Deep learning and superoscillatory speckles empowered multimode fiber probe for in situ nano-displacement detection and micro-imaging

Deep learning and superoscillatory speckles empowered multimode fiber probe for in situ nano-displacement detection and micro-imaging | 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 Deep learning and superoscillatory speckles empowered multimode fiber probe for in situ nano-displacement detection and micro-imaging Qirong Xiao, Lele Wang, Yiwei Zhang, Yibing Zhou, Yuan Meng, Zhengyang Lu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5922891/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract High-precision metrology has laid the foundation for semiconductor fabrication and life sciences. However, existing displacement measurement approaches are incapable of performing flexible probing within complex equipment interiors. Here, we present a novel in situ, non-contact nano-displacement measurement approach. Leveraging a multimode fiber probe empowered by deep learning, fine feature information can be efficiently extracted from superoscillatory speckles, achieving single-ended detection with 10 nm resolution and 99.95% accuracy. A physical model is established to correlate the displacement with higher-order modes proportion in the fiber. Sub-millimeter-sized probe enables detecting targets with different structures in confined spaces. Robust recognition is achieved through joint learning, under varying fiber bending conditions and different metal materials. With extreme compression ratios of less than 0.1%, the system delivers high accuracy, low training costs, and high speed processing. The imaging capability of the probe is also experimentally validated, proving great potential as a powerful tool in applications such as lithography, weak force sensing, and super-resolution micro-endoscopy. Physical sciences/Nanoscience and technology/Techniques and instrumentation Physical sciences/Optics and photonics/Optical physics Physical sciences/Physics/Techniques and instrumentation/Imaging techniques Physical sciences/Nanoscience and technology/Other nanotechnology/Nanometrology Physical sciences/Nanoscience and technology/Other nanotechnology/Computational nanotechnology Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplymentaryMovieS1XXXMultimodefiberoutputspecklefieldvariesinrealtimewithwaferdisplacement.mp4 Supplymentary Movie S1: Multimode fiber output speckle field varies in real time with wafer displacement SupplymentaryMovieS2XXXFlowofDisplacementDetectionandImageReconstructionUsingDITML.mp4 Supplymentary Movie S2: Flow of Displacement Detection and Image Reconstruction Using DITML Supplementaryinformation.docx Supplementary information for Deep learning and superoscillatory speckles empowered multimode fiber probe for in situ nano-displacement detection and micro-imaging Cite Share Download PDF Status: Published Journal Publication published 05 Jan, 2026 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. 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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-5922891","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":412693739,"identity":"a31fc2bd-03f1-4a27-bc20-5f909c44b602","order_by":0,"name":"Qirong 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