Physical Mechanisms and Applications of High-Durability Polymer- Based Artificial Synapses Using P3DT

Physical Mechanisms and Applications of High-Durability Polymer- Based Artificial Synapses Using P3DT | 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 Physical Mechanisms and Applications of High-Durability Polymer- Based Artificial Synapses Using P3DT Xiaoyu Hou, Junxin Liu, Liqing Liu, Hongguang Zhang, Mingdong Yi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8470525/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 Organic polymers are regarded as promising candidates for constructing low-cost and flexible neuromorphic computing hardware due to their designable structures, intrinsic flexibility, and compatibility with conventional solution-processing techniques. Such materials hold significant potential for overcoming the limitations of traditional silicon-based hardware in biomimetic intelligent systems. In this study, a highly stable and high-performance memristor was fabricated and characterized based on poly(3-decylthiophene) (P3DT). The device operates at a milliampere-level current, exhibiting good signal-driving capability and circuit compatibility. Electrical measurements confirm that the memristor can successfully emulate various essential synaptic behaviors, demonstrating its potential as a fundamental building block for neuromorphic computing. Furthermore, the device shows remarkable long-term stability, maintaining its resistive switching and conductance modulation characteristics after 150 days of storage in an unencapsulated environment. To validate its applicability, an artificial neural network (ANN) model was constructed based on the conductance modulation behavior of the device and applied to the Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition task. Without extensive optimization, the network achieved a recognition accuracy of 93.8%, effectively demonstrating the feasibility and efficiency of the device in simulating neuromorphic computation. This work provides a reliable device-level solution for the development of practical neuromorphic computing hardware by realizing a memristor that combines high stability with rich bio-inspired functionalities. Memristor P3DT Artificial neural network Array Artificial synapse Full Text Additional Declarations No competing interests reported. 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-8470525","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572678136,"identity":"891882f0-90f6-4d21-96e4-ade7dcd81b9d","order_by":0,"name":"Xiaoyu Hou","email":"","orcid":"","institution":"Nanjing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Hou","suffix":""},{"id":572678137,"identity":"2e115185-9315-4b99-948f-781bce17359c","order_by":1,"name":"Junxin Liu","email":"","orcid":"","institution":"Nanjing University of Posts and 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