Piezoelectric neuron for neuromorphic computing

Piezoelectric neuron for neuromorphic computing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Piezoelectric neuron for neuromorphic computing Zhen Fan, Wenjie Li, Tan Shan, Zhiwei Chen, Ou Jiali, Liu Kun, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3989574/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Neuromorphic computing has attracted great attention for its massive parallelism and high energy efficiency. As the fundamental components of neuromorphic computing systems, artificial neurons play a key role in information processing. However, the development of artificial neurons that can simultaneously incorporate low hardware overhead, high reliability, high speed, and low energy consumption remains a challenge. To address this challenge, we propose and demonstrate a piezoelectric neuron with a simple circuit structure, consisting of a piezoelectric cantilever, a parallel capacitor, and a series resistor. It operates through the synergy between the converse piezoelectric effect and the capacitive charging/discharging. Thanks to this efficient and robust mechanism, the piezoelectric neuron not only implements critical leaky integrate-and-fire functions (including leaky integration, threshold-driven spiking, all-or-nothing response, refractory period, strength-modulated firing frequency, and spatiotemporal integration), but also demonstrates small cycle-to-cycle and device-to-device variations (~1.9% and ~10.0%, respectively), high endurance (10 7 ), high speed (integration/firing: ~9.6/~0.4 μs), and low energy consumption (~13.4 nJ/spike). Furthermore, spiking neural networks based on piezoelectric neurons are constructed, showing capabilities to implement both supervised and unsupervised learning. This study therefore demonstrates the piezoelectric neuron as a simple yet reliable, fast, and energy-efficient artificial neuron, and also showcases its applicability in neuromorphic computing. Physical sciences/Materials science/Materials for devices/Information storage Physical sciences/Materials science/Materials for devices/Electronic devices Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3989574","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":275182872,"identity":"72acd1c5-81bd-41d1-8f8c-918e45522786","order_by":0,"name":"Zhen 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As the fundamental components of neuromorphic computing systems, artificial neurons play a key role in information processing. However, the development of artificial neurons that can simultaneously incorporate low hardware overhead, high reliability, high speed, and low energy consumption remains a challenge. To address this challenge, we propose and demonstrate a piezoelectric neuron with a simple circuit structure, consisting of a piezoelectric cantilever, a parallel capacitor, and a series resistor. It operates through the synergy between the converse piezoelectric effect and the capacitive charging/discharging. Thanks to this efficient and robust mechanism, the piezoelectric neuron not only implements critical leaky integrate-and-fire functions (including leaky integration, threshold-driven spiking, all-or-nothing response, refractory period, strength-modulated firing frequency, and spatiotemporal integration), but also demonstrates small cycle-to-cycle and device-to-device variations (~1.9% and ~10.0%, respectively), high endurance (10\u003csup\u003e7\u003c/sup\u003e), high speed (integration/firing: ~9.6/~0.4 μs), and low energy consumption (~13.4 nJ/spike). Furthermore, spiking neural networks based on piezoelectric neurons are constructed, showing capabilities to implement both supervised and unsupervised learning. 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