A semi-empirical approach to calibrate simulation models for semiconductor devices

A semi-empirical approach to calibrate simulation models for semiconductor 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 A semi-empirical approach to calibrate simulation models for semiconductor devices Rahul Jaiswal, Manel Ramon-Martinez, Tito Busani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2701878/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jun, 2023 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Semiconductor device optimization using computer-based prototyping techniques like simulation or machine learning digital twins can be time and resource efficient compared to the conventional strategy of iterating over device design variations by fabricating the actual device. However, simulation models require perfect calibration of material parameters for the model to represent a particular semiconductor device. This cali- bration process itself can require characterization information of the device and its precursors and extensive expert knowledge of non char- acterizable parameters and their tuning. We propose a hybrid method to calibrate multiple simulation models for a device using minimal characterization data and machine learning-based prediction models. A photovoltaic device is chosen as the example for this technique where optical and electrical simulation models of an industrially manufactured silicon solar cell are calibrated and the simulated device performance is compared with the measurement data from the physical device. Physical sciences/Energy science and technology/Renewable energy/Solar energy Physical sciences/Mathematics and computing/Scientific data Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Computational science Machine learning TCAD Gaussian process regression photovoltaics silicon heterojunction solar cells Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Jun, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 25 Apr, 2023 Reviews received at journal 24 Apr, 2023 Reviewers agreed at journal 13 Apr, 2023 Reviews received at journal 03 Apr, 2023 Reviewers agreed at journal 28 Mar, 2023 Reviewers invited by journal 28 Mar, 2023 Editor assigned by journal 26 Mar, 2023 Editor invited by journal 26 Mar, 2023 Submission checks completed at journal 26 Mar, 2023 First submitted to journal 16 Mar, 2023 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. 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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-2701878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":186552975,"identity":"dbaed287-1111-4b93-912d-1c36ae206c30","order_by":0,"name":"Rahul Jaiswal","email":"","orcid":"","institution":"University of New Mexico","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rahul","middleName":"","lastName":"Jaiswal","suffix":""},{"id":186552976,"identity":"f847bf9e-1d48-4a41-9070-5f460bf0fac3","order_by":1,"name":"Manel Ramon-Martinez","email":"","orcid":"","institution":"University of New Mexico","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Manel","middleName":"","lastName":"Ramon-Martinez","suffix":""},{"id":186552977,"identity":"24229e74-1590-499c-9cd2-40bb36c215f5","order_by":2,"name":"Tito Busani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYBACAygpB+GyEa/FwJhULQwGiQ1EazGXSH/4uaDgT/qG290JDB/KDhPWYjkjx1h6hoFB7oY7ZzcwzjhHhBaDGzkM0jwgLTdyNzDzthGlJf3xb6CWdAOQlr/EaUkwA9mSANbCSIwWy543ZtY8BsaGM4FaDvacSyesxZw9/fFtnj9y8nw3cjc++FFmTVgLCjhAovpRMApGwSgYBbgAAJ2aOdbgDwxoAAAAAElFTkSuQmCC","orcid":"","institution":"University of New Mexico","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tito","middleName":"","lastName":"Busani","suffix":""}],"badges":[],"createdAt":"2023-03-16 18:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2701878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2701878/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-36196-z","type":"published","date":"2023-06-27T21:28:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":44733949,"identity":"db86c647-1b8f-4529-9544-4248761ea0be","added_by":"auto","created_at":"2023-10-16 22:12:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":380228,"visible":true,"origin":"","legend":"","description":"","filename":"NatureSLmanuscriptAsemiempiricalapproachtocalibrate....rev1publish.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2701878/v1_covered_8b0e96de-80f4-4ef4-bbae-961970ddd890.pdf"},{"id":34898410,"identity":"48ab5ac1-ce18-4411-9e66-e6b626488004","added_by":"auto","created_at":"2023-03-28 05:16:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":63665,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2701878/v1/618c98c21e9ad2230a1a1007.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A semi-empirical approach to calibrate simulation models for semiconductor devices","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Machine learning, TCAD, Gaussian process regression, photovoltaics, silicon heterojunction solar cells","lastPublishedDoi":"10.21203/rs.3.rs-2701878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2701878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Semiconductor device optimization using computer-based prototyping techniques like simulation or machine learning digital twins can be time and resource efficient compared to the conventional strategy of iterating over device design variations by fabricating the actual device. 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