An autonomous adaptation vision chip with in-sensor neural network based on multifunctional phototransistor

An autonomous adaptation vision chip with in-sensor neural network based on multifunctional phototransistor | 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 An autonomous adaptation vision chip with in-sensor neural network based on multifunctional phototransistor Peng Huang, Haozhang Yang, Nan Tang, Xu Yan, Yi Xiao, Ruiqi Chen, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8435228/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 Natural light intensity spans over ten orders of magnitude, from faint starlight to bright midday sunlight. However, the widely used image sensors exhibit fixed photoresponse, leading to poor image quality under varying illumination conditions. Here, we report a scalable vision chip with in-sensor neural network based on 22-nm fully depleted silicon-on-insulator (FDSOI) multi-terminal multifunctional phototransistor, enabling environment-driven autonomous adaptation imaging. The proposed multi-terminal structure of the FDSOI phototransistor allows electrical manipulation of photocarrier distribution, yielding electrically controllable photoresponses. Leveraging the intrinsic memory properties of the FDSOI phototransistor, we further implement a compute-in-memory feedback neural network within the sensor that infers ambient illumination directly from captured images and continuously reconfigures the pixel response in real time. Experimental results demonstrate that the fabricated chip can enhance scene contrast and improve traffic sign recognition accuracy by ~20% in varying illumination conditions compared to multi-sensor fixed-photoresponse methods. Fabricated using scalable silicon technology, our work presents a general strategy for achieving autonomous adaptation vision systems that deliver robust performance in dynamic real-world environments. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Nanoscience and technology/Nanoscale devices/Electronic devices Physical sciences/Nanoscience and technology/Nanoscale devices/Sensors Full Text Additional Declarations There is NO Competing Interest. Supplementary Files AutonomousadaptationvisionchipSI.docx An autonomous adaptation vision chip with in-sensor neural network based on multifunctional phototransistor 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-8435228","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":576518180,"identity":"69006ab6-3619-40fe-804b-4855e7511f6e","order_by":0,"name":"Peng 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However, the widely used image sensors exhibit fixed photoresponse, leading to poor image quality under varying illumination conditions. Here, we report a scalable vision chip with in-sensor neural network based on 22-nm fully depleted silicon-on-insulator (FDSOI) multi-terminal multifunctional phototransistor, enabling environment-driven autonomous adaptation imaging. The proposed multi-terminal structure of the FDSOI phototransistor allows electrical manipulation of photocarrier distribution, yielding electrically controllable photoresponses. Leveraging the intrinsic memory properties of the FDSOI phototransistor, we further implement a compute-in-memory feedback neural network within the sensor that infers ambient illumination directly from captured images and continuously reconfigures the pixel response in real time. 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