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Wafer classification using support vector machines

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dc.contributor.author Baly R.
dc.contributor.author Hajj H.
dc.contributor.editor
dc.date 2012
dc.date.accessioned 2017-09-07T07:07:56Z
dc.date.available 2017-09-07T07:07:56Z
dc.date.issued 2012
dc.identifier 10.1109/TSM.2012.2196058
dc.identifier.isbn
dc.identifier.issn 08946507
dc.identifier.uri http://hdl.handle.net/10938/11709
dc.description.abstract Increasing yield is a primary concern to integrated circuit manufacturing companies as it dictates the readiness of a new process for high volume manufacturing. In order to expedite the process of discovering yield issues, companies have started looking for ways to perform early prediction for such issues. This paper suggests the use of the support vector machines (SVMs) for early wafer classification. The choice of SVM is motivated by the model's ability to effectively classify multivariate, multimodal, and inseparable data points. This model uses multidimensional hyperplanes to separate and classify wafers into low-yield and high-yield classes. This paper includes a proposal for how the classification model can be applied for yield classification and how it can be adaptively updated in a manufacturing environment. We show how the values for the SVM parameters can be selected for best yield classification. Furthermore, performance evaluation is conducted on real manufacturing data, comparing the proposed SVM classifier to state of the art. Results show that in all cases, SVM consistently outperforms other methods with and without adaptive model updates. The experiments also show that all classifiers' performances depend on yield thresholds. It is also shown that the classification model can be built and executed using a reduced set without compromising its accuracy. © 1988-2012 IEEE.
dc.format.extent
dc.format.extent Pages: (373-383)
dc.language English
dc.publisher PISCATAWAY
dc.relation.ispartof Publication Name: IEEE Transactions on Semiconductor Manufacturing; Publication Year: 2012; Volume: 25; no. 3; Pages: (373-383);
dc.relation.ispartofseries
dc.relation.uri
dc.source Scopus
dc.subject.other
dc.title Wafer classification using support vector machines
dc.type Article
dc.contributor.affiliation Baly, R., Department of Electrical and Computer Engineering, American University of Beirut, Beirut 170225, Lebanon
dc.contributor.affiliation Hajj, H., Department of Electrical and Computer Engineering, American University of Beirut, Beirut 170225, Lebanon
dc.contributor.authorAddress Baly, R.; Department of Electrical and Computer Engineering, American University of Beirut, Beirut 170225, Lebanon; email: rgb15@aub.edu.lb
dc.contributor.authorCorporate University: American University of Beirut; Faculty: Faculty of Engineering and Architecture; Department: Electrical and Computer Engineering;
dc.contributor.authorDepartment Electrical and Computer Engineering
dc.contributor.authorDivision
dc.contributor.authorEmail rgb15@aub.edu.lb; hazem.hajj@aub.edu.lb
dc.contributor.authorFaculty Faculty of Engineering and Architecture
dc.contributor.authorInitials Baly, R
dc.contributor.authorInitials Hajj, H
dc.contributor.authorOrcidID
dc.contributor.authorReprintAddress Baly, R (reprint author), Amer Univ Beirut, Dept Elect and Comp Engn, Beirut 170225, Lebanon.
dc.contributor.authorResearcherID
dc.contributor.authorUniversity American University of Beirut
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dc.description.citedCount 7
dc.description.citedTotWOSCount 4
dc.description.citedWOSCount 4
dc.format.extentCount 11
dc.identifier.articleNo 6189084
dc.identifier.coden ITSME
dc.identifier.pubmedID
dc.identifier.scopusID 84864646224
dc.identifier.url
dc.publisher.address 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
dc.relation.ispartofConference
dc.relation.ispartofConferenceCode
dc.relation.ispartofConferenceDate
dc.relation.ispartofConferenceHosting
dc.relation.ispartofConferenceLoc
dc.relation.ispartofConferenceSponsor
dc.relation.ispartofConferenceTitle
dc.relation.ispartofFundingAgency
dc.relation.ispartOfISOAbbr IEEE Trans. Semicond. Manuf.
dc.relation.ispartOfIssue 3
dc.relation.ispartOfPart
dc.relation.ispartofPubTitle IEEE Transactions on Semiconductor Manufacturing
dc.relation.ispartofPubTitleAbbr IEEE Trans Semicond Manuf
dc.relation.ispartOfSpecialIssue
dc.relation.ispartOfSuppl
dc.relation.ispartOfVolume 25
dc.source.ID WOS:000314401300012
dc.type.publication Journal
dc.subject.otherAuthKeyword Data models
dc.subject.otherAuthKeyword data processing
dc.subject.otherAuthKeyword multivariable systems
dc.subject.otherAuthKeyword nonlinear systems
dc.subject.otherAuthKeyword prediction methods
dc.subject.otherAuthKeyword semiconductor defects
dc.subject.otherAuthKeyword yield estimation
dc.subject.otherChemCAS
dc.subject.otherIndex Adaptive models
dc.subject.otherIndex Classification models
dc.subject.otherIndex Data points
dc.subject.otherIndex Early prediction
dc.subject.otherIndex High volume manufacturing
dc.subject.otherIndex Integrated circuit manufacturing
dc.subject.otherIndex Low-yield
dc.subject.otherIndex Manufacturing environments
dc.subject.otherIndex Multi-modal
dc.subject.otherIndex Performance evaluation
dc.subject.otherIndex Prediction methods
dc.subject.otherIndex State of the art
dc.subject.otherIndex SVM classifiers
dc.subject.otherIndex Yield estimation
dc.subject.otherIndex Yield threshold
dc.subject.otherIndex Crystal defects
dc.subject.otherIndex Data processing
dc.subject.otherIndex Data structures
dc.subject.otherIndex Industry
dc.subject.otherIndex Manufacture
dc.subject.otherIndex Multivariable systems
dc.subject.otherIndex Nonlinear systems
dc.subject.otherIndex Support vector machines
dc.subject.otherKeywordPlus SEMICONDUCTOR MANUFACTURING PROCESSES
dc.subject.otherKeywordPlus NEAREST-NEIGHBOR RULE
dc.subject.otherKeywordPlus VIRTUAL METROLOGY
dc.subject.otherKeywordPlus FAULT-DETECTION
dc.subject.otherKeywordPlus SYSTEM
dc.subject.otherWOS Engineering, Manufacturing
dc.subject.otherWOS Engineering, Electrical and Electronic
dc.subject.otherWOS Physics, Applied
dc.subject.otherWOS Physics, Condensed Matter


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