Artificial Neural Network-driven Diffraction Imaging for Nanoscale Optical Critical Dimension Metrology in Semiconductor Manufacturing

Artificial Neural Network-driven Diffraction Imaging for Nanoscale Optical Critical Dimension Metrology in Semiconductor Manufacturing | 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 Artificial Neural Network-driven Diffraction Imaging for Nanoscale Optical Critical Dimension Metrology in Semiconductor Manufacturing Fu-Sheng Yang, Liang-Chia Chen, Chao-Ching Ho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7467039/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract This study introduces a new optical measurement technique that integrates artificial neural network (ANN) technology with coherent optical scatterometry and back focal plane image to address the limitations of conventional methods, such as scanning electron microscopy and atomic force microscopy (AFM). Designed for three-dimensional advanced semiconductor packaging applications, the proposed approach combines diffraction imaging with energy distribution in periodic structures to train an ANN model. The technique enables precise measurement of line width, line spacing, and the height of redistribution layer structures or through-silicon vias. Compared with AFM measurements, the method exhibits a less than 2% measured bias for structure top width and spacing, and under 1.2% for height measurements. This nondestructive optical method completes measurements in less than 3 ms, a 104-fold speed increase compared to the traditional library search. It also offers considerable improvements in speed and cost-efficiency over traditional techniques, which make it ideal for integration into production environments. The development has been proven an effective tool in semiconductor manufacturing and provides an inline process control solution for accurate critical dimension measurements. Semiconductor manufacturing metrology optical critical dimension (OCD) coherence optical scatterometry (COS) artificial intelligence Full Text Cite Share Download PDF Status: Published Journal Publication published 08 Jan, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 10 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers invited by journal 28 Aug, 2025 Editor assigned by journal 28 Aug, 2025 First submitted to journal 26 Aug, 2025 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-7467039","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506949256,"identity":"3c922b81-7339-4bff-9d5b-9766b00db9d4","order_by":0,"name":"Fu-Sheng Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fu-Sheng","middleName":"","lastName":"Yang","suffix":""},{"id":506949257,"identity":"be0df1fb-bfd7-4134-916f-18ff63852c03","order_by":1,"name":"Liang-Chia Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACPgYeIFkB4UgQpYUNrOUMRDWQMCBSC2MbSVrYzx58XDjvTp3BAeaDt3kY/iQ2ENTCk5dsPHPbMwmDA2zJ1jwMBkRokeAxk+bddhioBcgAasklRov5b945IC3834jWYsbM2wC2hY1ILTw5xtI8xw5LzjzMZmw5x8C4nqAWfvYzhp95ag7z8x1vfnjjTYWcMSEdSIAZRBARLaNgFIyCUTAKiAAAzOsvrWM4jqAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9613-9936","institution":"National Taiwan University","correspondingAuthor":true,"prefix":"","firstName":"Liang-Chia","middleName":"","lastName":"Chen","suffix":""},{"id":506949258,"identity":"f341c7b8-b5a5-43e2-b37e-411c8e1e811d","order_by":2,"name":"Chao-Ching Ho","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chao-Ching","middleName":"","lastName":"Ho","suffix":""}],"badges":[],"createdAt":"2025-08-27 02:26:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7467039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7467039/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-17343-4","type":"published","date":"2026-01-08T15:58:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100070492,"identity":"85f267b8-331b-483a-81b0-262e78e3cc08","added_by":"auto","created_at":"2026-01-12 16:17:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2775586,"visible":true,"origin":"","legend":"","description":"","filename":"DiffractionOCDIJAMT.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7467039/v1_covered_84ec6a47-a7af-4853-be0f-73b1da9540cb.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eArtificial Neural Network-driven Diffraction Imaging for Nanoscale Optical Critical Dimension Metrology in Semiconductor Manufacturing\u003c/strong\u003e\u003c/p\u003e","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":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Semiconductor manufacturing, metrology, optical critical dimension (OCD), coherence optical scatterometry (COS), artificial intelligence","lastPublishedDoi":"10.21203/rs.3.rs-7467039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7467039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study introduces a new optical measurement technique that integrates artificial neural network (ANN) technology with coherent optical scatterometry and back focal plane image to address the limitations of conventional methods, such as scanning electron microscopy and atomic force microscopy (AFM). 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