A Low-Power CMOS Spiking Neuron Design with Frequency Adaptation in 180nm Technology at 300mV Supply

A Low-Power CMOS Spiking Neuron Design with Frequency Adaptation in 180nm Technology at 300mV Supply | 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 Research Article A Low-Power CMOS Spiking Neuron Design with Frequency Adaptation in 180nm Technology at 300mV Supply Mehdi Haji Rahimi, Shaban Rezaei Borjlu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8064967/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract This paper presents a low-power spiking neuron design using 180 nm CMOS technology, operating at a 300 mV supply with a frequency adaptation mechanism. The design achieves an energy consumption of 9.3 pJ/spike, a 22.5% improvement over the baseline model (12 pJ/spike), a stable 255 Hz firing frequency, and a 70% increase in spike amplitude through a P-type transistor in the feedback loop. Simulations in ADS confirm the design’s stability, though the lack of hardware validation remains a limitation. Compared to advanced nodes (e.g., 28 nm), it offers a cost-effective solution for IoT and robotics applications, with potential memristor integration enhancing synaptic flexibility. Future work includes hardware validation and scalability improvements. Spiking neuron CMOS technology frequency adaptation neuromorphic computing low-power subthreshold operation spiking neural networks low-voltage design Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 Jan, 2026 Reviews received at journal 01 Jan, 2026 Reviews received at journal 02 Dec, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviewers invited by journal 20 Nov, 2025 Editor assigned by journal 10 Nov, 2025 Submission checks completed at journal 10 Nov, 2025 First submitted to journal 08 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. 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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-8064967","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549962966,"identity":"9517a901-9971-4b4d-972c-6edf0dc081d9","order_by":0,"name":"Mehdi Haji Rahimi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"Haji","lastName":"Rahimi","suffix":""},{"id":549962967,"identity":"d39fe20d-d551-497a-9867-9893ec01ef6a","order_by":1,"name":"Shaban Rezaei 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