dc.contributor.author | Zhang, Wangyang | |
dc.contributor.author | Balakrishnan, Karthik | |
dc.contributor.author | Li, Xin | |
dc.contributor.author | Acar, Emrah | |
dc.contributor.author | Liu, Frank | |
dc.contributor.author | Rutenbar, Rob A. | |
dc.contributor.author | Boning, Duane S. | |
dc.date.accessioned | 2014-12-22T14:48:54Z | |
dc.date.available | 2014-12-22T14:48:54Z | |
dc.date.issued | 2012-05 | |
dc.identifier.isbn | 978-1-4673-0145-9 | |
dc.identifier.isbn | 978-1-4673-0146-6 | |
dc.identifier.isbn | 978-1-4673-0144-2 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/92427 | |
dc.description.abstract | In 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.sponsorship | Interconnect Focus Center (United States. Defense Advanced Research Projects Agency and Semiconductor Research Corporation) | en_US |
dc.description.sponsorship | Focus Center Research Program. Focus Center for Circuit & System Solutions | en_US |
dc.description.sponsorship | National Science Foundation (U.S.) (Contract CCF-0915912) | en_US |
dc.language.iso | en_US | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/ICICDT.2012.6232875 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | Boning | en_US |
dc.title | Spatial variation decomposition via sparse regression | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Zhang, 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.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Microsystems Technology Laboratories | en_US |
dc.contributor.approver | Boning, Duane S. | en_US |
dc.contributor.mitauthor | Boning, Duane S. | en_US |
dc.relation.journal | Proceedings of the 2012 IEEE International Conference on IC Design & Technology | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dspace.orderedauthors | Zhang, Wangyang; Balakrishnan, Karthik; Li, Xin; Boning, Duane; Acar, Emrah; Liu, Frank; Rutenbar, Rob A. | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-0417-445X | |
mit.license | OPEN_ACCESS_POLICY | en_US |
mit.metadata.status | Complete | |