{"paper_id":"26dde340-9a67-4c8a-8fe3-f5fa964073b3","body_text":"Combining Residual Network and Bidirectional Long Short-Term Memory with Additive Attention for Wafer Defect Classification | 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 Combining Residual Network and Bidirectional Long Short-Term Memory with Additive Attention for Wafer Defect Classification Gyumin Kang, Gyucheol Lee, Younghoon Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7047273/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Nov, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Accurate classification of wafer-map defect patterns is critical for boosting yield and reducing cost in semiconductor fabrication. To solve this image classification problem, we propose a new methodology that combines the advantage of the Residual Network (ResNet)—its ability to minimize information loss in deep networks—with Long Short-Term Memory (LSTM) to capture both spatial and sequential features.We propose the Shortcut3-ResNet with Five Sets and Bidirectional LSTM with Additive Attention Network (SCRBLAA-Net), a lightweight composite model. In this model, a Shortcut3-ResNet with Five Sets (SCR5) block first distills spatial features from standardized inputs. This vector is then reshaped and analyzed by an attention-augmented Bidirectional-LSTM (Bi-LSTM), and the refined sequence is fused back with the original spatial representation to minimize information loss. On the WM-811K dataset from which the “none” class was removed, SCRBLAA-Net achieves an F1-score of 93.99%, outperforming the baseline SCR5 network by 1.6% points and surpassing SCR5-LSTM and SCR5-Bi-LSTM variants by 0.78 and 0.21% points, respectively. Wafer Defect Classification Residual Network Sliding-Window Tokenization Bidirectional-Long Short-Term Memory Additive Attention Full Text Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 05 Sep, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor assigned by journal 07 Jul, 2025 First submitted to journal 04 Jul, 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-7047273\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":483716985,\"identity\":\"16fb561c-b164-4c17-89f8-5a50b409689c\",\"order_by\":0,\"name\":\"Gyumin Kang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Gyumin\",\"middleName\":\"\",\"lastName\":\"Kang\",\"suffix\":\"\"},{\"id\":483716986,\"identity\":\"5ea8e3a4-be21-40fd-8596-a2ee628273fb\",\"order_by\":1,\"name\":\"Gyucheol Lee\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Gyucheol\",\"middleName\":\"\",\"lastName\":\"Lee\",\"suffix\":\"\"},{\"id\":483716987,\"identity\":\"f7919a0a-eb1d-4f4a-8100-ded2b5e671d6\",\"order_by\":2,\"name\":\"Younghoon Kim\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYFACNoYDDBUJCRDOAaK1nCFVCwNjGylaDA6wJR66OS8tz+AA88MPDGfuEaXlwOHcbTnFQIaxBMONYmK0sDcAtVQkbjjAYMbA8CGBWC1zQFrYvxGrBeSwhhygFh6gLTeI0CJ5mC3hcM6xtMSZh3mKJRLOEKGF73ib8eecmuTEvuPtGz98OEaEFoXDMBYzEBOhgYFBvoEYVaNgFIyCUTCyAQB66ED7Dax1EQAAAABJRU5ErkJggg==\",\"orcid\":\"https://orcid.org/0000-0003-0265-3825\",\"institution\":\"Kyung Hee University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Younghoon\",\"middleName\":\"\",\"lastName\":\"Kim\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-07-04 13:33:49\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7047273/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7047273/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s00170-025-16934-5\",\"type\":\"published\",\"date\":\"2025-11-15T15:57:22+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":96104984,\"identity\":\"1a287e28-1180-41f1-a633-ab6c23e45819\",\"added_by\":\"auto\",\"created_at\":\"2025-11-17 16:06:13\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2095014,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7047273/v1_covered_2221f775-b40a-4b84-912f-5b4a3d0708a9.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Combining Residual Network and Bidirectional Long Short-Term Memory with Additive Attention for Wafer Defect Classification\",\"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\":true,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Wafer Defect Classification, Residual Network, Sliding-Window Tokenization, Bidirectional-Long Short-Term Memory, Additive Attention\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7047273/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7047273/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"Accurate classification of wafer-map defect patterns is critical for boosting yield and reducing cost in semiconductor fabrication. 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