Digital Twin Platform for Accelerating Optimization of Oxide Semiconductor Transistors to Overcome Fundamental Performance-Reliability Trade-off

Digital Twin Platform for Accelerating Optimization of Oxide Semiconductor Transistors to Overcome Fundamental Performance-Reliability Trade-off | 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 Digital Twin Platform for Accelerating Optimization of Oxide Semiconductor Transistors to Overcome Fundamental Performance-Reliability Trade-off Tanvir Haider Pantha, Minjong Lee, Kharanshu Bhojak, Thi T. Huong Chu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8524772/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Amorphous oxide semiconductor (AOS) transistors are promising candidates for nextgeneration logic and high-density memory technologies, particularly for back-end-of-line– compatible monolithic three-dimensional integration. However, their widespread adoption is limited by a persistent trade-off between device performance and long-term reliability, the optimization of which conventionally requires exhaustive exploration of high-dimensional material, process, and device parameter spaces. Here, we introduce a closed-loop digital twin framework that accelerates AOS transistor optimization by integrating device fabrication, electrical characterization, circuit simulation, surrogate modeling and Bayesian active learning. Gaussian process–based surrogate models are used to link low-level fabrication parameters to device- and application-level performance and reliability metrics, enabling data-efficient, multi-objective optimization across the technology stack. By actively prioritizing the most informative experiments, the framework achieves over 3x reduction in experimental effort compared with exhaustive search. We demonstrate the approach using top-gated IGZO transistors spanning 80 gate oxide process splits and extend the optimization beyond device metrics to application-aware DRAM performance. This digital twin paradigm provides a scalable pathway to systematically navigate performance–reliability trade-offs and accelerate the development of emerging semiconductor technologies. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Nanoscience and technology/Nanoscale devices/Electronic devices Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformationfinal.pdf Digital Twin Platform for Accelerating Optimization of Oxide Semiconductor Transistors to Overcome Fundamental Performance-Reliability Trade-off-Supplementary Cite Share Download PDF Status: Under Review 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-8524772","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":585088224,"identity":"62198de2-ba88-4ff3-82a6-bb3e5dba0e15","order_by":0,"name":"Tanvir Haider Pantha","email":"data:image/png;base64,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","orcid":"","institution":"The University of Texas Dallas","correspondingAuthor":true,"prefix":"","firstName":"Tanvir","middleName":"Haider","lastName":"Pantha","suffix":""},{"id":585088225,"identity":"866aebbc-444c-440a-9300-d07de45d118a","order_by":1,"name":"Minjong Lee","email":"","orcid":"","institution":"The University of Texas at Dallas","correspondingAuthor":false,"prefix":"","firstName":"Minjong","middleName":"","lastName":"Lee","suffix":""},{"id":585088226,"identity":"54c4133a-9773-43e0-91f1-55e4ffbfb94d","order_by":2,"name":"Kharanshu Bhojak","email":"","orcid":"","institution":"The University of Texas at Dallas","correspondingAuthor":false,"prefix":"","firstName":"Kharanshu","middleName":"","lastName":"Bhojak","suffix":""},{"id":585088227,"identity":"904dba87-b379-4e4a-9be8-56f081342cae","order_by":3,"name":"Thi T. 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