Defect Recognition in Array Chips: A Two-Dimensional Otsu-SVM Method Optimized by PSO++ 

Defect Recognition in Array Chips: A Two-Dimensional Otsu-SVM Method Optimized by PSO++ | 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 Defect Recognition in Array Chips: A Two-Dimensional Otsu-SVM Method Optimized by PSO++ Biao Wang, Yanlin Zhu, Shihao Li, Xiaoling Cao, kaifa cao, Yonghong Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8142701/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 Accurate segmentation and classification of array chip defects are crucial for intelligent chip manufacturing. To address issues of inaccurate threshold segmentation and redundant high-dimensional features, this paper proposes an array chip defect recognition method based on an improved Particle Swarm Optimization (PSO++). PSO + + is combined with 2D Otsu for plastic encapsulation defect segmentation, dynamically adjusting inertia weights and acceleration coefficients to achieve optimal thresholds efficiently. For pin defects, a contour-rectangularity-based algorithm precisely locates defect regions. PSO + + further selects optimal feature subsets for an SVM classifier, improving accuracy while reducing dimensionality. Experiments show PSO++-Otsu shortens segmentation time by 40–49%, and PSO++-SVM achieves 96.20–98.60% accuracy, outperforming existing methods by 2.20–7.47%. Physical sciences/Engineering Physical sciences/Mathematics and computing PSO++ 2D Otsu SVM Threshold segmentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 May, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 01 Dec, 2025 Editor invited by journal 01 Dec, 2025 Submission checks completed at journal 28 Nov, 2025 First submitted to journal 28 Nov, 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-8142701","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":559555229,"identity":"89273ec2-f14a-4c4d-a7f0-d747472dfd85","order_by":0,"name":"Biao Wang","email":"","orcid":"","institution":"Hefei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Biao","middleName":"","lastName":"Wang","suffix":""},{"id":559555230,"identity":"99c2c67b-6105-4fa2-bb82-d00d99186e57","order_by":1,"name":"Yanlin Zhu","email":"","orcid":"","institution":"Hefei University of 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","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"PSO++, 2D Otsu, SVM, Threshold segmentation","lastPublishedDoi":"10.21203/rs.3.rs-8142701/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8142701/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate segmentation and classification of array chip defects are crucial for intelligent chip manufacturing. To address issues of inaccurate threshold segmentation and redundant high-dimensional features, this paper proposes an array chip defect recognition method based on an improved Particle Swarm Optimization (PSO++). PSO\u0026thinsp;+\u0026thinsp;+\u0026thinsp;is combined with 2D Otsu for plastic encapsulation defect segmentation, dynamically adjusting inertia weights and acceleration coefficients to achieve optimal thresholds efficiently. For pin defects, a contour-rectangularity-based algorithm precisely locates defect regions. PSO\u0026thinsp;+\u0026thinsp;+\u0026thinsp;further selects optimal feature subsets for an SVM classifier, improving accuracy while reducing dimensionality. Experiments show PSO++-Otsu shortens segmentation time by 40\u0026ndash;49%, and PSO++-SVM achieves 96.20\u0026ndash;98.60% accuracy, outperforming existing methods by 2.20\u0026ndash;7.47%.\u003c/p\u003e","manuscriptTitle":"Defect Recognition in Array Chips: A Two-Dimensional Otsu-SVM Method Optimized by PSO++ ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 21:59:09","doi":"10.21203/rs.3.rs-8142701/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"97934839795607386878421722222054052980","date":"2026-05-06T04:38:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T03:55:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175596442061665032144094395912337394691","date":"2026-04-06T13:33:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237866314947564143916775213946694706752","date":"2026-04-03T04:00:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T14:24:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T22:27:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-01T17:51:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-28T10:05:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-28T09:58:21+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"10925bd1-642f-424c-bb05-cf397ceb0f38","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"97934839795607386878421722222054052980","date":"2026-05-06T04:38:50+00:00","index":85,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59573844,"name":"Physical sciences/Engineering"},{"id":59573845,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-15T21:59:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 21:59:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8142701","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8142701","identity":"rs-8142701","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}