u003cem\u003e\"Hi, my name is Kang Hyuk Cho. I am researching vibration sensors at POSTECH.\"\u003c/em\u003e—and analyzed the resulting waveforms and harmonic structures using STFT\u003csup\u003e76,77\u003c/sup\u003e. The spectral characteristics closely matched those captured by a commercial microphone (Fig. 5b, top: our sensor; bottom: commercial microphone). Moreover, even in acoustically challenging environments, the sensor effectively captured voice signals with minimal interference from ambient noise (Supplementary Fig. 33). This was achieved by detecting voice through neck-skin vibrations rather than airborne sound waves, while the flexible EMI shielding case further blocked external acoustic waves from reaching the diaphragm, ensuring that background noise had minimal impact.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImportantly, for effective use in speech communication, the sensor must maintain high sensitivity across the dynamic range corresponding to normal conversation levels (40–60 dB\u003csub\u003eSPL\u003c/sub\u003e). However, most state-of-the-art vibration and acoustic sensors exhibit low sensitivity in this range, or worse, their dynamic range does not fully cover it, instead focusing on levels above 60 dB\u003csub\u003eSPL\u003c/sub\u003e (Fig. 5c and Supplementary Table 3). Many of these sensors are characterized at sound levels exceeding 90 dB\u003csub\u003eSPL\u003c/sub\u003e, which are classified as harmful and can cause hearing damage with prolonged exposure. Measuring sensitivity at such high levels is impractical for everyday speech applications. In contrast, our sensor features a broad dynamic range, spanning from approximately 20 dB\u003csub\u003eSPL\u003c/sub\u003e (equivalent to whispering) to 90 dB\u003csub\u003eSPL\u003c/sub\u003e (comparable to the noise of a heavy truck), while maintaining consistently high linear sensitivity throughout (Fig. 5c).\u003c/p\u003e\n\u003cp\u003eIn addition to a broad dynamic range, maintaining a high and stable SNR across a wide frequency spectrum is essential for accurately capturing broadband physiological signals beyond voice. However, many existing sensors exhibit highly non-flat frequency responses, often reporting SNR only at their resonant frequency rather than across the full spectrum (Fig. 5d and Supplementary Table 3). In real-world applications, this results in significant signal distortion, requiring extensive post-processing for high-fidelity detection. While some recent designs incorporate multichannel resonant frequency arrays to address this limitation, they still experience a gradual SNR decline at higher frequencies, resembling damping effects. Capacitive sensors provide a more stable SNR, but their absolute values remain relatively low. In contrast, our sensor achieves an exceptionally high and consistent SNR across a broad frequency range (80–5,000 Hz), ensuring high-fidelity broadband signal acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRespiratory disease diagnosis \u003cem\u003evia\u003c/em\u003e vibration sensing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRespiratory signals provide critical diagnostic information, yet their detection is often hindered by ambient noise and the weak signal strength of breath sounds\u003csup\u003e78,79\u003c/sup\u003e. A vibration sensor placed on the skin near the vocal cords offers a direct and reliable approach for capturing these signals by detecting subtle laryngeal vibrations. As the vocal cords open during inhalation and partially close during exhalation, they generate airflow-induced vibrations that propagate through surrounding tissue\u003csup\u003e80\u003c/sup\u003e. Positioning the sensor between the vocal cords and ribcage facilitates the detection of respiratory sounds associated with disorders such as stridor and crackles. Recent studies have demonstrated the feasibility of this method, underscoring its potential for diagnosing respiratory disease\u003csup\u003e81,82\u003c/sup\u003e. Notably, our sensor’s high-fidelity vibration detection allows for precise differentiation of respiratory sounds, improving diagnostic accuracy.\u003c/p\u003e\n\u003cp\u003eTo evaluate this capability, the sensor was placed on the skin near the vocal cords (Fig. 6a), and natural conversation was recorded for 20 seconds. The recorded output signals were transformed into a spectrogram using STFT to visually distinguish speech from respiratory sounds (Fig. 6b). Speech signals exhibited multiple harmonics spanning a broad frequency range, originating from the fundamental frequency of vocal cord vibrations. In contrast, respiratory sounds appeared as short, low-amplitude harmonic signals concentrated below 1 kHz with minimal spectral complexity. The sensor could detect breathing sounds at different respiration rates (slow, normal, and fast), with clear inhale and exhale events recorded for all conditions (Supplementary Fig. 34). This contrast enabled a clear separation between speech and respiratory signals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn normal breathing, inhalation and exhalation waveforms followed a regular pattern (Fig. 6c), with power spectral density (PSD) analysis showing no significant differences in frequency distribution between the two phases (Fig. 6d). In contrast, breathing signals from patients with stridor exhibited distinct differences in pitch between inhalation and exhalation, with sharp waveform peaks observed during the inhalation phase (Fig. 6e)\u003csup\u003e83,84\u003c/sup\u003e. PSD analysis of stridor patients' signals revealed a distinct inhalation spectral peak at ~350 Hz—a hallmark of turbulent airflow resulting from airway narrowing (Fig. 6f)\u003csup\u003e85\u003c/sup\u003e. The vibration sensor also captured a distinctive spectral peak characteristic of coarse crackles, another class of abnormal respiratory sounds (Supplementary Fig. 35). These results demonstrate that our sensor can effectively detect pathological respiratory signals, with observations closely aligning with previously reported clinical findings. This consistency suggests that the sensor may serve as a non-invasive and reliable tool for respiratory disease diagnostics.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe have reported a hyperpacked, piezoelectric-powered capacitive sensor array for broadband mechanical stimuli detection. The device architecture is based on a star-shaped support structure fabricated \u003cem\u003evia\u003c/em\u003e a facile one-step photolithography process. This design facilitates air ventilation through side vent holes\u0026mdash;eliminating the need for intricate structures\u0026mdash;and enables an ultra-compact array configuration. Distinguishing itself from existing approaches, the sensor incorporates a non-contact piezoelectric diaphragm\u0026mdash;a simple yet highly effective strategy for boosting sensitivity, stability, and energy efficiency. As a result, the device demonstrates a significant leap in sensing performance compared to previously reported vibration/acoustic sensors, with a linear sensitivity of 626 mV \u003cem\u003eg\u003c/em\u003e⁻\u0026sup1;, a flat frequency response spanning 80\u0026ndash;5,000 Hz, an SNR of 80 dB, and excellent temporal and environmental stability\u0026mdash;all without requiring an external bias voltage. We demonstrated high-fidelity recording of music, voice, and respiratory signals by detecting subtle mechanical vibrations. This architecture- and materials-driven approach opens new possibilities for broadband mechanical sensing, offering progressive solutions for voice-enabled Internet of Things systems and personalized mobile healthcare technologies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eFabrication of hyperpacked vibration sensor array\u003c/h2\u003e\u003cp\u003eA PVDF-TrFE solution (18 wt%) was prepared by dissolving PVDF-TrFE powder (FC30, Piezotech\u0026reg;) in PA and stirring for 24 h. A glass wafer was sequentially coated with a Ti/Al (15/100 nm) sacrificial layer \u003cem\u003evia\u003c/em\u003e thermal evaporation and a 50 nm Au top electrode \u003cem\u003evia\u003c/em\u003e sputter deposition (E-1030 Ion Sputter, Hitachi). The PVDF-TrFE solution was then spin-coated to form a\u0026thinsp;~\u0026thinsp;15 \u0026micro;m-thick diaphragm. To induce β-phase crystallization, thermal annealing was performed on a hotplate at 60\u0026deg;C for 10 min, 80\u0026deg;C for 60 min, and 130\u0026deg;C for 180 min. A second Au layer was sputtered onto the diaphragm to enhance adhesion with the support structure and suppress noise from non-diaphragm regions. A negative photoresist (SU-8 3050, Micro Chem) was spin-coated and patterned to define a 40 \u0026micro;m-thick star-shaped support. The Au layer not covered by the SU-8 support, forming the diaphragm region, was then etched (Gold Etchant, Standard, Sigma Aldrich). The piezoelectric diaphragm was subsequently poled \u003cem\u003evia\u003c/em\u003e corona poling (see separate section for details). The Al sacrificial layer was removed using an Al etchant (aqueous solution: CuCl\u003csub\u003e2\u003c/sub\u003e, H₂O, HCl)\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e, completing the electrode/diaphragm/support stack. For the bottom electrode, a Ti/Au (5/50 nm) layer was thermally deposited onto a 20 \u0026micro;m-thick Parylene-C substrate, which had been pre-coated with Ti/Al sacrificial layers. A diluted SU-8 2000 solution (17.8 wt%) was used to form an ultrathin adhesive layer on the bottom electrode. The top stack was laminated onto the bottom electrode/substrate assembly on a hot plate at 60\u0026deg;C, while mechanical pressing was applied simultaneously to ensure strong bonding. Final etching of the Al sacrificial layer yielded the free-standing, hyperpacked vibration sensor array.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCorona poling of piezoelectric diaphragm\u003c/h3\u003e\n\u003cp\u003eThe diaphragm was poled using a corona discharge method. The experimental setup was constructed within a plastic chamber to ensure electrical isolation from the surrounding environment. Inside the chamber, a conductive needle array and a grounded metal plate were positioned 4 cm apart, facing each other. Poling was performed by placing the diaphragm directly beneath the needle array. The needle, fabricated from stainless steel, had a tip curvature radius of approximately 50 \u0026micro;m and a cone angle of ~\u0026thinsp;6\u0026deg;. A direct current (DC) voltage was supplied by a function generator (33510B, KEYSIGHT) and subsequently amplified using a high-voltage amplifier (20/20C-HS, Trek). The amplified voltage was applied to the conductive needle, while the metal plate was grounded. The poling process was carried out at room temperature by applying a DC voltage of 10 kV for 30 min.\u003c/p\u003e\n\u003ch3\u003eEvalutation of piezoelectric properties\u003c/h3\u003e\n\u003cp\u003eTo compare the degree of α- and β-phase formation in PVDF-TrFE diaphragms depending on solvent selection, free-standing PVDF-TrFE films prepared using DMF and PA as solvents were analyzed \u003cem\u003evia\u003c/em\u003e FT-IR (Vertex 70V, Bruker). The transition to the β-phase and the degree of molecular alignment following poling were further investigated using XRD (D/MAX-2500-PC, Rigaku) with CuKα radiation over a 2θ range of 5\u0026deg;\u0026ndash;50\u0026deg;, as well as TrWAXS. TrWAXS measurements were conducted at the 3C SAXS-I beamline of the Pohang Accelerator Laboratory (PLS-II, PAL), Korea, and the acquired images were processed using p-GIXS software\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. The piezoelectric coefficient was measured following a previously reported method\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, using a universal tensile machine (LS1, AMETEK) in conjunction with an electrometer (B2987A, Keysight).\u003c/p\u003e\n\u003ch3\u003eDevice characterization\u003c/h3\u003e\n\u003cp\u003eThe diaphragm thickness was measured using an optical profilometer (Alpha-step D-500, KLA). To ensure accurate thickness determination, measurements were taken at three different locations on more than three samples. OM and SEM images were acquired using an optical microscope (ZEISS) and an SEM (S-4800, HITACHI), respectively. Numerical simulations were performed using COMSOL Multiphysics 6.2, incorporating solid mechanics, electrostatics, pressure acoustics, and nonlinear physics modules. The output voltage and real-time fast Fourier transform (FFT) data were recorded using an oscilloscope (TDS 3054C, Tektronix) and a signal analyzer (SR785, Stanford Research Systems), respectively, while mechanical input vibrations were generated using a mini vibration exciter (Type 4810, Br\u0026uuml;el \u0026amp; Kj\u0026aelig;r) (Supplementary Fig.\u0026nbsp;36). Vibration sensitivity was defined as the output voltage of the device relative to that of a reference accelerometer (PCB Piezotronics, 352C33), which has a constant sensitivity of 100 mV \u003cem\u003eg\u003c/em\u003e⁻\u0026sup1; across 10 Hz to 10 kHz. To eliminate potential interference from electromagnetic waves emitted by nearby electronic devices, the sensor and circuit were enclosed in an aluminum shielding box. During the frequency sweep (80\u0026ndash;5000 Hz), over 800 measurement points were acquired, uniformly distributed on a logarithmic scale.\u003c/p\u003e\n\u003ch3\u003eVibration-based audio recording\u003c/h3\u003e\n\u003cp\u003eA vibration speaker (VBT-001, Newadin Technology) was used for music playback, with both a commercial accelerometer (352C33, PCB Piezotronics) and the fabricated sensor positioned on the vibrating disk to capture the music signal. To ensure a fair performance comparison, the playback volume was kept constant across all measurements. The fabricated sensor was covered with a Ni/Al (10/100 nm)-coated polyimide film (thickness: 150 \u0026micro;m) and electrically connected to ground to provide EMI shielding. For the recording demonstration, excerpts from orchestral music (\u003cem\u003eSpring\u003c/em\u003e, Movement 1, \u003cem\u003eThe Four Seasons\u003c/em\u003e, Vivaldi) were used. To comply with copyright regulations, the original audio was obtained from Musopen (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://musopen.org/music/\u003c/span\u003e\u003cspan address=\"https://musopen.org/music/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which distributes content under the Creative Commons Public Domain Dedication 1.0. The recorded signals were acquired using an external oscilloscope (TBS 2000 Series, Tektronix) and processed in MATLAB (R2023a, MathWorks) to generate STFT spectrograms and reconstruct the audio signal.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAttachment to skin for voice and respiratory signal detection\u003c/h2\u003e\u003cp\u003ePrior to application on the human neck, the sensor was encapsulated with the aforementioned flexible EMI shielding case. To ensure secure adhesion to the skin, a biocompatible adhesive (LP-001, ABLE C\u0026amp;C Co., Ltd.), commonly used in cosmetics and easily removable with a makeup remover, was applied to the bottom substrate. Additionally, an ultrathin, transparent medical film (Tegaderm Film 1622W, 3M) was placed over the sensor and electrical connections to enhance stability and minimize motion artifacts. For voice and respiratory signal acquisition, the sensor was positioned near the vocal cords (Supplementary Fig.\u0026nbsp;22). A commercial microphone (Mic-12, Actto) simultaneously recorded speech for comparative analysis. To evaluate sensor performance in a noisy environment, artificial background noise at 80 dB\u003csub\u003eSPL\u003c/sub\u003e was introduced during one of the recordings. For normal respiratory signal acquisition, data were directly obtained from the sensor attached near the vocal cords. For abnormal respiratory signals, open-access pathological breathing sounds were played through the vibration speaker. A rubber layer was added to the vibrating disk to mimic the mechanical properties of soft human tissue, such as the larynx. Pathological breathing sounds like stridor have been recorded in the laryngeal region\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. All signals were recorded using the oscilloscope and processed in MATLAB.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical approval\u003c/h2\u003e\u003cp\u003eThis study was approved by the Institutional Review Board of Pohang University of Science and Technology (PIRB-2025-052). All procedures were conducted in accordance with the approved guidelines and regulations. Informed consent was obtained from the participants prior to their involvement in the study. Additionally, all individuals depicted in the images provided written consent for publication.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAuthor information\u003c/h2\u003e\u003cp\u003eThese authors contributed equally: Kang Hyuk Cho, Dr. Jeng-Hun Lee\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eK.H.C., J.-H. L., and K.C. are inventors of a KR patent related to this work. All other authors declare no completing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eK.H.C. and J.-H.L. conceived the idea, performed the experiments, analyzed the data, created the figures, and wrote the manuscript. S.Y., S.L., and S.C. conducted material characterizations and numerical simulations. W.K. and W.M. designed the experimental setups. Y.K. and Y.C. developed the interface circuit for the sensor. K.C. supervised the entire research project.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Research Foundation of Korea (NRF) grants funded by the Ministry of Science and ICT (MSIT) (2021M3C1C3097512).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLee, J. \u003cem\u003eet al.\u003c/em\u003e Stretchable and suturable fibre sensors for wireless monitoring of connective tissue strain. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Electron\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 291\u0026ndash;301 (2021).\u003c/li\u003e\n\u003cli\u003eKim, K. K. \u003cem\u003eet al.\u003c/em\u003e A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Electron\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 64\u0026ndash;75 (2023).\u003c/li\u003e\n\u003cli\u003eZhou, Z. \u003cem\u003eet al.\u003c/em\u003e Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Electron\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 571\u0026ndash;578 (2020).\u003c/li\u003e\n\u003cli\u003eAksoy, B. \u003cem\u003eet al.\u003c/em\u003e Shielded soft force sensors. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Commun\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 4649 (2022).\u003c/li\u003e\n\u003cli\u003ePeng, Y., Peng, H., Chen, Z. \u0026amp; Zhang, J. Ultrasensitive Soft Sensor from Anisotropic Conductive Biphasic Liquid Metal-Polymer Gels. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 2305707 (2024).\u003c/li\u003e\n\u003cli\u003eKim, S. W. \u003cem\u003eet al.\u003c/em\u003e Mechanically Robust and Linearly Sensitive Soft Piezoresistive Pressure Sensor for a Wearable Human-Robot Interaction System. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 3151\u0026ndash;3160 (2024).\u003c/li\u003e\n\u003cli\u003eLee, J. H. \u003cem\u003eet al.\u003c/em\u003e Rational Design of All Resistive Multifunctional Sensors with Stimulus Discriminability. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Funct\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 2107570 (2022).\u003c/li\u003e\n\u003cli\u003eChun, S. \u003cem\u003eet al.\u003c/em\u003e An artificial neural tactile sensing system. \u003cem\u003eNat Electron\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 429\u0026ndash;438 (2021).\u003c/li\u003e\n\u003cli\u003eLee, J. H., Cho, K. H. \u0026amp; Cho, K. Emerging Trends in Soft Electronics: Integrating Machine Intelligence with Soft Acoustic/Vibration Sensors. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e. \u003cstrong\u003e35\u003c/strong\u003e, 2209673 (2023).\u003c/li\u003e\n\u003cli\u003eLin, Z. \u003cem\u003eet al.\u003c/em\u003e Insights into Materials, Physics, and Applications in Flexible and Wearable Acoustic Sensing Technology. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e. \u003cstrong\u003e36\u003c/strong\u003e, 2306880 (2024).\u003c/li\u003e\n\u003cli\u003eShen, R. \u003cem\u003eet al.\u003c/em\u003e High-sensitivity and high-resolution triboelectric acoustic sensor for mechanical equipment monitoring. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e133\u003c/strong\u003e, 110450 (2025).\u003c/li\u003e\n\u003cli\u003eNayeem, O. G. \u003cem\u003eet al.\u003c/em\u003e All-nanofiber-based, ultrasensitive, gas-permeable mechanoacoustic sensors for continuous long-term heart monitoring. \u003cem\u003eProc. Natl. Acad. Sci. U.S.A.\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 7063\u0026ndash;7070 (2020).\u003c/li\u003e\n\u003cli\u003eMelton, J., Bradford, Z. \u0026amp; Lee, J. Acoustic Characteristics of Vocal Sounds Used by Professional Actors Performing Classical Material Without Microphones in Outdoor Theatre. \u003cem\u003eJ. Voice\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e36\u003c/strong\u003e, 733.e23\u0026ndash;733.e29 (2022).\u003c/li\u003e\n\u003cli\u003eSabry, A. H., Dallal Bashi, O. I., Nik Ali, N. H. \u0026amp; Al Kubaisi, Y. M. Lung disease recognition methods using audio-based analysis with machine learning. \u003cem\u003eHeliyon\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e26218 (2024).\u003c/li\u003e\n\u003cli\u003eWang, R. \u003cem\u003eet al.\u003c/em\u003e Molecular Ferroelectrics for Highly Sensitive Detection Toward Low-Frequency Sound Recognition. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e. \u003c/em\u003e\u003cstrong\u003e37\u003c/strong\u003e, 2409251 (2025).\u003c/li\u003e\n\u003cli\u003eHui, X. \u003cem\u003eet al.\u003c/em\u003e Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 2401508 (2024).\u003c/li\u003e\n\u003cli\u003eQiao, W. \u003cem\u003eet al.\u003c/em\u003e A Highly-Sensitive Omnidirectional Acoustic Sensor for Enhanced Human\u0026ndash;Machine Interaction. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 2413086 (2024).\u003c/li\u003e\n\u003cli\u003eLan, B. \u003cem\u003eet al.\u003c/em\u003e Multichannel Gradient Piezoelectric Transducer Assisted with Deep Learning for Broadband Acoustic Sensing. \u003cem\u003eACS Appl\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e. Interfaces\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 12146\u0026ndash;12153 (2023).\u003c/li\u003e\n\u003cli\u003eXiang, Z. \u003cem\u003eet al.\u003c/em\u003e High-performance microcone-array flexible piezoelectric acoustic sensor based on multicomponent lead-free perovskite rods. \u003cem\u003eMatter\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 554\u0026ndash;569 (2023).\u003c/li\u003e\n\u003cli\u003eGao, S. \u003cem\u003eet al.\u003c/em\u003e High-bandwidth self-powered vibration sensors based on triboelectric particle-surface interactions. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e119\u003c/strong\u003e, 109060 (2024).\u003c/li\u003e\n\u003cli\u003eYu, Z. \u003cem\u003eet al.\u003c/em\u003e Integrated piezo-tribo hybrid acoustic-driven nanogenerator based on porous MWCNTs/PVDF-TrFE aerogel bulk with embedded PDMS tympanum structure for broadband sound energy harvesting. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e97\u003c/strong\u003e, 107205 (2022).\u003c/li\u003e\n\u003cli\u003eJin, B. \u003cem\u003eet al.\u003c/em\u003e Flexible Hair-Like Piezoelectric Acoustic Particle Velocity Sensor with Enhanced Sensitivity for Speaker Recognition. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Funct\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 2417164 (2024).\u003c/li\u003e\n\u003cli\u003eChe, Z. \u003cem\u003eet al.\u003c/em\u003e Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Commun\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1873 (2024).\u003c/li\u003e\n\u003cli\u003eShao, H. \u003cem\u003eet al.\u003c/em\u003e Efficient conversion of sound noise into electric energy using electrospun polyacrylonitrile membranes. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 104956 (2020).\u003c/li\u003e\n\u003cli\u003eZhao, X. \u003cem\u003eet al.\u003c/em\u003e A self-filtering liquid acoustic sensor for voice recognition. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Electron\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 924\u0026ndash;932 (2024).\u003c/li\u003e\n\u003cli\u003eGong, S. \u003cem\u003eet al.\u003c/em\u003e A Soft Resistive Acoustic Sensor Based on Suspended Standing Nanowire Membranes with Point Crack Design. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Funct\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003eWang, H. S.\u003cem\u003e \u003c/em\u003e\u003cem\u003eet al.\u003c/em\u003e\u003cem\u003e \u003c/em\u003eBiomimetic and Flexible Piezoelectric Mobile Acoustic Sensors with Multiresonant Ultrathin Structures for Machine Learning Biometrics. \u003cem\u003eSci. Adv\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, eabe5683 (2021).\u003c/li\u003e\n\u003cli\u003eZhao, J. \u003cem\u003eet al.\u003c/em\u003e Capacitive Low-Frequency Hydrophone Based on Micronanostructured Iontronic Hydrogel for Underwater Monitoring. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 22010\u0026ndash;22020 (2024).\u003c/li\u003e\n\u003cli\u003eGuo, H. \u003cem\u003eet al.\u003c/em\u003e Iontronic Dynamic Sensor with Broad Bandwidth and Flat Frequency Response Using Controlled Preloading Strategy. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 5599\u0026ndash;5608 (2024).\u003c/li\u003e\n\u003cli\u003eYang, R. \u003cem\u003eet al.\u003c/em\u003e Iontronic pressure sensor with high sensitivity over ultra-broad linear range enabled by laser-induced gradient micro-pyramids. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Commun\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 2907 (2023).\u003c/li\u003e\n\u003cli\u003eLee, S. \u003cem\u003eet al.\u003c/em\u003e An ultrathin conformable vibration-responsive electronic skin for quantitative vocal recognition. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Commun\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 2468 (2019).\u003c/li\u003e\n\u003cli\u003eLee, S. \u003cem\u003eet al.\u003c/em\u003e An Electret-Powered Skin-Attachable Auditory Sensor that Functions in Harsh Acoustic Environments. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2205537 (2022).\u003c/li\u003e\n\u003cli\u003eLee, S. \u003cem\u003eet al.\u003c/em\u003e A High-Fidelity Skin-Attachable Acoustic Sensor for Realizing Auditory Electronic Skin. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2109545 (2022).\u003c/li\u003e\n\u003cli\u003ePeng, T. H. \u0026amp; Huang, J. H. The Effect of Compliant Backplate on Capacitive MEMS Microphones. \u003cem\u003eIEEE Sens\u003c/em\u003e\u003cem\u003e. J\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 17803\u0026ndash;17811 (2024).\u003c/li\u003e\n\u003cli\u003eNaderyan, V., Raspet, R. \u0026amp; Hickey, C. Analytical, computational, and experimental study of thermoviscous acoustic damping in perforated micro-electro-mechanical systems with flexible diaphragm. \u003cem\u003eJ\u003c/em\u003e\u003cem\u003e. Acoust\u003c/em\u003e\u003cem\u003e. Soc\u003c/em\u003e\u003cem\u003e. Am\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e150\u003c/strong\u003e, 2749\u0026ndash;2756 (2021).\u003c/li\u003e\n\u003cli\u003eLin, S. \u003cem\u003eet al.\u003c/em\u003e Multiplying the Stable Electrostatic Field of Electret Based on the Heterocharge-Synergy and Superposition Effect. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Sci\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 2203150 (2022).\u003c/li\u003e\n\u003cli\u003eLi, H. \u003cem\u003eet al.\u003c/em\u003e Nanocomposite electret with surface potential self-recovery from water dipping for environmentally stable energy harvesting. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e64\u003c/strong\u003e, 103913 (2019).\u003c/li\u003e\n\u003cli\u003eWang, Y. \u003cem\u003eet al.\u003c/em\u003e Highly stable and ultra-fast vibration-responsive flexible iontronic sensors for accurate acoustic signal recognition. \u003cem\u003eNanoscale\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 22021\u0026ndash;22028 (2024).\u003c/li\u003e\n\u003cli\u003eLee, J. P. \u003cem\u003eet al.\u003c/em\u003e Encoding of multi-modal emotional information via personalized skin-integrated wireless facial interface. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Commun\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 530 (2024).\u003c/li\u003e\n\u003cli\u003eSun, H. \u003cem\u003eet al.\u003c/em\u003e Graphene-based dual-function acoustic transducers for machine learning-assisted human\u0026ndash;robot interfaces. \u003cem\u003eInfoMat\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, e12385 (2023).\u003c/li\u003e\n\u003cli\u003eRen, C. \u003cem\u003eet al.\u003c/em\u003e Electret mechano-sensor array integrated with tribopotential-modulated thin film transistors for precise spatiotemporal pressure perception. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e132\u003c/strong\u003e, 110351 (2024).\u003c/li\u003e\n\u003cli\u003eYasuda, T. \u003cem\u003eet al.\u003c/em\u003e Ultra-Rapidly Responsive Electret-Based Flexible Pressure Sensor via Functional Polymeric Nanoparticle Synthesis. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Funct\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2402064 (2024).\u003c/li\u003e\n\u003cli\u003eJiang, Y. \u003cem\u003eet al.\u003c/em\u003e Ultrathin Eardrum-Inspired Self-Powered Acoustic Sensor for Vocal Synchronization Recognition with the Assistance of Machine Learning. \u003cem\u003eSmall\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 2106960 (2022).\u003c/li\u003e\n\u003cli\u003eLee, S., Kim, W., Park, N. C. \u0026amp; Park, J. W. Frequency Selectivity via Inner Boundary Conditions for A Self-Powered Multiresonant Acoustic Sensing Array with Broad Bandwidth. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Funct\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 2306026 (2023).\u003c/li\u003e\n\u003cli\u003eLi, B. \u003cem\u003eet al.\u003c/em\u003e Ultrasensitive mechanical/thermal response of a P(VDF-TrFE) sensor with a tailored network interconnection interface. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Commun\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 4000 (2023).\u003c/li\u003e\n\u003cli\u003ePark, J. \u003cem\u003eet al.\u003c/em\u003e Frequency-Selective Acoustic and Haptic Smart Skin for Dual-Mode Dynamic/Static Human-Machine Interface. \u003cem\u003eSci. Adv\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, abj9220 (2022).\u003c/li\u003e\n\u003cli\u003eZhang, Z. \u003cem\u003eet al.\u003c/em\u003e Enhanced flexible piezoelectric sensor by the integration of P(VDF-TrFE)/AgNWs Film with a-IGZO TFT. \u003cem\u003eIEEE Electron Device Lett.\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cstrong\u003e40\u003c/strong\u003e, 111\u0026ndash;114 (2019).\u003c/li\u003e\n\u003cli\u003eSherwood, J., Albericio, F. \u0026amp; de la Torre, B. G. N,N-Dimethyl Formamide European Restriction Demands Solvent Substitution in Research and Development. \u003cem\u003eChemSusChem\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, e202301639 (2024).\u003c/li\u003e\n\u003cli\u003eResende, P. M., Isasa, J. D., Hadziioannou, G. \u0026amp; Fleury, G. Deciphering TrFE Fingerprints in P(VDF-TrFE) by Raman Spectroscopy: Defect Quantification and Morphotropic Phase Boundary. \u003cem\u003eMacromolecules\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 9673\u0026ndash;9684 (2023).\u003c/li\u003e\n\u003cli\u003eMao, D., Binh, E. \u0026amp; Madani, A. Ferroelectric Properties and Polarization Switching Kinetic of Poly (vinylidene fluoride-trifluoroethylene) Copolymer. in \u003cem\u003eFerroelectrics - Physical Effects\u003c/em\u003e (InTech, 2011).\u003c/li\u003e\n\u003cli\u003eApelt, S. \u003cem\u003eet al.\u003c/em\u003e Poly(vinylidene fluoride-co-trifluoroethylene) Thin Films after Dip- and Spin-Coating. \u003cem\u003eMacromol\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e. Eng\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e307\u003c/strong\u003e, 2200296 (2022).\u003c/li\u003e\n\u003cli\u003eZhang, N. \u003cem\u003eet al.\u003c/em\u003e Ultra-high electrostriction and ferroelectricity in poly (vinylidene fluoride) by \u0026lsquo;printing of charge\u0026rsquo; throughout the film. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Commun\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 744 (2025).\u003c/li\u003e\n\u003cli\u003eRyu, J., No, K., Kim, Y., Park, E. \u0026amp; Hong, S. Synthesis and Application of Ferroelectric Poly(Vinylidene Fluoride-co-Trifluoroethylene) Films using Electrophoretic Deposition. \u003cem\u003eSci\u003c/em\u003e\u003cem\u003e. Rep\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 36176 (2016).\u003c/li\u003e\n\u003cli\u003eStolichnov, I. \u003cem\u003eet al.\u003c/em\u003e Cold-field switching in PVDF-TrFE ferroelectric polymer nanomesas. \u003cem\u003ePhys\u003c/em\u003e\u003cem\u003e. Rev\u003c/em\u003e\u003cem\u003e. Lett\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 027603 (2012).\u003c/li\u003e\n\u003cli\u003eShehzad, M., Shehzad, M. \u0026amp; Wang, Y. Structural Tailing and Pyroelectric Energy Harvesting of P(VDF-TrFE) and P(VDF-TrFE-CTFE) Ferroelectric Polymer Blends. \u003cem\u003eACS Omega\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 13712\u0026ndash;13718 (2020).\u003c/li\u003e\n\u003cli\u003eLi, Y. \u003cem\u003eet al.\u003c/em\u003e Investigation on in-situ sprayed, annealed and corona poled PVDF-TrFE coatings for guided wave-based structural health monitoring: From crystallization to piezoelectricity. \u003cem\u003eMater. Des.\u003c/em\u003e \u003cstrong\u003e199\u003c/strong\u003e, (2021).\u003c/li\u003e\n\u003cli\u003eRoy, D., Chakraborty, M., Pattader, P. S. G., Islam, A. K. M. M. \u0026amp; Bandyopadhyay, D. Role of annealing with electric field toward improvement of ferroelectric and electroactive properties of PVDF copolymer and terpolymer thin films. \u003cem\u003eMacromol. Rapid Commun.\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 2400496 (2024).\u003c/li\u003e\n\u003cli\u003eYan, W. \u003cem\u003eet al.\u003c/em\u003e Single fibre enables acoustic fabrics via nanometre-scale vibrations. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e603\u003c/strong\u003e, 616\u0026ndash;623 (2022).\u003c/li\u003e\n\u003cli\u003eWang, K., Godfroid, T., Robert, D. \u0026amp; Preumont, A. Electrostrictive PVDF-TrFE thin film actuators for the control of adaptive thin shell reflectors. \u003cem\u003eActuators\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 53 (2020).\u003c/li\u003e\n\u003cli\u003eDucrot, P.-H., Dufour, I. \u0026amp; Ayela, C. Optimization of PVDF-TrFE processing conditions for the fabrication of organic MEMS resonators. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 19426 (2016).\u003c/li\u003e\n\u003cli\u003eWang, H., Zhang, Q. M., Cross, L. E. \u0026amp; Sykes, A. O. Piezoelectric, dielectric, and elastic properties of poly(vinylidene fluoride/trifluoroethylene). \u003cem\u003eJ. Appl. Phys.\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 3394\u0026ndash;3398 (1993).\u003c/li\u003e\n\u003cli\u003eHu, X., You, M., Yi, N., Zhang, X. \u0026amp; Xiang, Y. Enhanced piezoelectric coefficient of PVDF-TrFE films via in situ polarization. \u003cem\u003eFront. Energy Res.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 621540 (2021).\u003c/li\u003e\n\u003cli\u003eWen, D., Chen, X., Huang, F., Zhang, J., Yang, P., Li, R., Lu, Y. \u0026amp; Liu, Y. Piezoelectric and magnetoelectric effects of flexible magnetoelectric heterostructure PVDF-TrFE/FeCoSiB. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 15992 (2022).\u003c/li\u003e\n\u003cli\u003eKronast, W., Muller, B., Siedel, W. \u0026amp; Stoffel, A. Single-chip condenser microphone using porous silicon as sacrificial layer for the air gap. \u003cem\u003eProc. MEMS 98, Eleventh Annual Int. Workshop on Micro Electro Mechanical Systems\u003c/em\u003e (25\u0026ndash;29 January 1998), 123\u0026ndash;128 (1998).\u003c/li\u003e\n\u003cli\u003eWang, W., Yang, T., Chen, X. \u0026amp; Yao, X. Vibration energy harvesting using a piezoelectric circular diaphragm array. \u003cem\u003eIEEE Trans. Ultrason. Ferroelectr. Freq. Control\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 2022\u0026ndash;2026 (2012).\u003c/li\u003e\n\u003cli\u003eBeranek, L. L. \u0026amp; Mellow, T. J. Cellphone acoustics. in \u003cem\u003eAcoustics: Sound Fields and Transducers\u003c/em\u003e 391\u0026ndash;406 (Elsevier, 2012). doi:10.1016/b978-0-12-391421-7.00008-7.\u003c/li\u003e\n\u003cli\u003eKanekal, D. \u0026amp; Jindal, S. K. Prefabrication design, theoretical framework and simulation demonstration of a meander-shaped MEMS piezoresistive pressure sensor implanted on silicon substrate circular diaphragm for enhancement of key performance parameters utilized for low-pressure applications. \u003cem\u003eJ\u003c/em\u003e\u003cem\u003e. Comput\u003c/em\u003e\u003cem\u003e. Electron\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 433\u0026ndash;447 (2024).\u003c/li\u003e\n\u003cli\u003eKang, D. hee \u003cem\u003eet al.\u003c/em\u003e A Self-Powered, Highly Sensitive, and Frequency-Tunable Triboelectric Acoustic Sensor Inspired by the Human Cochlea. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Funct\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2408344 (2024).\u003c/li\u003e\n\u003cli\u003eYang, Y.-J. \u0026amp; Senturia, S. D. Numerical simulation of compressible squeezed-film damping. \u003cem\u003eProc. Solid-State Sensors, Actuators, and Microsystems Workshop\u003c/em\u003e (Hilton Head Island, South Carolina, 2\u0026ndash;6 June 1996), 76\u0026ndash;79 (1996).\u003c/li\u003e\n\u003cli\u003eRanjbar, A., Mehrabani, M. T. \u0026amp; Pary, F. T. A numerical study on the viscous damping effect for a condenser microphone. \u003cem\u003eIEEE Sens. J.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1307\u0026ndash;1316 (2011).\u003c/li\u003e\n\u003cli\u003eKano, S. \u0026amp; Mekaru, H. Preliminary comparison of respiratory signals using acceleration on neck and humidity in exhaled air. \u003cem\u003eMicrosyst. Technol.\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e27\u003c/strong\u003e, 1\u0026ndash;9 (2021).\u003c/li\u003e\n\u003cli\u003eMartin, D. T. \u003cem\u003eet al.\u003c/em\u003e A micromachined dual-backplate capacitive microphone for aeroacoustic measurements. \u003cem\u003eJ. Microelectromech. Syst. \u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 1289\u0026ndash;1302 (2007).\u003c/li\u003e\n\u003cli\u003eSlizovskaia, O., Haro, G. \u0026amp; G\u0026oacute;mez, E. Conditioned Source Separation for Musical Instrument Performances. \u003cem\u003eIEEE/ACM Trans. Audio Speech Lang. Process\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 2083\u0026ndash;2095 (2021).\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;vec, J. G., Titze, I. R. \u0026amp; Popolo, P. S. Estimation of sound pressure levels of voiced speech from skin vibration of the neck. \u003cem\u003eJ. Acoust. Soc. Am.\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 1386\u0026ndash;1394 (2005).\u003c/li\u003e\n\u003cli\u003eYoo, D., Won, D. J., Cho, W., Lim, J. \u0026amp; Kim, J. Double Side Electromagnetic Interference-Shielded Bending-Insensitive Capacitive-Type Flexible Touch Sensor with Linear Response over a Wide Detection Range. \u003cem\u003eAdv\u003c/em\u003e\u003cem\u003e. Mater\u003c/em\u003e\u003cem\u003e. Technol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 2100358 (2021).\u003c/li\u003e\n\u003cli\u003eHan, J. H. \u003cem\u003eet al.\u003c/em\u003e Machine learning-based self-powered acoustic sensor for speaker recognition. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 658\u0026ndash;665 (2018).\u003c/li\u003e\n\u003cli\u003eJung, Y. H. \u003cem\u003eet al.\u003c/em\u003e Deep learning-based noise robust flexible piezoelectric acoustic sensors for speech processing. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e101\u003c/strong\u003e, 107610 (2022).\u003c/li\u003e\n\u003cli\u003eMitsuke, T., Shimakawa, H. \u0026amp; Harada, H. Respiratory Disease Diagnosis through Comprehensive Analysis of Spectrograms of Lung Sounds. in \u003cem\u003eHuman Interaction \u0026amp; Emerging Technologies (IHIET 2023): Artificial Intelligence \u0026amp; Future Applications\u003c/em\u003e \u003cstrong\u003evol. 111\u003c/strong\u003e (AHFE International, 2023).\u003c/li\u003e\n\u003cli\u003eKim, Y. \u003cem\u003eet al.\u003c/em\u003e Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. \u003cem\u003eSci\u003c/em\u003e\u003cem\u003e. Rep\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 17186 (2021).\u003c/li\u003e\n\u003cli\u003eBrancatisano, T., Collett, P. W. \u0026amp; Engel, L. A. Respiratory movements of the vocal cords. \u003cem\u003eJ\u003c/em\u003e\u003cem\u003e. Appl\u003c/em\u003e\u003cem\u003e. Physiol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 1269\u0026ndash;1276 (1983).\u003c/li\u003e\n\u003cli\u003eGong, S. \u003cem\u003eet al.\u003c/em\u003e Hierarchically resistive skins as specific and multimetric on-throat wearable biosensors. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Nanotechnol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 889\u0026ndash;897 (2023).\u003c/li\u003e\n\u003cli\u003eYoo, J. Y. \u003cem\u003eet al.\u003c/em\u003e Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e. Med\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 3137\u0026ndash;3148 (2023).\u003c/li\u003e\n\u003cli\u003eAndrs, E. Advances and perspectives in the field of auscultation, with a special focus on the contribution of new intelligent communicating stethoscope systems in clinical practice, in teaching and telemedicine. In \u003cem\u003eeHealth and Remote Monitoring\u003c/em\u003e (InTech, 2012).\u003c/li\u003e\n\u003cli\u003eLee, K.-R.\u003cem\u003e et al. \u003c/em\u003eA Wearable Stethoscope for Accurate Real-Time Lung Sound Monitoring and Automatic Wheezing Detection Based on an AI Algorithm. \u003cem\u003eEngineering\u003c/em\u003e (2025) doi:10.1016/j.eng.2024.12.031.\u003c/li\u003e\n\u003cli\u003eOzawa, T., Sekiya, K., Aizawa, N., Terajima, K. \u0026amp; Nishizawa, M. Laryngeal stridor in multiple system atrophy: Clinicopathological features and causal hypotheses. \u003cem\u003eJ. Neurol. Sci.\u003c/em\u003e \u003cstrong\u003e361\u003c/strong\u003e, 243\u0026ndash;249 (2016).\u003c/li\u003e\n\u003cli\u003eCakir, O. Copper etching with cupric chloride and regeneration of waste etchant. \u003cem\u003eJ. Mater. Process. Technol.\u003c/em\u003e \u003cstrong\u003e175\u003c/strong\u003e, 63\u0026ndash;68 (2006).\u003c/li\u003e\n\u003cli\u003eKim, Y. Y., Kim, J. \u0026amp; Kim, N. \u003cem\u003epGIXS: PLS-II 3C SAXS beamline data plot program with MATLAB (0.1 version)\u003c/em\u003e. Zenodo (2022). https://doi.org/10.5281/zenodo.7042272\u003c/li\u003e\n\u003cli\u003eDouros, K., Grammeniatis, V. \u0026amp; Loukou, I. Crackles and other lung sounds. In \u003cem\u003eBreath Sounds: From Basic Science to Clinical Practice\u003c/em\u003e (eds Priftis, K. N., Hadjileontiadis, L. J. \u0026amp; Everard, M. L.) 225\u0026ndash;236 (Springer International Publishing, Cham, 2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Pohang University of Science and Technology","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"
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