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Variation-Aware Interconnect Extraction using Statistical Moment Preserving Model Order Reduction

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dc.contributor.author Daniel, Luca
dc.contributor.author El-Moselhy, Tarek Ali
dc.date.accessioned 2011-04-08T15:03:40Z
dc.date.available 2011-04-08T15:03:40Z
dc.date.issued 2010-04
dc.date.submitted 2010-03
dc.identifier.isbn 978-1-4244-7054-9
dc.identifier.issn 1530-1591
dc.identifier.other INSPEC Accession Number: 11283398
dc.identifier.uri http://hdl.handle.net/1721.1/62170
dc.description.abstract In this paper we present a stochastic model order reduction technique for interconnect extraction in the presence of process variabilities, i.e. variation-aware extraction. It is becoming increasingly evident that sampling based methods for variation-aware extraction are more efficient than more computationally complex techniques such as stochastic Galerkin method or the Neumann expansion. However, one of the remaining computational challenges of sampling based methods is how to simultaneously and efficiently solve the large number of linear systems corresponding to each different sample point. In this paper, we present a stochastic model reduction technique that exploits the similarity among the different solves to reduce the computational complexity of subsequent solves. We first suggest how to build a projection matrix such that the statistical moments and/or the coefficients of the projection of the stochastic vector on some orthogonal polynomials are preserved.We further introduce a proximity measure, which we use to determine apriori if a given system needs to be solved, or if it is instead properly represented using the currently available basis. Finally, in order to reduce the time required for the system assembly, we use the multivariate Hermite expansion to represent the system matrix. We verify our method by solving a variety of variation-aware capacitance extraction problems ranging from on-chip capacitance extraction in the presence of width and thickness variations, to off-chip capacitance extraction in the presence of surface roughness. We further solve very large scale problems that cannot be handled by any other state of the art technique. en_US
dc.description.sponsorship Semiconductor Research Corporation. Focus Center Research Program en_US
dc.description.sponsorship Semiconductor Research Corporation. Interconnect Focus Center en_US
dc.language.iso en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.rights Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. en_US
dc.source IEEE en_US
dc.title Variation-Aware Interconnect Extraction using Statistical Moment Preserving Model Order Reduction en_US
dc.type Article en_US
dc.identifier.citation El-Moselhy, Tarek, and Luca Daniel. “Variation-aware Interconnect Extraction Using Statistical Moment Preserving Model Order Reduction.” Design, Automation & Test in Europe Conference & Exhibition (DATE), 2010. 2010. 453-458. ©2010 IEEE. en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science en_US
dc.contributor.approver Daniel, Luca
dc.contributor.mitauthor Daniel, Luca
dc.contributor.mitauthor El-Moselhy, Tarek Ali
dc.relation.journal Design, Automation & Test in Europe Conference & Exhibition (DATE), 2010 en_US
dc.identifier.mitlicense PUBLISHER_POLICY en_US
dc.eprint.version Final published version en_US
dc.type.uri http://purl.org/eprint/type/ConferencePaper en_US
dspace.orderedauthors El-Moselhy, Tarek; Daniel, Luca


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