Design optimization of semiconductor manufacturing equipment using a novel multi-fidelity surrogate modeling approach

Design optimization of semiconductor manufacturing equipment using a novel multi-fidelity surrogate modeling approach | 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 Research Article Design optimization of semiconductor manufacturing equipment using a novel multi-fidelity surrogate modeling approach Bingran Wang, Min Sung Kim, Taewoong Yoon, Dasom Lee, Byeong-Sang Kim, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5365259/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Careful design of semiconductor manufacturing equipment is crucial for ensuring the performance, yield, and reliability of semiconductor devices. Despite this, numerical optimization methods are seldom applied to optimize the design of such equipment due to the difficulty of obtaining accurate simulation models. In this paper, we address a practical and industrially relevant electrostatic chuck (ESC) design optimization problem by proposing a novel multi-fidelity surrogate modeling approach. The optimization aims to improve the temperature uniformity of the wafer during the etching process by adjusting seven parameters associated with the coolant path and embossing. Our approach combines low-fidelity (LF) and high-fidelity (HF) simulation data to efficiently predict spatial-field quantities, even with a limited number of data points. We use proper orthogonal decomposition (POD) to project the spatially interpolated HF and LF field data onto a shared latent space, followed by the construction of a multi-fidelity kriging model to predict the latent variables of the HF output field. In the ESC design problem, with hundreds or fewer data, our approach achieves a more than 10% reduction in prediction error compared to using kriging models with only HF or LF data. Additionally, in the ESC optimization problem, our proposed method yields better solutions with improvements in all of the quantities of interest, while requiring 20% less data generation cost compared to the HF surrogate modeling approach. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 May, 2025 Reviews received at journal 14 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviews received at journal 27 Apr, 2025 Reviewers agreed at journal 11 Dec, 2024 Reviewers invited by journal 09 Dec, 2024 Editor assigned by journal 05 Nov, 2024 Submission checks completed at journal 05 Nov, 2024 First submitted to journal 31 Oct, 2024 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-5365259","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374642244,"identity":"4dbb94ec-fa9f-41be-8438-c37e679b0eef","order_by":0,"name":"Bingran Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACA/YGEGUDZjMwsBGjhecAiEojRYuEA4g6TIIWcwkew88Fv84n9vcf3sDwoewwYS2Ws3uMpWf23U6ccSOtgHHGOSK0GNw5u0Gat+d24gYJHgNm3jZitNzI3fybt+dc4gb+MwbMf4nUsk2a58eBxA0MOQbMjMRosew5/82atyHZGOSXgz3n0glrMWdvS77N88dOFhhiGx/8KLMmrAUMGNsg9AEi1YPAHxLUjoJRMApGwcgDAAMxP9MafRHaAAAAAElFTkSuQmCC","orcid":"","institution":"University of California, San Diego","correspondingAuthor":true,"prefix":"","firstName":"Bingran","middleName":"","lastName":"Wang","suffix":""},{"id":374642248,"identity":"a51791fb-45ca-46d6-9025-e0f740be7be0","order_by":1,"name":"Min Sung Kim","email":"","orcid":"","institution":"Samsung (South Korea)","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"Sung","lastName":"Kim","suffix":""},{"id":374642249,"identity":"a1cee1b1-4ac5-492b-a72c-c208a3c252ff","order_by":2,"name":"Taewoong Yoon","email":"","orcid":"","institution":"Samsung (South Korea)","correspondingAuthor":false,"prefix":"","firstName":"Taewoong","middleName":"","lastName":"Yoon","suffix":""},{"id":374642250,"identity":"0f1d0528-5d79-4e2c-880e-dfe785e65ccd","order_by":3,"name":"Dasom Lee","email":"","orcid":"","institution":"Samsung (South Korea)","correspondingAuthor":false,"prefix":"","firstName":"Dasom","middleName":"","lastName":"Lee","suffix":""},{"id":374642251,"identity":"279239af-3988-45f9-9733-ff5ca93196cb","order_by":4,"name":"Byeong-Sang Kim","email":"","orcid":"","institution":"Samsung (South Korea)","correspondingAuthor":false,"prefix":"","firstName":"Byeong-Sang","middleName":"","lastName":"Kim","suffix":""},{"id":374642252,"identity":"fd4af831-cca5-4cc4-9b96-c425f180a09d","order_by":5,"name":"Dougyong Sung","email":"","orcid":"","institution":"Samsung (South Korea)","correspondingAuthor":false,"prefix":"","firstName":"Dougyong","middleName":"","lastName":"Sung","suffix":""},{"id":374642253,"identity":"6d4955ca-d700-44e7-9d5d-04233356fcad","order_by":6,"name":"John T. 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