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dc.contributor.advisorWalker, Duncan M.
dc.creatorBalasubramanian, Vijay
dc.date.accessioned2007-04-25T20:06:47Z
dc.date.available2007-04-25T20:06:47Z
dc.date.created2006-12
dc.date.issued2007-04-25
dc.identifier.urihttp://hdl.handle.net/1969.1/4766
dc.description.abstractIntegrated circuits manufactured in current technology consist of millions of transistors with dimensions shrinking into the nanometer range. These small transistors have quiescent (leakage) currents that are increasingly sensitive to process variations, which have increased the variation in good-chip quiescent current and consequently reduced the effectiveness of IDDQ testing. This research proposes the use of a multivariate statistical technique known as principal component analysis for the purpose of variance reduction. Outlier analysis is applied to the reduced leakage current values as well as the good chip leakage current estimate, to identify defective chips. The proposed idea is evaluated using IDDQ values from multiple wafers of an industrial chip fabricated in 130 nm technology. It is shown that the proposed method achieves significant variance reduction and identifies many outliers that escape identification by other established techniques. For example, it identifies many of the absolute outliers in bad neighborhoods, which are not detected by Nearest Neighbor Residual and Nearest Current Ratio. It also identifies many of the spatial outliers that pass when using Current Ratio. The proposed method also identifies both active and passive defects.en
dc.format.extent3632736 bytesen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherTexas A&M University
dc.subjectIDDQ testingen
dc.subjectVariance Reductionen
dc.subjectPCAen
dc.titleVariance reduction and outlier identification for IDDQ testing of integrated chips using principal component analysisen
dc.typeBooken
dc.typeThesisen
thesis.degree.departmentComputer Scienceen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberShi, Weiping
dc.contributor.committeeMemberWelch, Jennifer L.
dc.type.genreElectronic Thesisen
dc.type.materialtexten
dc.format.digitalOriginborn digitalen


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