Highly Accurate, Efficient, and Fabrication Tolerance-aware Nanostructure Prediction for High-Performance Optoelectronic Devices

Highly Accurate, Efficient, and Fabrication Tolerance-aware Nanostructure Prediction for High-Performance Optoelectronic Devices | 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 Highly Accurate, Efficient, and Fabrication Tolerance-aware Nanostructure Prediction for High-Performance Optoelectronic Devices Min-Ho Seo, Won-Kyeong Jeong, Ki-Hoon Kim, Chaehyun Park, Myungkwan Song This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3622565/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 Despite extensive efforts to predict optimal nanostructures for enhancing optical devices, a more accurate, efficient, and practical method for nanostructure optimisation is required. In particular, fabrication tolerance is a promising avenue for significantly improving manufacturing efficiency; however, research in this area is limited. In this study, we introduce a practical approach for enhancing the performance of optoelectronic devices using an artificial intelligence (AI)-based nanostructure optimisation strategy. We optimised a support vector regression (SVR) model to capture the complex and nonlinear relationships between the transmittance and nanograting structure variables with the goal of improving optoelectronic devices. Our versatile model accurately predicted the continuous transmittance data with high precision (R2=0.995) using only 216 training data points. It can also make predictions under untrained conditions, thereby enabling the creation of a transmittance nanostructure contour map (R2=0.949). This method facilitates the design of nanostructures tailored to specific optical properties and provides valuable insights into fabrication tolerance. Through experimental validation, we identified an optimal nanograting structure with the highest transmittance in the visible-light spectrum. When integrated into optoelectronic devices such as organic light-emitting diodes (OLEDs) and organic solar cells (OSCs), their performance is significantly improved by increasing the light transmittance. Specifically, devices using the fabricated nanograting film exhibited a 17% improvement in external quantum efficiency (EQE) for solution-processed organic light-emitting diodes (SP-OLEDs) and a 10.7% improvement in power-conversion efficiency (PCE) for OSCs. Physical sciences/Nanoscience and technology/Nanoscale materials/Structural properties Physical sciences/Optics and photonics/Optical materials and structures/Nanowires Nanostructure machine learning support vector regression (SVR) organic light-emitting diodes (OLEDs) organic solar cells (OSCs) Full Text Additional Declarations (Not answered) Supplementary Files WKJSIFinal.pdf 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. 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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-3622565","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":266638115,"identity":"4f928890-c858-4d38-aadc-e94941f2830f","order_by":0,"name":"Min-Ho 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