In-sensor image memorization, low-level processing, and high-level computing by using above-bandgap photovoltages

In-sensor image memorization, low-level processing, and high-level computing by using above-bandgap photovoltages | 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 In-sensor image memorization, low-level processing, and high-level computing by using above-bandgap photovoltages Zhen Fan, Kun Liu, Shan Tan, Haipeng Lin, Ou Jiali, Haoyue Deng, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5939557/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract In-sensor computing holds great promise for ultrafast and energy-efficient machine vision. However, the development of a versatile in-sensor computing system that can integrate image memorization, low-level processing, and high-level computing functions remains a challenge, primarily due to the limited availability of photosensors that can offer both dynamic photoresponse and programmable photoresponsivity. Here, we demonstrate the integration of these multi-functions into a ferroelectric photosensor (FE-PS)-based array. Thanks to its unique photovoltaic mechanism known as the bulk photovoltaic effect, the FE-PS exhibits above-bandgap, dynamically responding, and electrically switchable photovoltages. By using the dynamic photovoltage response, the FE-PS-based array is capable of memorizing and pre-processing images, with the ability to adjust the memory and pre-processing effects by ferroelectric polarization. On the other hand, the electrically switchable photovoltages featuring multi-level switchability and retrievability enable the FE-PS-based array to perform in-sensor high-level computing, achieving 100% accuracy in a 4-class image recognition task. Notably, the high precision and reliability of the photovoltage-based image memorization and processing greatly benefit from the high photovoltages produced by the FE-PS. This study represents a significant step towards developing versatile in-sensor computing systems that could be utilized across a wide range of machine vision scenarios. Physical sciences/Materials science/Materials for devices/Information storage Physical sciences/Materials science/Condensed-matter physics/Ferroelectrics and multiferroics Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Nature Communications → 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-5939557","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":416521530,"identity":"f1060a5e-835c-4e2a-a016-fde06e980e55","order_by":0,"name":"Zhen 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