An industrial X-ray inspection framework for SMT solder bridge detection in BGA assemblies

An industrial X-ray inspection framework for SMT solder bridge detection in BGA assemblies | 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 Method Article An industrial X-ray inspection framework for SMT solder bridge detection in BGA assemblies Xiran Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8777482/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract X-ray inspection is widely used in electronics manufacturing for quality assessment of ball grid array (BGA) solder joints, where undetected defects can lead to functional failures and costly rework. Among various defect types, solder bridges are particularly challenging to identify due to their small size, low contrast, and strong visual similarity to surrounding components and package structures in industrial X-ray images. This paper presents a practical deep learning–based inspection framework for automated solder bridge detection in surface-mount technology (SMT) assemblies using real production X-ray data. The proposed approach adopts a coarse-to-fine, two-stage inspection strategy tailored for factory deployment. In the first stage, BGA regions are automatically localized and normalized to suppress background clutter and reduce scale variations across different boards. In the second stage, a lightweight object detection model is applied to identify solder bridge candidates with an explicit emphasis on high recall under limited labeled data. The proposed framework is evaluated on an industrial X-ray dataset collected from a production line. Experimental results on unseen test images demonstrate that the method achieves high defect recall while maintaining stable detection performance, indicating its robustness and suitability for real-world manufacturing inspection scenarios. Electrical Engineering X-ray inspection Integrated circuit assembly BGA solder joints SMT inspection Manufacturing defect inspection Quality control Intelligent manufacturing Machine vision Deep learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted 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. 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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-8777482","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":585136291,"identity":"02f6b94d-c0cf-4cb2-8eb1-f67e0e4ccf75","order_by":0,"name":"Xiran Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDAC5gPsHz5U2MhBeGzEaGFLYGOccSbNmDQtzLxthxMbiNZicIz92QOeM2np2/nPGDB8KDtMWItkG0O6gUSFTe7OGTkGjDPOEaGFX77hgITBmbTcDTd4DEAuJKyFjY2xQSKx7XC6wfkzBsx/idHCz8bMJnGw7XCCwYEcA2ZGYrRItrExGzacSTPcOSOt4GDPuXTCWoAh9vDxnwobeXP+wxsf/CizJqwFoReID5CgHqplFIyCUTAKRgFWAAC+Bju1AWlhwwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-6491-0707","institution":"The Pennsylvania State University","correspondingAuthor":true,"prefix":"","firstName":"Xiran","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-02-03 15:05:23","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8777482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8777482/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101880979,"identity":"1565cc98-552c-4b0e-9b94-9e2ae908613c","added_by":"auto","created_at":"2026-02-04 15:08:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":565886,"visible":true,"origin":"","legend":"","description":"","filename":"AIinBGABridgeDetectionIJAMT.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8777482/v1_covered_de662a37-75e7-46fc-949e-3dae50c78479.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAn industrial X-ray inspection framework for SMT solder bridge detection in BGA assemblies\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"X-ray inspection,Integrated circuit assembly,BGA solder joints,SMT inspection,Manufacturing defect inspection,Quality control,Intelligent manufacturing,Machine vision,Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-8777482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8777482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eX-ray inspection is widely used in electronics manufacturing for quality assessment of ball grid array (BGA) solder joints, where undetected defects can lead to functional failures and costly rework. Among various defect types, solder bridges are particularly challenging to identify due to their small size, low contrast, and strong visual similarity to surrounding components and package structures in industrial X-ray images. This paper presents a practical deep learning–based inspection framework for automated solder bridge detection in surface-mount technology (SMT) assemblies using real production X-ray data. The proposed approach adopts a coarse-to-fine, two-stage inspection strategy tailored for factory deployment. In the first stage, BGA regions are automatically localized and normalized to suppress background clutter and reduce scale variations across different boards. In the second stage, a lightweight object detection model is applied to identify solder bridge candidates with an explicit emphasis on high recall under limited labeled data. The proposed framework is evaluated on an industrial X-ray dataset collected from a production line. 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