Automatic semiconductor wafer map defect signature detection using a neural network classifier

dc.contributor.advisorGhosh, Joydeepen
dc.contributor.committeeMemberEl-Hamdi, Mohameden
dc.creatorRadhamohan, Ranjan Subbarayaen
dc.date.accessioned2011-02-21T19:57:09Zen
dc.date.available2011-02-21T19:57:09Zen
dc.date.available2011-02-21T19:57:22Zen
dc.date.issued2010-12en
dc.date.submittedDecember 2010en
dc.date.updated2011-02-21T19:57:22Zen
dc.descriptiontexten
dc.description.abstractThe application of popular image processing and classification algorithms, including agglomerative clustering and neural networks, is explored for the purpose of grouping semiconductor wafer defect map patterns. Challenges such as overlapping pattern separation, wafer rotation, and false data removal are examined and solutions proposed. After grouping, wafer processing history is used to automatically determine the most likely source of the issue. Results are provided that indicate these methods hold promise for wafer analysis applications.en
dc.description.departmentElectrical and Computer Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-12-2423en
dc.language.isoengen
dc.subjectSemiconductoren
dc.subjectWaferen
dc.subjectNeuralen
dc.subjectNetworken
dc.subjectAgglomerativeen
dc.subjectClusteringen
dc.subjectMapen
dc.subjectDefecten
dc.subjectYielden
dc.titleAutomatic semiconductor wafer map defect signature detection using a neural network classifieren
dc.type.genrethesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen

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