Thermally controllable diffractive processor using hybrid metasurfaces

Thermally controllable diffractive processor using hybrid metasurfaces | 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 Thermally controllable diffractive processor using hybrid metasurfaces Xiaofei Zang, Guannan Wang, Xinyu Huang, Teng Zhang, Zhiyu Tan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8811837/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract All-optical diffractive neural networks (DNNs) have garnered notable attention in artificial intelligence because of their unprecedented ability to perform complex tasks at the speed of light. However, current DNNs designs mainly rely on non-tunable diffractive components with fixed functionality. Here, to introduce active diffraction units into DNNs at terahertz (THz) spectrum, we report a hybrid metasurface-based thermally controllable diffractive processor that mimics task-switchable DNNs using thermally reconfigurable meta-neurons composed of vanadium dioxide (VO 2 ) resonators and gold resonators. The optical response of the network layer of this processer can be dynamically switched based on ambient temperature to meet dynamic task requirements. We experimentally validate the efficacy of the processer through classification tasks involving single and double handwritten digits as well as fashion products. Moreover, we propose an information encryption framework that leverages ambient temperatures as decryption keys. This temperature-dependent all-optical processor with multiplexing capability paves the way for the next-generation high-speed optical computing, dynamic optical systems, and secure communications. Physical sciences/Optics and photonics Physical sciences/Optics and photonics/Optical techniques Full Text Additional Declarations There is no conflict of interest Supplementary Files Supplementary20260207.docx Thermally controllable diffractive processor using hybrid metasurfaces Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 26 Mar, 2026 Review # 3 received at journal 17 Mar, 2026 Review # 2 received at journal 16 Mar, 2026 Review # 1 received at journal 15 Mar, 2026 Reviewer # 3 agreed at journal 05 Mar, 2026 Reviewer # 2 agreed at journal 04 Mar, 2026 Reviewer # 1 agreed at journal 04 Mar, 2026 Reviewers invited by journal 04 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 03 Mar, 2026 Unknown event 12 Feb, 2026 Editor assigned by journal 06 Feb, 2026 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. 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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-8811837","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":600856623,"identity":"01872be0-b2f1-49f3-b14b-6f034d7a1057","order_by":0,"name":"Xiaofei 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