High Performance Dielectrically Modulated Germanium Source Extended Double Gate Tunnel Field Effect Transistor for Biosensing Applications

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M. Joseph, Nameirakpam Premjit Singh, K. Vanlalawmpuia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8044117/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This study investigates the potential of a dielectrically-modulated Germanium source-extended double gate tunnel field-effect transistor (Ge-SE DG TFET) as a highly effective biosensor for the detection of vital biomolecules, including vitamins, proteins, and amino acids. We thoroughly analyze the device’s linear characteristics, sensitivity, and selectivity, along with its analog figure of merits (FOMs). The results indicate that negatively charged biomolecules have 58% greater sensitivity (S) than neutral ones, while neutral biomolecules show 93% better selectivity ( \(\:\varDelta\:S\) ) than the negatively charged ones. Self-heating effects (SHE) at 310 K are effectively mitigated through the use of a low bandgap Germanium source and an extended source, which reduces power density and improves heat dissipation. The proposed device is benchmarked with recent reported TFET biosensors and showcases better overall performance metrics, with a transconductance (g m ) of 12 mS, a very high cut-off frequency (f T ) of 2 THz, and S of 6.95 × 10 11 . These results highlight the Ge-SE DG TFET’s feasibility for incorporation into low-power, high-speed biosensing systems for next-generation biosensing applications. Source-extended Label-free biosensing Sensitivity Selectivity FOMs Linearity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Early and accurate detection of diseases is one of the most significant challenges in modern healthcare. Traditional diagnostic methods require long processing times, the need for centralised laboratory facilities, high costs, and a shortage of skilled personnel [ 1 ]. As a result, there is a growing demand for portable, rapid, and highly sensitive platforms that enable real-time monitoring of disease biomarkers at the point of care. Biosensors have emerged as effective alternatives to conventional diagnostic procedures because they can quickly and inexpensively detect a wide range of label-free biomolecules [ 2 ],[ 3 ]. By combining biological recognition elements with modern electrical transducers, biosensors can identify specific biomarkers at very low concentrations, facilitating early diagnosis and personalised care [ 4 ]. Moreover, their potential for miniaturisation and integration with microelectronics makes them ideal for wearable health monitoring devices and large-scale disease screening in resource-limited settings. L.C. Clark Jr. invented the Clark Electrode for measuring oxygen in 1956, which marked the first step towards the invention of biosensors [ 5 ]. K. Cammann introduced the term biosensor in 1977 to describe devices that integrate a biological recognition element with a transducer to generate a signal [ 6 ]. Among the various types of biosensors, field-effect transistor (FET)-based platforms [ 7 ] have garnered significant attention for their high sensitivity, real-time operation, and compatibility with complementary metal-oxide semiconductor (CMOS) fabrication. Specifically, metal-oxide-semiconductor field-effect transistor (MOSFET)-based biosensors [ 8 ] are extensively studied due to their scalability, low power consumption, and established fabrication techniques. However, despite these advantages, MOSFET biosensors have inherent limitations, such as the 60 mV/dec subthreshold swing constraint, relatively high leakage currents, and reduced sensitivity in physiological solutions due to Debye screening [ 9 ]. These drawbacks hinder their ability to detect ultra-low concentrations of biomolecules, which are crucial for early-stage disease diagnosis. To address these challenges, tunnel field-effect transistor (TFET)-based biosensors have been proposed as next-generation alternatives. TFETs have a subthreshold slope that is steeper than the thermionic limit of MOSFETs (less than 60 mV/dec) and exhibit significantly lower leakage currents, making them ideal for ultra-sensitive and low-power biosensing applications [ 10 ]. Their capability to detect a wide range of biomolecules, including proteins, DNA, RNA, vitamins, and amino acids, as well as specific biomarkers, such as uricase and streptavidin, at concentrations ranging from femtomolar to attomolar, further enhances their potential for early disease detection and continuous health monitoring [ 11 ]. The concept of using TFETs for biosensing applications began to gain attention around 2012 [ 12 ], as researchers recognised that the steep subthreshold swing of these devices could lead to enhanced sensitivity in biomolecule detection. The first comprehensive experimental demonstration of a silicon nanowire TFET biosensor was published, showcasing the practical feasibility of TFET biosensors for point-of-care diagnostics [ 13 ]. Junctionless Electrically Doped TFETs (JLTFETs) simplify fabrication by removing junctions but still face ambipolar issues [ 14 ],[ 15 ]. Charge Plasma (CP) TFETs increase available carriers during biomolecule interaction, enhancing sensitivity [ 16 ],[ 17 ]. Transition Metal Dichalcogenide (TMD) TFETs [ 18 ] use atomically thin materials for better sensitivity, but are mechanically fragile. Dielectric-Modulated (DM) TFETs feature a dielectric-filled cavity to differentiate biomolecules, improving sensitivity, though ambipolar conductivity can limit performance [ 19 ]. Ferroelectric (FE) TFET biosensors have a higher current ratio, but the hysteresis effects in FE materials might affect biosensor stability [ 20 ]. However, conventional silicon-based TFETs often experience low ON-current (I ON ), which can limit signal strength and degrade overall sensing performance. In this context, the Germanium Source Extended Double Gate Tunnel Field-Effect Transistor (Ge-SE DG TFET) is explored as a promising device structure for high-performance biosensing. By incorporating a Ge source [ 21 ], the tunneling barrier height is reduced, which improves band-to-band tunneling efficiency and increases I ON . Additionally, the double gate architecture enhances electrostatic control of the channel, suppresses short-channel effects, and improves scalability [ 22 ],[ 23 ]. Extending the source region beyond the gate edge in the proposed device influences the distribution of the electric field and also reduces the tunneling barrier width at the source. The proposed device facilitates both lateral and vertical tunneling. Lateral tunneling occurs across the source-channel interface in the horizontal direction, while vertical tunneling is aided by the source extension. Combining these tunneling mechanisms boosts carrier injection, allowing for larger drive current and better switching characteristics. These features enable better current modulation in response to biomolecular interactions at the gate interface. This work focuses on designing and analysing the proposed Ge-SE DG TFET for biosensing applications, aiming for ultra-sensitive detection, low power operation, and reliable performance for next-generation healthcare diagnostics. 2. Device Structure and Simulation Setup The Ge-SE DG TFET structure, as shown in Fig. 1 has an intrinsic Silicon (Si) channel sandwiched between a p-type Ge source and an n-type Si drain of lengths L s , L ch and L D, respectively. The effective channel length (L cheff ) is governed by both lateral and vertical tunnelling, and this dual mechanism modifies the junction profile, leading to enhanced tunneling. Furthermore, the source is extended into the channel by a length L S_ext, with a thickness represented by t S_ext .. A high-k dielectric (HfO 2 ) with a thickness (t ox ) is utilized as gate oxide to improve gate control and reduce leakage currents. The device has dual gates at both junctions, improving electrostatic control and tunneling efficiency. A cavity region above the channel with a length of L cavity & thickness of t cavity has a thin SiO 2 immobilisation layer (t SiO2 ), allowing for stable biomolecule attachment and effective detection. The source and drain are evenly doped with 1×10 20 cm − 3 and 5 × 10 18 cm − 3 , respectively, while the channel doping concentration is 1× 10 16 cm − 3 . A metal gate with a length L G and a work function of 4.5 eV should be used for optimal performance. The device's performance is evaluated and simulated using Sentaurus Technology Computer-Aided Design (TCAD) [ 24 ]. A non-local band-to-band tunneling (BTBT) model is employed to accurately describe the quantum mechanical tunneling process across the source–channel junction, which is essential for TFET operation. In this model, the tunneling probability is calculated by incorporating the A and B values of 1.67 × 10 15 cm − 3 s − 1 and 6.55 MV cm − 1 for Ge and 3.29 × 10 15 cm − 3 s − 1 and 23.8 MV cm − 1 for Si, respectively [ 25 ]. This model ensures spatially precise computation of tunneling generation rates, allowing for realistic evaluation of ON-state current (I ON ) properties. Fermi-Dirac carrier statistics are used throughout the simulation to precisely characterize carrier distribution, particularly in severely doped regions, ensuring proper modeling of electrostatics and carrier dynamics. Additionally, Shockley-Read-Hall (SRH) recombination has a major impact on leakage current and OFF-state behaviour. The Van Dort quantization model is used to simulate bandgap widening at the oxide-semiconductor interface, representing the consequences of quantum confinement. Given the variation in doping concentrations across the source, channel, and drain regions, a doping-dependent mobility model was used to accurately account for the influence of local doping levels on carrier mobility, allowing for the modeling of transport properties across different regions of the device. As a result, combining these models enabled a complete simulation of the TFET, offering important insight into the major aspects driving its performance. The models used are calibrated with the experimental data [ 26 ] as depicted in Fig. 2 , which shows a good alignment with the experimental result and the TCAD simulation. 3. Results & Discussions Analyzing parameters in TFET-based biosensors is crucial for their performance. Key electrical characteristics such as I ON /I OFF ratio, subthreshold swing (SS), and leakage currents indicate charge transport efficiency and confirm that their responses are due to biomolecule interactions. Evaluating sensitivity and selectivity ensures accurate detection of target biomolecules and differentiation from non-specific interactions. Additionally, interface characteristics such as biomolecule charge density and the dielectric constant (k) of the sensing layer are important for device stability and compatibility with biological fluids. The linearity analysis, along with the analog FoMs together, establishes a framework for assessing the viability of TFET-based biosensors in medical diagnostics. 3.1 Impact of Sensitivity Analysis on Biomolecules Sensitivity of the FET biosensor is defined as the ability of the device to produce a measurable change in its output parameter (e.g. I DS , SS, I ON /I OFF , V th ) in response to a given change in the concentration of the target analyte or biomolecule. Depending upon the measured parameter, various sensitivities are defined. Current Sensitivity (S) is mathematically expressed as [ 27 ]: \(\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\text{S}=\frac{{\text{I}}_{{\text{D}\text{S}}_{\text{k}}}-\:{\text{I}}_{{\text{D}}_{\text{S}{\kappa\:}=1}}\:}{{\text{I}}_{{\text{D}\text{S}}_{\text{k}=1}}}\) (1) where \(\:{\text{I}}_{{\text{D}\text{S}}_{\text{k}}}\) is I DS at k > 1 and \(\:{\text{I}}_{{\text{D}}_{\text{S}\text{k}=1}}\) is I DS at k = 1(air). On /Off Current Ratio Sensitivity (S ION/IOFF ) is defined mathematically as [ 28 ]: \(\:{\text{S}}_{\raisebox{1ex}{${\text{I}}_{\text{O}\text{N}}$}\!\left/\:\!\raisebox{-1ex}{${\text{I}}_{\text{O}\text{F}\text{F}}$}\right.}=\frac{{\left(\frac{{\text{I}}_{\text{O}\text{N}}}{{\text{I}}_{\text{O}\text{F}\text{F}}}\right)}_{\text{k}}-{\left(\frac{{\text{I}}_{\text{O}\text{N}}}{{\text{I}}_{\text{O}\text{F}\text{F}}}\right)}_{\text{k}=1}}{\:\:\:\:\:{\left(\frac{{\text{I}}_{\text{O}\text{N}}}{{\text{I}}_{\text{O}\text{F}\text{F}}}\right)}_{\text{k}=1}}\) (2) where \(\:{\left(\frac{{I}_{ON}}{{I}_{OFF}}\right)}_{k}\) = current ratio at different k values and \(\:{\left(\frac{{I}_{ON}}{{I}_{OFF}}\right)}_{k=1}\) = current ratio at air. Figure 3 (a) illustrates the transfer characteristics of the Ge-SE DG TFET under varying dielectric conditions k for neutral biomolecules (N Bio = 0), with k values of 2.63 (Biotin), 3.57 (APTES), 6.3 (Bacteriophage T7), 8 (Keratin) and 12 (Amino acids) at a constant drain voltage of 0.5 V, assuming complete filling of the nanogaps. The baseline condition (k = 1) corresponds to air-filled nanogaps. As the dielectric constant of biomolecules increases, the drain current also rises. Figure 3 (b) illustrates the device's switching behaviour, characterized by a high ON/OFF current ratio and a low subthreshold swing, which reaches 53.8 mV/dec for the highest k = 12. This indicates great potential for low-power operation. Figure 3 (c) presents the drain current sensitivity for neutral biomolecules (N Bio = 0). with permittivity ranging from 2.63 to 12. The results demonstrate a consistent increase in sensitivity as permittivity rises, underscoring the effectiveness of the Ge-SD DG TFET as a highly responsive biosensor for detecting neutral biomolecules with various dielectric properties. 3.1.2 Positively Charged Biomolecules Figure 4 illustrates the response of the Ge-SD DG TFET biosensor when exposed to positively charged biomolecules (N Bio = 1 × 10 12 C/cm 2 ) populating the nanogap region at different dielectric constant values. Figure 4 (a) shows that positively charged biomolecules have a higher response than neutral ones because these charges near the gate-channel contact increase the electric field. This results in a reduction in tunneling barrier width at the source-channel junction, considerably enhancing the probability of band-to-band tunneling (BTBT), resulting in increased drain current. Figure 4 (b) illustrates the trend of how the I ON /I OFF ratio initially increases with rising dielectric constant because high-k materials improve gate control over the channel, increasing I ON while decreasing I OFF . Increasing k values lead to high ambipolar conduction. Consequently, raising k above 6.3 results in increased gate leakage and reduced electrostatic modulation, which raises I OFF and ultimately decreases the I ON /I OFF ratio. It also explains that the reduction of SS for positive biomolecules is due to improved electrostatic gate control, which causes the device to have a steeper, more efficient response to gate voltage changes. Figure 4 (c) explains the current ratio sensitivity changes with respect to the increasing dielectric constant, with the highest \(\:{\text{S}}_{\raisebox{1ex}{${\text{I}}_{\text{O}\text{N}}$}\!\left/\:\!\raisebox{-1ex}{${\text{I}}_{\text{O}\text{F}\text{F}}$}\right.}\:\) of 4.774 × 10 7 for Bacteriophage T7. 3.1.3 Negatively Charged Biomolecules: Figure 5 (a) explicates the I D -V G curve of the Ge-SD DG TFET biosensor when its nanogap is filled with negatively charged biomolecules (N Bio = -1 × 10 12 C/cm 2 ) at different k values at V GS = 0.5V. When negatively charged biomolecules attach to the gate-channel interface, they create an electric field that opposes the gate bias. This opposition increases the width of the tunneling barrier at the source-channel junction, making it harder for carriers to tunnel through. As a result, the drain current decreases significantly, indicating an increase in the threshold voltage. Figure 5 (b) explains that the I ON /I OFF ratio for negatively charged biomolecules increases with rising k because of the way high-k dielectrics interact with charge effects at the channel interface. When negative charges attach near the gate/channel region, they oppose the gate field, which increases the tunneling barrier width and suppresses the on-state current, but at the same time, they also reduce leakage, leading to a lower off-state current. As the k increases, the enhanced gate capacitance strengthens electrostatic control over the channel, partially compensating for the current reduction by improving tunneling probability and thus recovering I ON ​. However, the suppression of I OFF ​ by the negative charges remains effective, so the denominator in the ratio does not increase significantly. This combined effect of restored I ON and consistently low I OFF results in an overall rise in the current ​ ratio with increasing k, making the biosensor effective for sensing negatively charged biomolecules. The current ratio sensitivity, as in Fig. 5 (c), increases with k due to enhanced gate electrostatic control, with the maximum value of S = 6.95 × 10 11 for amino acid biomolecule. Increasing k improves the sensor’s ability to detect small variations in biomolecule charge concentration, making the device more sensitive and effective for low-concentration biosensing applications. 3.1.4 Comparison of Neutral and Charged Biomolecules Figure 6 shows that sensitivity varies with k for three different biomolecule charge densities of N Bio = 0, N Bio = 1 × 10 12 C/cm 2 and N Bio = -1 × 10 12 C/cm 2 . Among these, negatively charged biomolecules have the highest sensitivity of 6.953 x 10 11 , followed by positively charged biomolecules, while neutral biomolecules have the lowest. As k increases, the sensitivity of the negatively charged particles increases far more than that of the other two. This is because a greater k enhances gate control and increases the effect of biomolecular charges on channel conductance. Using high-k dielectrics in conjunction with charged biomolecules maximises the device’s sensitivity, whereas neutral biomolecules have the least effect on transfer characteristics. 3.1.5 Influence of Varied Concentrations of Negatively Charged Biomolecules Increasing the concentration of negatively charged biomolecules near the channel significantly impacts the device's sensitivity by altering the energy band shape and the tunneling barrier. The presence of negative charges enhances the net negative charge density around the channel, causing the energy bands to bend and widening the tunneling barrier. This increased barrier more effectively inhibits carrier flow, leading to a stronger modulation of the channel current. Consequently, modest changes in biomolecule concentration result in substantial variations in current, thereby enhancing the sensor's ability to detect these biomolecules. The electrostatic interaction between the charges of the biomolecules and the channel produces a precise and amplified electrical response, further increasing the sensor's sensitivity as shown in Fig. 7(a) for the charge densities of -2×10 11 C/m 2 , -4 ×10 11 C/m 2 , -6 × 10 11 C/m 2 , -8× 10 11 C/m 2 and − 1 × 10 12 C/m 2 . Figure 7(b) depicts that by increasing the dielectric constant of negatively charged biomolecules near the channel improves gate capacitance and boosts electrostatic interaction with the channel. Improved coupling reduces the surface potential barrier at the channel, allowing current to be generated at lower gate voltages, resulting in a lower threshold voltage (V th ). 3.2 Exploring Irregular Hybridization of Biomolecules in the Nanogap Cavity In order to examine the steric hindrance, we simulated four different non-uniform hybridization profiles in the Ge-SD DG TFET structure: increasing, decreasing, convex, and concave step profiles, as shown in Fig. 8 (a)-(d). These configurations represent the partial filling of the nanocavity with a charge concentration of N Bio = 1 × 10¹² C/cm⁻². Each nanocavity had a surface area of 279 nm² and was separated into nine segments with different heights based on the hybridization profile. In the increasing profile, the segment heights steadily grew from 4.35 nm at the nanogap entry to 39.15 nm at the rear. In contrast, the decreasing profile exhibited a reverse trend. The concave profile arranged the segments evenly and sloped downwards toward the centre, while the convex profile mirrored this arrangement in an outward-facing arc. These structures allowed for the investigation of the spatial distribution of biomolecules’ impact on sensitivity. Figure 9 illustrates the sensitivity of the Ge-SD DG TFET biosensor as a function of k, with a positive N Bio for four different nanocavity profiles of increasing, decreasing, convex, and concave. The sensitivity is plotted on a logarithmic scale, emphasising differences across configurations and k. For all nanocavity shapes, S increases with k; however, the extent and pattern of this increase differ significantly between cavity types. Convex cavity exhibits the highest sensitivity at all k values, reaching the maximum value of 10 6 at k = 12. The concave cavity also shows a substantial sensitivity boost with k, especially for higher values, though less than the convex cavity. Both convex and concave profiles provide enhanced local electric fields and better interaction with biomolecules, significantly amplifying the device’s response to changes in dielectric constants and biomolecule concentration. Decreasing cavity demonstrates moderate sensitivity gains with increasing k, surpassing the increasing cavity, which yields the lowest sensitivity. Thus, careful nanocavity engineering combined with high-k materials can lead to optimal biosensor sensitivity. 3.3 Selectivity Selectivity refers to the biosensor’s capacity to detect a specific target analyte in a complicated mixture that contains other compounds or potential interfering agents. It is one of the most important characteristics since it assures that the biosensor only reacts to the target biomolecule. The sensor surface, which is often coated with receptors, antibodies, or probe DNA strands, is chemically modified to ensure it specifically binds to the intended biomolecule. At the same time, non-target molecules bind very weakly. When the target biomolecule attaches, its electric charges influence the energy band structure near the channel, resulting in a measurable change in current. In contrast, non-target molecules either produce negligible charge effects or are repelled, which helps minimize false signals. Selectivity is expressed mathematically as [ 29 ]: $$\:\:\:\:\:Selectivity\:\varDelta\:S=\frac{{S}_{Target\:Biomolecule}}{{S}_{Non-Target\:Biomolecule}}$$ 3 Where \(\:{\text{S}}_{\text{T}\text{a}\text{r}\text{g}\text{e}\text{t}\:\text{B}\text{i}\text{o}\text{m}\text{o}\text{l}\text{e}\text{c}\text{u}\text{l}\text{e}}\) is the Sensitivity for Target Biomolecule and \(\:{\text{S}}_{\text{N}\text{o}\text{n}-\text{T}\text{a}\text{r}\text{g}\text{e}\text{t}\:\text{B}\text{i}\text{o}\text{m}\text{o}\text{l}\text{e}\text{c}\text{u}\text{l}\text{e}}\) is the Sensitivity for Non-Target Biomolecule. Figure 10 displays the variation of selectivity in a biosensor with the k for three different charge conditions of neutral, positively and negatively charged biomolecules while considering k = 2.57 (Biotin) as the non-target biomolecule. As the dielectric constant increases from 3.57 to 12, selectivity rises for all biomolecule charges; however, the trend varies for each. Neutral biomolecules exhibit the maximum selectivity across all dielectric constants. At k = 12(Gelatine) selectivity reaches a peak of almost 10 11, indicating an increase of 93% compared to negatively charged. Negatively charged biomolecules have intermediate selectivity, growing progressively as the k rises but always below neutral. Selectivity is poor when k = 3.57 and improves as k increases. Positively charged biomolecules have the lowest selectivity at each k value, but improve significantly as the dielectric constant increases. The findings indicate that, while higher dielectric constants improve selectivity for all biomolecule types due to greater gate control and less screening effects, neutral biomolecules optimise selectivity, whereas charged biomolecules decrease it. This could be owing to charge-induced modulation effects that reduce the differential response between target and non-target analytes, resulting in slightly reduced selectivity when compared to a neutral environment. High-k materials accentuate these distinctions, resulting in strong selectivity modification dependent on both the dielectric environment and the biomolecular charge state. 3.4 Analog Figure of Merits Analog and RF Figures of Merit (FOMs) are significant performance criteria that reflect how well the device performs in analog and high-frequency applications, both of which are vital for sensing signals with high precision and speed. Figure 11 collectively illustrates that the dielectric constant affects the analogue/RF performance metrics of the Ge-SD DG TFET-based biosensor with a fixed N Bio of 1 × 10 12 C/cm² and V GS . Higher values of transconductance(g m ) indicate stronger amplification of input signals, which is critical for high sensitivity in biosensors. Mathematically, transconductance is [ 30 ]: $$\:{g}_{m}=\:\frac{\partial\:{I}_{D}}{\partial\:{V}_{GS}}$$ 4 where, \(\:\partial\:{\text{I}}_{\text{D}}\) = Drain current and \(\:\partial\:{\text{V}}_{\text{G}\text{S}}\) = Gate voltage. As shown in Fig. 11 (a), g m peaks sharply at lower V GS and higher values for larger k, while it is much lower and more spread out for smaller k values. Due to a higher k, the gate control improves, resulting in a stronger modulation of current through the channel. This leads to a higher g m , indicating a more sensitive device to changes in V GS , and a sharper turn-on characteristic. Using (4) g m for k = 12 yields 0.13mS. Total gate capacitance (C gg ) is a crucial parameter that affects device switching speed, power consumption, and analog/RF performance. Figure 11 (b) depicts that C gg increases with both V GS and k, but the growth saturates at higher gate voltages. Higher k results in an increased C gg across all V GS , with k = 12 achieving a maximum value of 1.55×10 − 15 F. The increase in k enhances the gate oxide capacitance, resulting in a higher total gate capacitance, which allows for greater charge control, which impacts switching speed and frequency response. The cutoff frequency (f T ) is the frequency at which the current gain equals unity. A greater f T enables faster response times, which is beneficial for rapid biomolecule identification. The relationship is given as: $$\:{f}_{T}=\frac{{g}_{m}}{2\pi\:{C}_{gg}}$$ 5 f T shows a sharp peak of 2 THz for higher k values at k = 12, coinciding with the region of high transconductance, and then decreases as V GS increases further, as shown in Fig. 11 (c) Lower k values exhibit much lower f T throughout the V GS range. Even though C gg increases for high k, the very large increase in g m dominates, boosting f T significantly. This means devices with higher k can operate at higher speeds (higher f T ), making them suitable for fast and high-frequency biosensing applications. 3.5 Linearity Analysis Linearity analysis of a TFET-based biosensor is critical because it determines the degree to which the device can translate biomolecule-induced potential changes into electrical signals without distortion, which is required for accurate calibration, high dynamic range, and integration into analog/RF sensing circuits. Because of the steep band-to-band tunneling mechanism and ambipolar conduction, TFETs are intrinsically nonlinear, which causes harmonic distortion, intermodulation, and gain compression when the input signal or biomolecule-induced potential increases. To assess linearity, the device response is enlarged with higher-order transconductance terms and examined using harmonic distortion and intermodulation analysis. These approaches assess distortion and determine the ideal bias range in which the sensor provides high sensitivity while exhibiting low nonlinearity. Thus, linearity analysis not only ensures correct biomolecule detection but also directs design decisions—such as biasing, structural engineering, and differential operation—to achieve both sensitivity and signal integrity. In a TFET-based biosensor, the higher-order transconductance parameters g m2 and g m3 are crucial for understanding nonlinearity in device response. The second-order transconductance (g m2 ) illustrates the small-signal gain variation with gate voltage and is primarily responsible for producing even-order nonlinearities. Ambipolar conduction in TFETs is a common source of these aberrations. $$\:{g}_{m2}=\:\frac{{\partial\:}^{2}{I}_{D}}{{\partial\:}^{2}{V}_{GS}}$$ 6 Third-order transconductance (g m3 ) represents the curvature of the transfer characteristic at a deeper level and governs odd-order nonlinearities and is expressed as: $$\:{g}_{m3}=\:\frac{{\partial\:}^{3}{I}_{D}}{{\partial\:}^{3}{V}_{GS}}$$ 7 The second-order voltage intercept point (VIP2) is an important linearity parameter that specifies the input gate voltage amplitude at which the extrapolated second-order distortion products would become equivalent in magnitude to the fundamental output signal. VIP2 is articulated as: \(\:VIP2=4\:\times\:\:\frac{{g}_{m}}{{g}_{m2}}\) (8) VIP3 (third-order voltage intercept point) quantifies the device’s resilience to intermodulation distortion by extrapolating the input voltage at which the third-order product matches the fundamental output and is given by: \(\:VIP3=\:\sqrt{\left(24\:\times\:\frac{{g}_{m}}{{g}_{m3}}\right)}\) (9) The Third-Order Input Intercept Point (IIP3) serves as a critical metric in evaluating a device's performance, indicating the input signal level at which the third-order intermodulation distortion (IIM3) products match the amplitude of the fundamental signal when referred to the gate. This pivotal point can be precisely defined using the Taylor expansion of the device's transfer characteristic, underscoring its significance in assessing linearity and signal integrity. The expression is articulated as: \(\:IIP3=\:\frac{2}{3}\times\:\frac{{g}_{m}}{{g}_{m3}\times\:{R}_{s}}\) where, R s =50 Ω (10) IMD3 refers to the third-order intermodulation distortion products generated when two or more signals pass through a nonlinear system and is given by: \(\:IMD3={\left(\frac{9}{2}\times\:{VIP3}^{3}\times\:{g}_{m3}\right)}^{2}\times\:{R}_{s}\) (11) Figure 12 illustrates varying the surface charge density of immobilised biomolecules, i.e negatively charged, neutral and positively charged, affects the g m2 , g m3 , VIP2, VIP3, IIP3 and IMD3 as functions of gate voltage. All simulations assume a high-k dielectric of 12. Figure 12 (a) & (b) compares three charged biomolecules. It shows that negative N Bio has a higher g m2 and g m3 value as the biomolecular surface charge critically modulates both the magnitude and bias dependence, which alter the electrostatic environment and charge transport characteristics. Higher VIP2 in Fig. 12 (c) indicates more effective suppression of these distortions, allowing the device to accommodate bigger biomolecule-induced potentials or excitation signals without significantly reducing signal quality. It provides direct insight into the biosensor's ability to withstand even-order nonlinearities. It aids in the optimization of biasing and device structure, resulting in sensitivity and consistent linear performance regardless of charge. Higher VIP₃ significantly boosts the signal-to-interference ratio, allowing for the detection of the slightest biomolecular binding, as shown in Fig. 12 (d). Figure 12 (e) demonstrates that biomolecular surface charge shifts gate-voltage bias sites, causing third-order distortion peaks. It also affects linearity at threshold and strong inversion. Positive surface charge enhances IIP3 peaks but causes deeper nonlinearity around the threshold, while negative charge reduces threshold distortion but limits maximum dynamic range. Figure 12 (f) shows the IMD3 plateaus occurring between approximately – 10 dBm and + 50 dBm across all surface-charge conditions. This indicates that while further increases in gate voltage may yield only modest increases in distortion, the behaviour varies based on the type of surface charge. Positive biomolecular charges show slightly higher IMD3 levels reaching up to + 50 dBm, indicating a more significant third-order nonlinearity under these conditions. In contrast, negative charges and neutral charges achieve similar plateau levels around + 40 dBm. Operating within this range maximizes sensitivity but also results in considerable spurious distortion 3.6 Benchmarking with other TFET Biosensors: The proposed device is compared to previously described TFET biosensors, as shown in Table 1 . The main factors considered in this comparison are the on-current, leakage current, current ratio, and drain current sensitivity. The Ge-SE DG TFET demonstrates a high I ON of 3.76 x 10 − 3 A/µm compared to the other TFET biosensors. Although the I ON /I OFF ratio of the proposed device is not the highest, it exhibits the best drain current sensitivity of 6.95 x 10 11 among existing TFET-based biosensors. Table 1 Comparative analysis between Ge-SE DG TFET and reported TFET Biosensors. S.no Device I ON (A/µm) I OFF (A/µm) I ON /I OFF S 1. HM-SE-TFET [ 31 ] 10 − 4 10 − 15 10 13 6.87 × 10 5 2. HV-TFET [ 32 ] - - 9.67 × 10 10 3.61 × 10 8 3. HSS-TFET [ 32 ] - - 6.04 × 10 9 2.48 × 10 7 4. SiGe FE-VTFET [ 33 ] 1.02 × 10 –4 7.65 × 10 –18 1.33 × 10 13 1.08 × 10 3 5. FE DGDM-JLTFET [ 34 ] 0.46 × 10 − 3 1.46 × 10 − 14 3.15 × 10 10 8.61 × 10 4 6. SiGe DM TFET [ 35 ] - - 1.947 × 10 8 1.548 × 10 8 7. DM-ED-JLTFET [ 36 ] 10 − 6 10 − 15 10 9 5.58× 10 10 8. δ-doped Ge-S vTFET [ 37 ] 1.44 × 10 − 3 1.66 × 10 − 14 8.66 × 10 10 8.15 × 10 8 9. DLSVH-TFET [ 38 ] 10 − 6 10 − 16 10 10 5.66 × 10 6 10. DG- ES TFET [ 39 ] - - 15× 10 3 45 × 10 2 11. Ge-SD DG TFET (Proposed Work) 3.76 × 10 − 3 2.57 × 10 − 13 1.459 × 10 10 6.95× 10 11 4. Conclusion This work presents a comprehensive investigation of a dielectrically modulated Germanium Source-Extended Double Gate TFET (Ge-SE DG TFET) biosensor. The study reveals that the device’s extended source structure effectively enlarges the tunneling junction area, creating additional paths for BTBT. This enhances the tunneling probability, resulting in a high I ON ​ of 3.76×10 − 3 A/µm along with a SS of 49mV/dec. The biosensing capabilities of the proposed device are analyzed in terms of its sensitivity and selectivity for neutral and charged biomolecules. The results show a higher sensitivity to charged biomolecules, with negatively charged biomolecules exhibiting 58% higher sensitivity than neutral biomolecules. Furthermore, the device demonstrates strong selectivity, particularly for neutral biomolecules. At k = 12, neutral biomolecules show 93% higher selectivity than charged ones, indicating the biosensor’s ability to differentiate between different biomolecules based on their electrical properties. The Ge-SE DG TFET also exhibits increased analog FOM values of 12 mS (g m ) and 2 THz (f T ). The linearity analysis showcases that positively charged biomolecules have better linearity (VIP3) as well as high sensitivity sensing (IMD3) than neutral and negatively charged biomolecules. On benchmarking with the recently reported TFET structures, although the proposed device has a lower current ratio, it exhibits the best drain current sensitivity of 6.95 x 10 11 . Hence, the dielectrically modulated Ge-SE DG TFET biosensor is a promising next-generation biosensor technology capable of meeting the demands of high-sensitivity, high-speed, and low-power biological and medical applications. Declarations Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Author Contributions: Sneha M Joseph: Conceptualization, Formal analysis, Methodology, writing original draft, Editing. Nameirakpam Premjit Singh: Formal analysis and K. Vanlalawmpuia: problem formulation, Supervision and reviewing. Data Availability Not applicable. Ethics Approval: The Authors approve that the submitted work is original and has not been published elsewhere in any form or language (partially or in full). Consent to Participate: All authors voluntarily agree to participate in this paper. Consent for Publication: All authors permit the Journal to publish this paper. References Yu Y, Nyein HYY, Gao W, Javey A (2020) Flexible electrochemical bioelectronics: the rise of in situ bioanalysis. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Jan, 2026 Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 10 Nov, 2025 Submission checks completed at journal 10 Nov, 2025 First submitted to journal 06 Nov, 2025 You are reading this latest preprint version 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. 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1","display":"","copyAsset":false,"role":"figure","size":100130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGe Source Extended Double Gate TFET (Ge-SEDGTFET) Biosensor\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/c989678402315378dbc4b149.png"},{"id":98061237,"identity":"a968b2eb-697e-4201-81d6-f7efd8620fbe","added_by":"auto","created_at":"2025-12-12 10:58:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127067,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration of proposed device with experimental data [26]\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/23c50740d7fad26c43d6b518.png"},{"id":98061239,"identity":"6029663b-afb5-479d-a2a5-6214773de0dc","added_by":"auto","created_at":"2025-12-12 10:58:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":385115,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNanogap cavity filled with neutral charged biomolecule (a) IDVG curve (b) Current ratio and subthreshold swing (c) Current Ratio Sensitivity and Current Ratio\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/901b0664a4d14a745c23df60.png"},{"id":98428343,"identity":"c674ee84-1ecb-4000-a6c4-17298490eb2f","added_by":"auto","created_at":"2025-12-17 16:41:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":419761,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNanogap cavity filled with positively charged biomolecule (a) IDVG curve (b) Current ratio and subthreshold swing (c) Current Ratio Sensitivity and Current Ratio\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/5e9f95de5225ec4e7e2ce8b4.png"},{"id":98428082,"identity":"7e0f43db-f024-4738-94ab-353536d180b5","added_by":"auto","created_at":"2025-12-17 16:41:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":397384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNanogap cavity filled with negative charged biomolecule (a) IDVG curve (b) Current ratio and subthreshold swing (c) Current Ratio Sensitivity and Current Ratio\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/0801b929500be67d0cdd7905.png"},{"id":98061245,"identity":"13449dc4-e721-429c-b2af-d8494f7cf435","added_by":"auto","created_at":"2025-12-12 10:58:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":142224,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensitivity for positive, negative and neutral charged biomolecules for different values of k.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/7ace35656d0894a7195773be.png"},{"id":98428143,"identity":"c750219e-4af1-44ca-89b0-c935d3cc9ca8","added_by":"auto","created_at":"2025-12-17 16:41:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":318064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) Sensitivity (b) Threshold Voltage for different concentrations of negatively charged biomolecules for different values of k.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/d22789719de3cb79d5d11936.png"},{"id":98428879,"identity":"9a241e7f-3de4-4ee1-9456-6d5d13ce8f8b","added_by":"auto","created_at":"2025-12-17 16:42:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":17618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIrregular profiles due to steric hindrance for partially filled nanogaps showing (a) increasing (b) decreasing (c) convex(d) concave profiles\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/a5c934a856dc9e0c5b0c35f1.png"},{"id":98428310,"identity":"5a0e2d16-68d8-4154-90bb-d1883e454b49","added_by":"auto","created_at":"2025-12-17 16:41:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":134204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrain Current Sensitivity for irregular hybridization profiles at different k values\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/ede67b432a305a62337036de.png"},{"id":98428919,"identity":"dce6c6dc-e5be-4d22-8117-d98d815f45a9","added_by":"auto","created_at":"2025-12-17 16:42:35","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":98867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelectivity for three charged biomolecules at different k values\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/070ff32eab52b788a6b51264.png"},{"id":98428659,"identity":"a40ccd03-dd5f-44b3-8399-4822088234dc","added_by":"auto","created_at":"2025-12-17 16:42:14","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":500634,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) Transconductance (b) Total Gate Capacitance (c) Cut-off frequency for positively charged biomolecule at different k values\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/78b6816633e38d16ecd96e4a.png"},{"id":98426344,"identity":"ee233327-24df-4eb5-8d38-43acae11995f","added_by":"auto","created_at":"2025-12-17 16:36:12","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":389406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of (a) gm2 (b) gm3 (c) VIP2 (d) VIP3 (e ) IIP3 (f) IMD3 for negatively charged, neutral \u0026amp; positively charged biomolecules\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/e360937a4e771831d8a61e10.png"},{"id":98444720,"identity":"8d7e0edc-eea9-4114-8d44-883f7511ce12","added_by":"auto","created_at":"2025-12-17 17:17:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3988885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8044117/v1/477687e8-ddb0-420f-97d8-aa8dd4f46f29.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"High Performance Dielectrically Modulated Germanium Source Extended Double Gate Tunnel Field Effect Transistor for Biosensing Applications","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEarly and accurate detection of diseases is one of the most significant challenges in modern healthcare. Traditional diagnostic methods require long processing times, the need for centralised laboratory facilities, high costs, and a shortage of skilled personnel [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As a result, there is a growing demand for portable, rapid, and highly sensitive platforms that enable real-time monitoring of disease biomarkers at the point of care. Biosensors have emerged as effective alternatives to conventional diagnostic procedures because they can quickly and inexpensively detect a wide range of label-free biomolecules [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e],[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. By combining biological recognition elements with modern electrical transducers, biosensors can identify specific biomarkers at very low concentrations, facilitating early diagnosis and personalised care [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, their potential for miniaturisation and integration with microelectronics makes them ideal for wearable health monitoring devices and large-scale disease screening in resource-limited settings.\u003c/p\u003e\u003cp\u003eL.C. Clark Jr. invented the Clark Electrode for measuring oxygen in 1956, which marked the first step towards the invention of biosensors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. K. Cammann introduced the term biosensor in 1977 to describe devices that integrate a biological recognition element with a transducer to generate a signal [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among the various types of biosensors, field-effect transistor (FET)-based platforms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] have garnered significant attention for their high sensitivity, real-time operation, and compatibility with complementary metal-oxide semiconductor (CMOS) fabrication. Specifically, metal-oxide-semiconductor field-effect transistor (MOSFET)-based biosensors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] are extensively studied due to their scalability, low power consumption, and established fabrication techniques. However, despite these advantages, MOSFET biosensors have inherent limitations, such as the 60 mV/dec subthreshold swing constraint, relatively high leakage currents, and reduced sensitivity in physiological solutions due to Debye screening [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These drawbacks hinder their ability to detect ultra-low concentrations of biomolecules, which are crucial for early-stage disease diagnosis.\u003c/p\u003e\u003cp\u003eTo address these challenges, tunnel field-effect transistor (TFET)-based biosensors have been proposed as next-generation alternatives. TFETs have a subthreshold slope that is steeper than the thermionic limit of MOSFETs (less than 60 mV/dec) and exhibit significantly lower leakage currents, making them ideal for ultra-sensitive and low-power biosensing applications [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Their capability to detect a wide range of biomolecules, including proteins, DNA, RNA, vitamins, and amino acids, as well as specific biomarkers, such as uricase and streptavidin, at concentrations ranging from femtomolar to attomolar, further enhances their potential for early disease detection and continuous health monitoring [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The concept of using TFETs for biosensing applications began to gain attention around 2012 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], as researchers recognised that the steep subthreshold swing of these devices could lead to enhanced sensitivity in biomolecule detection. The first comprehensive experimental demonstration of a silicon nanowire TFET biosensor was published, showcasing the practical feasibility of TFET biosensors for point-of-care diagnostics [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Junctionless Electrically Doped TFETs (JLTFETs) simplify fabrication by removing junctions but still face ambipolar issues [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e],[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Charge Plasma (CP) TFETs increase available carriers during biomolecule interaction, enhancing sensitivity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e],[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Transition Metal Dichalcogenide (TMD) TFETs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] use atomically thin materials for better sensitivity, but are mechanically fragile. Dielectric-Modulated (DM) TFETs feature a dielectric-filled cavity to differentiate biomolecules, improving sensitivity, though ambipolar conductivity can limit performance [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Ferroelectric (FE) TFET biosensors have a higher current ratio, but the hysteresis effects in FE materials might affect biosensor stability [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, conventional silicon-based TFETs often experience low ON-current (I\u003csub\u003eON\u003c/sub\u003e), which can limit signal strength and degrade overall sensing performance.\u003c/p\u003e\u003cp\u003eIn this context, the Germanium Source Extended Double Gate Tunnel Field-Effect Transistor (Ge-SE DG TFET) is explored as a promising device structure for high-performance biosensing. By incorporating a Ge source [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the tunneling barrier height is reduced, which improves band-to-band tunneling efficiency and increases I\u003csub\u003eON\u003c/sub\u003e. Additionally, the double gate architecture enhances electrostatic control of the channel, suppresses short-channel effects, and improves scalability [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e],[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Extending the source region beyond the gate edge in the proposed device influences the distribution of the electric field and also reduces the tunneling barrier width at the source. The proposed device facilitates both lateral and vertical tunneling. Lateral tunneling occurs across the source-channel interface in the horizontal direction, while vertical tunneling is aided by the source extension. Combining these tunneling mechanisms boosts carrier injection, allowing for larger drive current and better switching characteristics. These features enable better current modulation in response to biomolecular interactions at the gate interface.\u003c/p\u003e\u003cp\u003eThis work focuses on designing and analysing the proposed Ge-SE DG TFET for biosensing applications, aiming for ultra-sensitive detection, low power operation, and reliable performance for next-generation healthcare diagnostics.\u003c/p\u003e"},{"header":"2. Device Structure and Simulation Setup","content":"\u003cp\u003eThe Ge-SE DG TFET structure, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e has an intrinsic Silicon (Si) channel sandwiched between a p-type Ge source and an n-type Si drain of lengths L\u003csub\u003es\u003c/sub\u003e, L\u003csub\u003ech\u003c/sub\u003e and L\u003csub\u003eD,\u003c/sub\u003e respectively. The effective channel length (L\u003csub\u003echeff\u003c/sub\u003e) is governed by both lateral and vertical tunnelling, and this dual mechanism modifies the junction profile, leading to enhanced tunneling. Furthermore, the source is extended into the channel by a length L\u003csub\u003eS_ext,\u003c/sub\u003e with a thickness represented by t\u003csub\u003eS_ext\u003c/sub\u003e.. A high-k dielectric (HfO\u003csub\u003e2\u003c/sub\u003e) with a thickness (t\u003csub\u003eox\u003c/sub\u003e) is utilized as gate oxide to improve gate control and reduce leakage currents. The device has dual gates at both junctions, improving electrostatic control and tunneling efficiency. A cavity region above the channel with a length of L\u003csub\u003ecavity\u003c/sub\u003e \u0026amp; thickness of t\u003csub\u003ecavity\u003c/sub\u003e has a thin SiO\u003csub\u003e2\u003c/sub\u003e immobilisation layer (t\u003csub\u003eSiO2\u003c/sub\u003e), allowing for stable biomolecule attachment and effective detection. The source and drain are evenly doped with 1\u0026times;10\u003csup\u003e20\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 5 \u0026times; 10\u003csup\u003e18\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively, while the channel doping concentration is 1\u0026times; 10\u003csup\u003e16\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. A metal gate with a length L\u003csub\u003eG\u003c/sub\u003e and a work function of 4.5 eV should be used for optimal performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe device's performance is evaluated and simulated using Sentaurus Technology Computer-Aided Design (TCAD) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A non-local band-to-band tunneling (BTBT) model is employed to accurately describe the quantum mechanical tunneling process across the source\u0026ndash;channel junction, which is essential for TFET operation. In this model, the tunneling probability is calculated by incorporating the A and B values of 1.67 \u0026times; 10\u003csup\u003e15\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 6.55 MV cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for Ge and 3.29 \u0026times; 10\u003csup\u003e15\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 23.8 MV cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for Si, respectively [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This model ensures spatially precise computation of tunneling generation rates, allowing for realistic evaluation of ON-state current (I\u003csub\u003eON\u003c/sub\u003e) properties. Fermi-Dirac carrier statistics are used throughout the simulation to precisely characterize carrier distribution, particularly in severely doped regions, ensuring proper modeling of electrostatics and carrier dynamics. Additionally, Shockley-Read-Hall (SRH) recombination has a major impact on leakage current and OFF-state behaviour. The Van Dort quantization model is used to simulate bandgap widening at the oxide-semiconductor interface, representing the consequences of quantum confinement. Given the variation in doping concentrations across the source, channel, and drain regions, a doping-dependent mobility model was used to accurately account for the influence of local doping levels on carrier mobility, allowing for the modeling of transport properties across different regions of the device. As a result, combining these models enabled a complete simulation of the TFET, offering important insight into the major aspects driving its performance. The models used are calibrated with the experimental data [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which shows a good alignment with the experimental result and the TCAD simulation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3. Results \u0026 Discussions","content":"\u003cp\u003eAnalyzing parameters in TFET-based biosensors is crucial for their performance. Key electrical characteristics such as I\u003csub\u003eON\u003c/sub\u003e/I\u003csub\u003eOFF\u003c/sub\u003e ratio, subthreshold swing (SS), and leakage currents indicate charge transport efficiency and confirm that their responses are due to biomolecule interactions. Evaluating sensitivity and selectivity ensures accurate detection of target biomolecules and differentiation from non-specific interactions. Additionally, interface characteristics such as biomolecule charge density and the dielectric constant (k) of the sensing layer are important for device stability and compatibility with biological fluids. The linearity analysis, along with the analog FoMs together, establishes a framework for assessing the viability of TFET-based biosensors in medical diagnostics.\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Impact of Sensitivity Analysis on Biomolecules\u003c/h2\u003e\u003cp\u003eSensitivity of the FET biosensor is defined as the ability of the device to produce a measurable change in its output parameter (e.g. I\u003csub\u003eDS\u003c/sub\u003e, SS, I\u003csub\u003eON\u003c/sub\u003e/I\u003csub\u003eOFF\u003c/sub\u003e, V\u003csub\u003eth\u003c/sub\u003e) in response to a given change in the concentration of the target analyte or biomolecule. Depending upon the measured parameter, various sensitivities are defined. Current Sensitivity (S) is mathematically expressed as [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{S}=\\frac{{\\text{I}}_{{\\text{D}\\text{S}}_{\\text{k}}}-\\:{\\text{I}}_{{\\text{D}}_{\\text{S}{\\kappa\\:}=1}}\\:}{{\\text{I}}_{{\\text{D}\\text{S}}_{\\text{k}=1}}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{I}}_{{\\text{D}\\text{S}}_{\\text{k}}}\\)\u003c/span\u003e\u003c/span\u003e is I\u003csub\u003eDS\u003c/sub\u003e at k\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{I}}_{{\\text{D}}_{\\text{S}\\text{k}=1}}\\)\u003c/span\u003e\u003c/span\u003e is I\u003csub\u003eDS\u003c/sub\u003e at k\u0026thinsp;=\u0026thinsp;1(air).\u003c/p\u003e\u003cp\u003eOn /Off Current Ratio Sensitivity (S\u003csub\u003eION/IOFF\u003c/sub\u003e) is defined mathematically as [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\raisebox{1ex}{${\\text{I}}_{\\text{O}\\text{N}}$}\\!\\left/\\:\\!\\raisebox{-1ex}{${\\text{I}}_{\\text{O}\\text{F}\\text{F}}$}\\right.}=\\frac{{\\left(\\frac{{\\text{I}}_{\\text{O}\\text{N}}}{{\\text{I}}_{\\text{O}\\text{F}\\text{F}}}\\right)}_{\\text{k}}-{\\left(\\frac{{\\text{I}}_{\\text{O}\\text{N}}}{{\\text{I}}_{\\text{O}\\text{F}\\text{F}}}\\right)}_{\\text{k}=1}}{\\:\\:\\:\\:\\:{\\left(\\frac{{\\text{I}}_{\\text{O}\\text{N}}}{{\\text{I}}_{\\text{O}\\text{F}\\text{F}}}\\right)}_{\\text{k}=1}}\\)\u003c/span\u003e\u003c/span\u003e (2) where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left(\\frac{{I}_{ON}}{{I}_{OFF}}\\right)}_{k}\\)\u003c/span\u003e\u003c/span\u003e= current ratio at different k values and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left(\\frac{{I}_{ON}}{{I}_{OFF}}\\right)}_{k=1}\\)\u003c/span\u003e\u003c/span\u003e= current ratio at air.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a) illustrates the transfer characteristics of the Ge-SE DG TFET under varying dielectric conditions k for neutral biomolecules (N\u003csub\u003eBio\u003c/sub\u003e = 0), with k values of 2.63 (Biotin), 3.57 (APTES), 6.3 (Bacteriophage T7), 8 (Keratin) and 12 (Amino acids) at a constant drain voltage of 0.5 V, assuming complete filling of the nanogaps. The baseline condition (k\u0026thinsp;=\u0026thinsp;1) corresponds to air-filled nanogaps. As the dielectric constant of biomolecules increases, the drain current also rises. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b) illustrates the device's switching behaviour, characterized by a high ON/OFF current ratio and a low subthreshold swing, which reaches 53.8 mV/dec for the highest k\u0026thinsp;=\u0026thinsp;12. This indicates great potential for low-power operation. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(c) presents the drain current sensitivity for neutral biomolecules (N\u003csub\u003eBio\u003c/sub\u003e = 0). with permittivity ranging from 2.63 to 12. The results demonstrate a consistent increase in sensitivity as permittivity rises, underscoring the effectiveness of the Ge-SD DG TFET as a highly responsive biosensor for detecting neutral biomolecules with various dielectric properties.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Positively Charged Biomolecules\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the response of the Ge-SD DG TFET biosensor when exposed to positively charged biomolecules (N\u003csub\u003eBio\u003c/sub\u003e = 1 \u0026times; 10\u003csup\u003e12\u003c/sup\u003e C/cm\u003csup\u003e2\u003c/sup\u003e) populating the nanogap region at different dielectric constant values. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a) shows that positively charged biomolecules have a higher response than neutral ones because these charges near the gate-channel contact increase the electric field. This results in a reduction in tunneling barrier width at the source-channel junction, considerably enhancing the probability of band-to-band tunneling (BTBT), resulting in increased drain current. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(b) illustrates the trend of how the I\u003csub\u003eON\u003c/sub\u003e/I\u003csub\u003eOFF\u003c/sub\u003e ratio initially increases with rising dielectric constant because high-k materials improve gate control over the channel, increasing I\u003csub\u003eON\u003c/sub\u003e while decreasing I\u003csub\u003eOFF\u003c/sub\u003e. Increasing k values lead to high ambipolar conduction. Consequently, raising k above 6.3 results in increased gate leakage and reduced electrostatic modulation, which raises I\u003csub\u003eOFF\u003c/sub\u003e and ultimately decreases the I\u003csub\u003eON\u003c/sub\u003e/I\u003csub\u003eOFF\u003c/sub\u003e ratio. It also explains that the reduction of SS for positive biomolecules is due to improved electrostatic gate control, which causes the device to have a steeper, more efficient response to gate voltage changes. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(c) explains the current ratio sensitivity changes with respect to the increasing dielectric constant, with the highest \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\raisebox{1ex}{${\\text{I}}_{\\text{O}\\text{N}}$}\\!\\left/\\:\\!\\raisebox{-1ex}{${\\text{I}}_{\\text{O}\\text{F}\\text{F}}$}\\right.}\\:\\)\u003c/span\u003e\u003c/span\u003eof 4.774 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e for Bacteriophage T7.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Negatively Charged Biomolecules:\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a) explicates the I\u003csub\u003eD\u003c/sub\u003e-V\u003csub\u003eG\u003c/sub\u003e curve of the Ge-SD DG TFET biosensor when its nanogap is filled with negatively charged biomolecules (N\u003csub\u003eBio\u003c/sub\u003e = -1 \u0026times; 10\u003csup\u003e12\u003c/sup\u003e C/cm\u003csup\u003e2\u003c/sup\u003e) at different k values at V\u003csub\u003eGS\u003c/sub\u003e = 0.5V. When negatively charged biomolecules attach to the gate-channel interface, they create an electric field that opposes the gate bias. This opposition increases the width of the tunneling barrier at the source-channel junction, making it harder for carriers to tunnel through. As a result, the drain current decreases significantly, indicating an increase in the threshold voltage. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b) explains that the I\u003csub\u003eON\u003c/sub\u003e/I\u003csub\u003eOFF\u003c/sub\u003e ratio for negatively charged biomolecules increases with rising k because of the way high-k dielectrics interact with charge effects at the channel interface. When negative charges attach near the gate/channel region, they oppose the gate field, which increases the tunneling barrier width and suppresses the on-state current, but at the same time, they also reduce leakage, leading to a lower off-state current. As the k increases, the enhanced gate capacitance strengthens electrostatic control over the channel, partially compensating for the current reduction by improving tunneling probability and thus recovering I\u003csub\u003eON\u003c/sub\u003e​. However, the suppression of I\u003csub\u003eOFF\u003c/sub\u003e​ by the negative charges remains effective, so the denominator in the ratio does not increase significantly. This combined effect of restored I\u003csub\u003eON\u003c/sub\u003e and consistently low I\u003csub\u003eOFF\u003c/sub\u003e results in an overall rise in the current ​ ratio with increasing k, making the biosensor effective for sensing negatively charged biomolecules. The current ratio sensitivity, as in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(c), increases with k due to enhanced gate electrostatic control, with the maximum value of S\u0026thinsp;=\u0026thinsp;6.95 \u0026times; 10\u003csup\u003e11\u003c/sup\u003e for amino acid biomolecule. Increasing k improves the sensor\u0026rsquo;s ability to detect small variations in biomolecule charge concentration, making the device more sensitive and effective for low-concentration biosensing applications.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4 Comparison of Neutral and Charged Biomolecules\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that sensitivity varies with k for three different biomolecule charge densities of N\u003csub\u003eBio\u003c/sub\u003e= 0, N\u003csub\u003eBio\u003c/sub\u003e = 1 \u0026times; 10\u003csup\u003e12\u003c/sup\u003e C/cm\u003csup\u003e2\u003c/sup\u003e and N\u003csub\u003eBio\u003c/sub\u003e = -1 \u0026times; 10\u003csup\u003e12\u003c/sup\u003e C/cm\u003csup\u003e2\u003c/sup\u003e. Among these, negatively charged biomolecules have the highest sensitivity of 6.953 x 10\u003csup\u003e11\u003c/sup\u003e, followed by positively charged biomolecules, while neutral biomolecules have the lowest. As k increases, the sensitivity of the negatively charged particles increases far more than that of the other two. This is because a greater k enhances gate control and increases the effect of biomolecular charges on channel conductance. Using high-k dielectrics in conjunction with charged biomolecules maximises the device\u0026rsquo;s sensitivity, whereas neutral biomolecules have the least effect on transfer characteristics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.1.5 Influence of Varied Concentrations of Negatively Charged Biomolecules\u003c/h2\u003e\u003cp\u003eIncreasing the concentration of negatively charged biomolecules near the channel significantly impacts the device's sensitivity by altering the energy band shape and the tunneling barrier. The presence of negative charges enhances the net negative charge density around the channel, causing the energy bands to bend and widening the tunneling barrier. This increased barrier more effectively inhibits carrier flow, leading to a stronger modulation of the channel current. Consequently, modest changes in biomolecule concentration result in substantial variations in current, thereby enhancing the sensor's ability to detect these biomolecules. The electrostatic interaction between the charges of the biomolecules and the channel produces a precise and amplified electrical response, further increasing the sensor's sensitivity as shown in Fig.\u0026nbsp;7(a) for the charge densities of -2\u0026times;10\u003csup\u003e11\u003c/sup\u003e C/m\u003csup\u003e2\u003c/sup\u003e, -4 \u0026times;10\u003csup\u003e11\u003c/sup\u003e C/m\u003csup\u003e2\u003c/sup\u003e, -6 \u0026times; 10\u003csup\u003e11\u003c/sup\u003e C/m\u003csup\u003e2\u003c/sup\u003e, -8\u0026times; 10\u003csup\u003e11\u003c/sup\u003e C/m\u003csup\u003e2\u003c/sup\u003e and \u0026minus;\u0026thinsp;1 \u0026times; 10\u003csup\u003e12\u003c/sup\u003e C/m\u003csup\u003e2\u003c/sup\u003e. Figure\u0026nbsp;7(b) depicts that by increasing the dielectric constant of negatively charged biomolecules near the channel improves gate capacitance and boosts electrostatic interaction with the channel. Improved coupling reduces the surface potential barrier at the channel, allowing current to be generated at lower gate voltages, resulting in a lower threshold voltage (V\u003csub\u003eth\u003c/sub\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Exploring Irregular Hybridization of Biomolecules in the Nanogap Cavity\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn order to examine the steric hindrance, we simulated four different non-uniform hybridization profiles in the Ge-SD DG TFET structure: increasing, decreasing, convex, and concave step profiles, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e(a)-(d). These configurations represent the partial filling of the nanocavity with a charge concentration of N\u003csub\u003eBio\u003c/sub\u003e = 1 \u0026times; 10\u0026sup1;\u0026sup2; C/cm⁻\u0026sup2;. Each nanocavity had a surface area of 279 nm\u0026sup2; and was separated into nine segments with different heights based on the hybridization profile. In the increasing profile, the segment heights steadily grew from 4.35 nm at the nanogap entry to 39.15 nm at the rear. In contrast, the decreasing profile exhibited a reverse trend. The concave profile arranged the segments evenly and sloped downwards toward the centre, while the convex profile mirrored this arrangement in an outward-facing arc. These structures allowed for the investigation of the spatial distribution of biomolecules\u0026rsquo; impact on sensitivity. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates the sensitivity of the Ge-SD DG TFET biosensor as a function of\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ek, with a positive N\u003csub\u003eBio\u003c/sub\u003e for four different nanocavity profiles of increasing, decreasing, convex, and concave. The sensitivity is plotted on a logarithmic scale, emphasising differences across configurations and k. For all nanocavity shapes, S increases with k; however, the extent and pattern of this increase differ significantly between cavity types. Convex cavity exhibits the highest sensitivity at all k values, reaching the maximum value of 10\u003csup\u003e6\u003c/sup\u003e at k\u0026thinsp;=\u0026thinsp;12. The concave cavity also shows a substantial sensitivity boost with k, especially for higher values, though less than the convex cavity. Both convex and concave profiles provide enhanced local electric fields and better interaction with biomolecules, significantly amplifying the device\u0026rsquo;s response to changes in dielectric constants and biomolecule concentration. Decreasing cavity demonstrates moderate sensitivity gains with increasing k, surpassing the increasing cavity, which yields the lowest sensitivity. Thus, careful nanocavity engineering combined with high-k materials can lead to optimal biosensor sensitivity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Selectivity\u003c/h2\u003e\u003cp\u003eSelectivity refers to the biosensor\u0026rsquo;s capacity to detect a specific target analyte in a complicated mixture that contains other compounds or potential interfering agents. It is one of the most important characteristics since it assures that the biosensor only reacts to the target biomolecule. The sensor surface, which is often coated with receptors, antibodies, or probe DNA strands, is chemically modified to ensure it specifically binds to the intended biomolecule. At the same time, non-target molecules bind very weakly. When the target biomolecule attaches, its electric charges influence the energy band structure near the channel, resulting in a measurable change in current. In contrast, non-target molecules either produce negligible charge effects or are repelled, which helps minimize false signals. Selectivity is expressed mathematically as [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:Selectivity\\:\\varDelta\\:S=\\frac{{S}_{Target\\:Biomolecule}}{{S}_{Non-Target\\:Biomolecule}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\text{T}\\text{a}\\text{r}\\text{g}\\text{e}\\text{t}\\:\\text{B}\\text{i}\\text{o}\\text{m}\\text{o}\\text{l}\\text{e}\\text{c}\\text{u}\\text{l}\\text{e}}\\)\u003c/span\u003e\u003c/span\u003e is the Sensitivity for Target Biomolecule and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\text{N}\\text{o}\\text{n}-\\text{T}\\text{a}\\text{r}\\text{g}\\text{e}\\text{t}\\:\\text{B}\\text{i}\\text{o}\\text{m}\\text{o}\\text{l}\\text{e}\\text{c}\\text{u}\\text{l}\\text{e}}\\)\u003c/span\u003e\u003c/span\u003e is the Sensitivity for Non-Target Biomolecule.\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e displays the variation of selectivity in a biosensor with the k for three different charge conditions of neutral, positively and negatively charged biomolecules while considering k\u0026thinsp;=\u0026thinsp;2.57 (Biotin) as the non-target biomolecule. As the dielectric constant increases from 3.57 to 12, selectivity rises for all biomolecule charges; however, the trend varies for each. Neutral biomolecules exhibit the maximum selectivity across all dielectric constants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt k\u0026thinsp;=\u0026thinsp;12(Gelatine) selectivity reaches a peak of almost 10\u003csup\u003e11,\u003c/sup\u003e indicating an increase of 93% compared to negatively charged. Negatively charged biomolecules have intermediate selectivity, growing progressively as the k rises but always below neutral. Selectivity is poor when k\u0026thinsp;=\u0026thinsp;3.57 and improves as k increases. Positively charged biomolecules have the lowest selectivity at each k value, but improve significantly as the dielectric constant increases. The findings indicate that, while higher dielectric constants improve selectivity for all biomolecule types due to greater gate control and less screening effects, neutral biomolecules optimise selectivity, whereas charged biomolecules decrease it. This could be owing to charge-induced modulation effects that reduce the differential response between target and non-target analytes, resulting in slightly reduced selectivity when compared to a neutral environment. High-k materials accentuate these distinctions, resulting in strong selectivity modification dependent on both the dielectric environment and the biomolecular charge state.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Analog Figure of Merits\u003c/h2\u003e\u003cp\u003eAnalog and RF Figures of Merit (FOMs) are significant performance criteria that reflect how well the device performs in analog and high-frequency applications, both of which are vital for sensing signals with high precision and speed. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e collectively illustrates that the dielectric constant affects the analogue/RF performance metrics of the Ge-SD DG TFET-based biosensor with a fixed N\u003csub\u003eBio\u003c/sub\u003e of 1 \u0026times; 10\u003csup\u003e12\u003c/sup\u003e C/cm\u0026sup2; and V\u003csub\u003eGS\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHigher values of transconductance(g\u003csub\u003em\u003c/sub\u003e) indicate stronger amplification of input signals, which is critical for high sensitivity in biosensors. Mathematically, transconductance is [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{g}_{m}=\\:\\frac{\\partial\\:{I}_{D}}{\\partial\\:{V}_{GS}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\partial\\:{\\text{I}}_{\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e= Drain current and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\partial\\:{\\text{V}}_{\\text{G}\\text{S}}\\)\u003c/span\u003e\u003c/span\u003e= Gate voltage.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e(a), g\u003csub\u003em\u003c/sub\u003e peaks sharply at lower V\u003csub\u003eGS\u003c/sub\u003e and higher values for larger k, while it is much lower and more spread out for smaller k values. Due to a higher k, the gate control improves, resulting in a stronger modulation of current through the channel. This leads to a higher g\u003csub\u003em\u003c/sub\u003e, indicating a more sensitive device to changes in V\u003csub\u003eGS\u003c/sub\u003e, and a sharper turn-on characteristic. Using (4) g\u003csub\u003em\u003c/sub\u003e for k\u0026thinsp;=\u0026thinsp;12 yields 0.13mS.\u003c/p\u003e\u003cp\u003eTotal gate capacitance (C\u003csub\u003egg\u003c/sub\u003e) is a crucial parameter that affects device switching speed, power consumption, and analog/RF performance. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e(b) depicts that C\u003csub\u003egg\u003c/sub\u003e increases with both V\u003csub\u003eGS\u003c/sub\u003e and k, but the growth saturates at higher gate voltages. Higher k results in an increased C\u003csub\u003egg\u003c/sub\u003e across all V\u003csub\u003eGS\u003c/sub\u003e, with k\u0026thinsp;=\u0026thinsp;12 achieving a maximum value of 1.55\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e F. The increase in k enhances the gate oxide capacitance, resulting in a higher total gate capacitance, which allows for greater charge control, which impacts switching speed and frequency response.\u003c/p\u003e\u003cp\u003eThe cutoff frequency (f\u003csub\u003eT\u003c/sub\u003e) is the frequency at which the current gain equals unity. A greater f\u003csub\u003eT\u003c/sub\u003e enables faster response times, which is beneficial for rapid biomolecule identification. The relationship is given as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{f}_{T}=\\frac{{g}_{m}}{2\\pi\\:{C}_{gg}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ef\u003csub\u003eT\u003c/sub\u003e shows a sharp peak of 2 THz for higher k values at k\u0026thinsp;=\u0026thinsp;12, coinciding with the region of high transconductance, and then decreases as V\u003csub\u003eGS\u003c/sub\u003e increases further, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e(c) Lower k values exhibit much lower f\u003csub\u003eT\u003c/sub\u003e throughout the V\u003csub\u003eGS\u003c/sub\u003e range. Even though C\u003csub\u003egg\u003c/sub\u003e increases for high k, the very large increase in g\u003csub\u003em\u003c/sub\u003e dominates, boosting f\u003csub\u003eT\u003c/sub\u003e significantly. This means devices with higher k can operate at higher speeds (higher f\u003csub\u003eT\u003c/sub\u003e), making them suitable for fast and high-frequency biosensing applications.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Linearity Analysis\u003c/h2\u003e\u003cp\u003eLinearity analysis of a TFET-based biosensor is critical because it determines the degree to which the device can translate biomolecule-induced potential changes into electrical signals without distortion, which is required for accurate calibration, high dynamic range, and integration into analog/RF sensing circuits. Because of the steep band-to-band tunneling mechanism and ambipolar conduction, TFETs are intrinsically nonlinear, which causes harmonic distortion, intermodulation, and gain compression when the input signal or biomolecule-induced potential increases. To assess linearity, the device response is enlarged with higher-order transconductance terms and examined using harmonic distortion and intermodulation analysis. These approaches assess distortion and determine the ideal bias range in which the sensor provides high sensitivity while exhibiting low nonlinearity. Thus, linearity analysis not only ensures correct biomolecule detection but also directs design decisions\u0026mdash;such as biasing, structural engineering, and differential operation\u0026mdash;to achieve both sensitivity and signal integrity.\u003c/p\u003e\u003cp\u003eIn a TFET-based biosensor, the higher-order transconductance parameters g\u003csub\u003em2\u003c/sub\u003e and g\u003csub\u003em3\u003c/sub\u003e are crucial for understanding nonlinearity in device response.\u003c/p\u003e\u003cp\u003eThe second-order transconductance (g\u003csub\u003em2\u003c/sub\u003e) illustrates the small-signal gain variation with gate voltage and is primarily responsible for producing even-order nonlinearities. Ambipolar conduction in TFETs is a common source of these aberrations.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{g}_{m2}=\\:\\frac{{\\partial\\:}^{2}{I}_{D}}{{\\partial\\:}^{2}{V}_{GS}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThird-order transconductance (g\u003csub\u003em3\u003c/sub\u003e) represents the curvature of the transfer characteristic at a deeper level and governs odd-order nonlinearities and is expressed as:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{g}_{m3}=\\:\\frac{{\\partial\\:}^{3}{I}_{D}}{{\\partial\\:}^{3}{V}_{GS}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe second-order voltage intercept point (VIP2) is an important linearity parameter that specifies the input gate voltage amplitude at which the extrapolated second-order distortion products would become equivalent in magnitude to the fundamental output signal. VIP2 is articulated as: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:VIP2=4\\:\\times\\:\\:\\frac{{g}_{m}}{{g}_{m2}}\\)\u003c/span\u003e\u003c/span\u003e (8)\u003c/p\u003e\u003cp\u003eVIP3 (third-order voltage intercept point) quantifies the device\u0026rsquo;s resilience to intermodulation distortion by extrapolating the input voltage at which the third-order product matches the fundamental output and is given by: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:VIP3=\\:\\sqrt{\\left(24\\:\\times\\:\\frac{{g}_{m}}{{g}_{m3}}\\right)}\\)\u003c/span\u003e\u003c/span\u003e (9)\u003c/p\u003e\u003cp\u003eThe Third-Order Input Intercept Point (IIP3) serves as a critical metric in evaluating a device's performance, indicating the input signal level at which the third-order intermodulation distortion (IIM3) products match the amplitude of the fundamental signal when referred to the gate. This pivotal point can be precisely defined using the Taylor expansion of the device's transfer characteristic, underscoring its significance in assessing linearity and signal integrity. The expression is articulated as:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IIP3=\\:\\frac{2}{3}\\times\\:\\frac{{g}_{m}}{{g}_{m3}\\times\\:{R}_{s}}\\)\u003c/span\u003e\u003c/span\u003e where, R\u003csub\u003es\u003c/sub\u003e=50 Ω (10)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIMD3 refers to the third-order intermodulation distortion products generated when two or more signals pass through a nonlinear system and is given by:\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IMD3={\\left(\\frac{9}{2}\\times\\:{VIP3}^{3}\\times\\:{g}_{m3}\\right)}^{2}\\times\\:{R}_{s}\\)\u003c/span\u003e\u003c/span\u003e (11)\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e illustrates varying the surface charge density of immobilised biomolecules, i.e negatively charged, neutral and positively charged, affects the g\u003csub\u003em2\u003c/sub\u003e, g\u003csub\u003em3\u003c/sub\u003e, VIP2, VIP3, IIP3 and IMD3 as functions of gate voltage. All simulations assume a high-k dielectric of 12. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e(a) \u0026amp; (b) compares three charged biomolecules. It shows that negative N\u003csub\u003eBio\u003c/sub\u003e has a higher g\u003csub\u003em2\u003c/sub\u003e and g\u003csub\u003em3\u003c/sub\u003e value as the biomolecular surface charge critically modulates both the magnitude and bias dependence, which alter the electrostatic environment and charge transport characteristics. Higher VIP2 in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e(c) indicates more effective suppression of these distortions, allowing the device to accommodate bigger biomolecule-induced potentials or excitation signals without significantly reducing signal quality. It provides direct insight into the biosensor's ability to withstand even-order nonlinearities. It aids in the optimization of biasing and device structure, resulting in sensitivity and consistent linear performance regardless of charge. Higher VIP₃ significantly boosts the signal-to-interference ratio, allowing for the detection of the slightest biomolecular binding, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e(d). Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e(e) demonstrates that biomolecular surface charge shifts gate-voltage bias sites, causing third-order distortion peaks. It also affects linearity at threshold and strong inversion. Positive surface charge enhances IIP3 peaks but causes deeper nonlinearity around the threshold, while negative charge reduces threshold distortion but limits maximum dynamic range. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e(f) shows the IMD3 plateaus occurring between approximately \u0026ndash; 10 dBm and +\u0026thinsp;50 dBm across all surface-charge conditions. This indicates that while further increases in gate voltage may yield only modest increases in distortion, the behaviour varies based on the type of surface charge. Positive biomolecular charges show slightly higher IMD3 levels reaching up to +\u0026thinsp;50 dBm, indicating a more significant third-order nonlinearity under these conditions. In contrast, negative charges and neutral charges achieve similar plateau levels around +\u0026thinsp;40 dBm. Operating within this range maximizes sensitivity but also results in considerable spurious distortion\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Benchmarking with other TFET Biosensors:\u003c/h2\u003e\u003cp\u003eThe proposed device is compared to previously described TFET biosensors, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The main factors considered in this comparison are the on-current, leakage current, current ratio, and drain current sensitivity. The Ge-SE DG TFET demonstrates a high I\u003csub\u003eON\u003c/sub\u003e of 3.76 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e A/\u0026micro;m compared to the other TFET biosensors. Although the I\u003csub\u003eON\u003c/sub\u003e/I\u003csub\u003eOFF\u003c/sub\u003e ratio of the proposed device is not the highest, it exhibits the best drain current sensitivity of 6.95 x 10\u003csup\u003e11\u003c/sup\u003e among existing TFET-based biosensors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative analysis between Ge-SE DG TFET and reported TFET Biosensors.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS.no\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDevice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI\u003csub\u003eON\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(A/\u0026micro;m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI\u003csub\u003eOFF\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(A/\u0026micro;m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eI\u003csub\u003eON\u003c/sub\u003e/I\u003csub\u003eOFF\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHM-SE-TFET [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e6.87 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHV-TFET [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.67 \u0026times; 10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e3.61 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHSS-TFET [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.04 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e2.48 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSiGe FE-VTFET [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02 \u0026times; 10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.65 \u0026times; 10\u003csup\u003e\u0026ndash;18\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.33 \u0026times; 10\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e1.08 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFE DGDM-JLTFET\u0026nbsp;[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.46 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.46 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.15 \u0026times; 10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e8.61 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSiGe DM TFET [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.947\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e1.548\u0026thinsp;\u0026times; 10\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDM-ED-JLTFET [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e5.58\u0026times; 10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eδ-doped Ge-S vTFET [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.44\u0026thinsp;\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.66 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.66\u0026thinsp;\u0026times; 10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e8.15\u0026thinsp;\u0026times; 10\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDLSVH-TFET [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e5.66\u0026thinsp;\u0026times; 10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDG- ES TFET [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u0026times; 10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e45 \u0026times; 10\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGe-SD DG TFET\u003c/p\u003e\u003cp\u003e(Proposed Work)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.76 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.57 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.459 \u0026times; 10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e\u003cp\u003e6.95\u0026times; 10\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis work presents a comprehensive investigation of a dielectrically modulated Germanium Source-Extended Double Gate TFET (Ge-SE DG TFET) biosensor. The study reveals that the device\u0026rsquo;s extended source structure effectively enlarges the tunneling junction area, creating additional paths for BTBT. This enhances the tunneling probability, resulting in a high I\u003csub\u003eON\u003c/sub\u003e​ of 3.76\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e A/\u0026micro;m along with a SS of 49mV/dec. The biosensing capabilities of the proposed device are analyzed in terms of its sensitivity and selectivity for neutral and charged biomolecules. The results show a higher sensitivity to charged biomolecules, with negatively charged biomolecules exhibiting 58% higher sensitivity than neutral biomolecules. Furthermore, the device demonstrates strong selectivity, particularly for neutral biomolecules. At k\u0026thinsp;=\u0026thinsp;12, neutral biomolecules show 93% higher selectivity than charged ones, indicating the biosensor\u0026rsquo;s ability to differentiate between different biomolecules based on their electrical properties. The Ge-SE DG TFET also exhibits increased analog FOM values of 12 mS (g\u003csub\u003em\u003c/sub\u003e) and 2 THz (f\u003csub\u003eT\u003c/sub\u003e). The linearity analysis showcases that positively charged biomolecules have better linearity (VIP3) as well as high sensitivity sensing (IMD3) than neutral and negatively charged biomolecules. On benchmarking with the recently reported TFET structures, although the proposed device has a lower current ratio, it exhibits the best drain current sensitivity of 6.95 x 10\u003csup\u003e11\u003c/sup\u003e. Hence, the dielectrically modulated Ge-SE DG TFET biosensor is a promising next-generation biosensor technology capable of meeting the demands of high-sensitivity, high-speed, and low-power biological and medical applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Sneha M Joseph: Conceptualization, Formal analysis, Methodology, writing original draft, Editing.\u0026nbsp;Nameirakpam Premjit Singh: Formal analysis and K. Vanlalawmpuia: problem formulation, Supervision and reviewing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval:\u003c/strong\u003e The Authors approve that the submitted work is original and has not been published elsewhere in any form or language (partially or in full).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e All authors voluntarily agree to participate in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e All authors permit the Journal to publish this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYu Y, Nyein HYY, Gao W, Javey A (2020) Flexible electrochemical bioelectronics: the rise of in situ bioanalysis. 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J Electron Mater 53:661\u0026ndash;673. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11664-023-10848-2\u003c/span\u003e\u003cspan address=\"10.1007/s11664-023-10848-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTalukdar J, Rawat G, Mummaneni K (2021) Dielectrically Modulated Single and Double Gate Tunnel FET Based Biosensors for Enhanced Sensitivity, in IEEE Sensors Journal, vol. 21, no. 23, pp. 26566\u0026ndash;26573, 1 Dec.1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/JSEN.2021.3122582\u003c/span\u003e\u003cspan address=\"10.1109/JSEN.2021.3122582\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"silicon","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scon","sideBox":"Learn more about [Silicon](https://www.springer.com/journal/12633)","snPcode":"12633","submissionUrl":"https://submission.nature.com/new-submission/12633/3","title":"Silicon","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Source-extended, Label-free biosensing, Sensitivity, Selectivity, FOMs, Linearity","lastPublishedDoi":"10.21203/rs.3.rs-8044117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8044117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the potential of a dielectrically-modulated Germanium source-extended double gate tunnel field-effect transistor (Ge-SE DG TFET) as a highly effective biosensor for the detection of vital biomolecules, including vitamins, proteins, and amino acids. We thoroughly analyze the device\u0026rsquo;s linear characteristics, sensitivity, and selectivity, along with its analog figure of merits (FOMs). The results indicate that negatively charged biomolecules have 58% greater sensitivity (S) than neutral ones, while neutral biomolecules show 93% better selectivity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:S\\)\u003c/span\u003e\u003c/span\u003e) than the negatively charged ones. Self-heating effects (SHE) at 310 K are effectively mitigated through the use of a low bandgap Germanium source and an extended source, which reduces power density and improves heat dissipation. The proposed device is benchmarked with recent reported TFET biosensors and showcases better overall performance metrics, with a transconductance (g\u003csub\u003em\u003c/sub\u003e) of 12 mS, a very high cut-off frequency (f\u003csub\u003eT\u003c/sub\u003e) of 2 THz, and S of 6.95 \u0026times; 10\u003csup\u003e11\u003c/sup\u003e. These results highlight the Ge-SE DG TFET\u0026rsquo;s feasibility for incorporation into low-power, high-speed biosensing systems for next-generation biosensing applications.\u003c/p\u003e","manuscriptTitle":"High Performance Dielectrically Modulated Germanium Source Extended Double Gate Tunnel Field Effect Transistor for Biosensing Applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 10:58:15","doi":"10.21203/rs.3.rs-8044117/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-08T01:43:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T18:26:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308232045516320148294292855478833840502","date":"2025-12-09T17:56:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T10:16:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-10T08:27:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-10T08:26:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Silicon","date":"2025-11-06T05:50:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"silicon","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scon","sideBox":"Learn more about [Silicon](https://www.springer.com/journal/12633)","snPcode":"12633","submissionUrl":"https://submission.nature.com/new-submission/12633/3","title":"Silicon","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e9c8fec8-1c77-4428-a279-cec908b62a8a","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-16T08:53:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 10:58:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8044117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8044117","identity":"rs-8044117","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}