Enhancing Yield and Quality in Multilayer PCB Manufacturing Through DoE-Based Process Optimization

Enhancing Yield and Quality in Multilayer PCB Manufacturing Through DoE-Based Process Optimization | 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 Enhancing Yield and Quality in Multilayer PCB Manufacturing Through DoE-Based Process Optimization Clemens Havas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9084744/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 The escalating complexity of multilayer printed circuit boards (PCBs) for applications in automotive electronics, high-speed telecommunications, and industrial control systems demands rigorous process control to ensure high yield and long-term reliability. This study presents a systematic approach to optimizing critical fabrication processes in multilayer PCB manufacturing using Design of Experiments (DoE) methodology. A full-factorial experimental design was employed to investigate the effects of lamination temperature, pressure, and dwell time on delamination resistance and layer registration accuracy. Subsequently, Response Surface Methodology (RSM) with a Central Composite Design (CCD) was applied to optimize copper plating parameters current density, plating time, and bath temperature to achieve uniform thickness distribution while minimizing processing time. Analysis of Variance (ANOVA) revealed that lamination temperature and its interaction with pressure significantly influence bond integrity (p < 0.05), while current density emerged as the dominant factor affecting thickness variability. The optimized parameter settings reduced delamination defects by 67%, improved layer-to-layer registration from ±75 µm to ±35 µm, and decreased plating time by 28% while maintaining target thickness specifications. These findings demonstrate that DoE-driven process optimization provides a robust framework for enhancing manufacturing capability and product quality in high-reliability PCB applications. Artificial Intelligence and Machine Learning Multilayer PCB Design of Experiments Process Optimization Response Surface Methodology Manufacturing Yield 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9084744","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603990226,"identity":"2ea8df68-fefe-42f0-a4d3-e6dd5fd61af2","order_by":0,"name":"Clemens Havas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACdiBmbGAAkwc+ACk2dkJamOFamA8enAHSwky8FrbkwzwwEXyAv5n56AbGHXaJ/f1nDA7b/Nomz8fMwPjhYw5uLRKH2dJuMJ5JTpxxI8fgcG7fbcM2ZgZmyZnbcGsxYOYxu8HYxmzMcIMHqKXnNpAN9A4vXi3834Ba6o3lzwMdZtlz254ILTxsQC2H5QwOpCUcZvhxO5GgFqBfzG4kth2XM7yRfOBgb8Pt5DZmxma8fuFvb35242NbNY/c+YPNH378uW07v7354IePeLSAQQKMwdgGJhsIqEcBf0hRPApGwSgYBSMFAAC0/1LexZeuwgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-3954-2176","institution":"Michigan State University","correspondingAuthor":true,"prefix":"","firstName":"Clemens","middleName":"","lastName":"Havas","suffix":""}],"badges":[],"createdAt":"2026-03-10 13:44:37","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-9084744/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9084744/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104780061,"identity":"211e8122-9a50-4f94-891d-08d9603c57f1","added_by":"auto","created_at":"2026-03-17 07:50:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":469007,"visible":true,"origin":"","legend":"","description":"","filename":"EnhancingYieldandQualityinMultilayerPCB.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9084744/v1_covered_76ba98e6-8c8d-47f4-9ad2-09d7efc5b37a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEnhancing Yield and Quality in Multilayer PCB Manufacturing Through DoE-Based Process Optimization\u003c/strong\u003e\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":"Multilayer PCB, Design of Experiments, Process Optimization, Response Surface Methodology, Manufacturing Yield","lastPublishedDoi":"10.21203/rs.3.rs-9084744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9084744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe escalating complexity of multilayer printed circuit boards (PCBs) for applications in automotive electronics, high-speed telecommunications, and industrial control systems demands rigorous process control to ensure high yield and long-term reliability. This study presents a systematic approach to optimizing critical fabrication processes in multilayer PCB manufacturing using Design of Experiments (DoE) methodology. A full-factorial experimental design was employed to investigate the effects of lamination temperature, pressure, and dwell time on delamination resistance and layer registration accuracy. Subsequently, Response Surface Methodology (RSM) with a Central Composite Design (CCD) was applied to optimize copper plating parameters current density, plating time, and bath temperature to achieve uniform thickness distribution while minimizing processing time. Analysis of Variance (ANOVA) revealed that lamination temperature and its interaction with pressure significantly influence bond integrity (p \u0026lt; 0.05), while current density emerged as the dominant factor affecting thickness variability. The optimized parameter settings reduced delamination defects by 67%, improved layer-to-layer registration from ±75 µm to ±35 µm, and decreased plating time by 28% while maintaining target thickness specifications. These findings demonstrate that DoE-driven process optimization provides a robust framework for enhancing manufacturing capability and product quality in high-reliability PCB applications.\u003c/p\u003e","manuscriptTitle":"Enhancing Yield and Quality in Multilayer PCB Manufacturing Through DoE-Based Process Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 09:00:25","doi":"10.21203/rs.3.rs-9084744/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"64abf9bc-3bd2-46de-9bab-1c9ce3f11719","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64255672,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2026-03-11T09:00:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 09:00:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9084744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9084744","identity":"rs-9084744","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}