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dc.contributor.authorZhang, Wangyang
dc.contributor.authorBalakrishnan, Karthik
dc.contributor.authorLi, Xin
dc.contributor.authorAcar, Emrah
dc.contributor.authorLiu, Frank
dc.contributor.authorRutenbar, Rob A.
dc.contributor.authorBoning, Duane S.
dc.date.accessioned2014-12-22T14:48:54Z
dc.date.available2014-12-22T14:48:54Z
dc.date.issued2012-05
dc.identifier.isbn978-1-4673-0145-9
dc.identifier.isbn978-1-4673-0146-6
dc.identifier.isbn978-1-4673-0144-2
dc.identifier.urihttp://hdl.handle.net/1721.1/92427
dc.description.abstractIn this paper, we briefly discuss the recent development of a novel sparse regression technique that aims to accurately decompose process variation into two different components: (1) spatially correlated variation, and (2) uncorrelated random variation. Such variation decomposition is important to identify systematic variation patterns at wafer and/or chip level for process modeling, control and diagnosis. We demonstrate that the spatially correlated variation can be accurately represented by the linear combination of a small number of “templates”. Based upon this observation, an efficient algorithm is developed to accurately separate spatially correlated variation from uncorrelated random variation. Several examples based on silicon measurement data demonstrate that the aforementioned sparse regression technique can capture systematic variation patterns with high accuracy.en_US
dc.description.sponsorshipInterconnect Focus Center (United States. Defense Advanced Research Projects Agency and Semiconductor Research Corporation)en_US
dc.description.sponsorshipFocus Center Research Program. Focus Center for Circuit & System Solutionsen_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Contract CCF-0915912)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICICDT.2012.6232875en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceBoningen_US
dc.titleSpatial variation decomposition via sparse regressionen_US
dc.typeArticleen_US
dc.identifier.citationZhang, Wangyang, Karthik Balakrishnan, Xin Li, Duane Boning, Emrah Acar, Frank Liu, and Rob A. Rutenbar. “Spatial Variation Decomposition via Sparse Regression.” 2012 IEEE International Conference on IC Design & Technology (May 2012).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Microsystems Technology Laboratoriesen_US
dc.contributor.approverBoning, Duane S.en_US
dc.contributor.mitauthorBoning, Duane S.en_US
dc.relation.journalProceedings of the 2012 IEEE International Conference on IC Design & Technologyen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsZhang, Wangyang; Balakrishnan, Karthik; Li, Xin; Boning, Duane; Acar, Emrah; Liu, Frank; Rutenbar, Rob A.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-0417-445X
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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