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dc.contributor.authorYucel, Abdulkadir C.
dc.contributor.authorBagci, Hakan
dc.contributor.authorMichielssen, Eric
dc.date.accessioned2015-05-14T18:29:01Z
dc.date.available2015-05-14T18:29:01Z
dc.date.issued2015-05-05
dc.identifier.citationAn ME-PC Enhanced HDMR Method for Efficient Statistical Analysis of Multiconductor Transmission Line Networks 2015:1 IEEE Transactions on Components, Packaging and Manufacturing Technology
dc.identifier.issn2156-3950
dc.identifier.issn2156-3985
dc.identifier.doi10.1109/TCPMT.2015.2424679
dc.identifier.urihttp://hdl.handle.net/10754/552901
dc.description.abstractAn efficient method for statistically characterizing multiconductor transmission line (MTL) networks subject to a large number of manufacturing uncertainties is presented. The proposed method achieves its efficiency by leveraging a high-dimensional model representation (HDMR) technique that approximates observables (quantities of interest in MTL networks, such as voltages/currents on mission-critical circuits) in terms of iteratively constructed component functions of only the most significant random variables (parameters that characterize the uncertainties in MTL networks, such as conductor locations and widths, and lumped element values). The efficiency of the proposed scheme is further increased using a multielement probabilistic collocation (ME-PC) method to compute the component functions of the HDMR. The ME-PC method makes use of generalized polynomial chaos (gPC) expansions to approximate the component functions, where the expansion coefficients are expressed in terms of integrals of the observable over the random domain. These integrals are numerically evaluated and the observable values at the quadrature/collocation points are computed using a fast deterministic simulator. The proposed method is capable of producing accurate statistical information pertinent to an observable that is rapidly varying across a high-dimensional random domain at a computational cost that is significantly lower than that of gPC or Monte Carlo methods. The applicability, efficiency, and accuracy of the method are demonstrated via statistical characterization of frequency-domain voltages in parallel wire, interconnect, and antenna corporate feed networks.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7101849
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectCrosstalk
dc.subjectgeneralized polynomial chaos (gPC)
dc.subjectglobal sensitivity analysis
dc.subjecthigh-dimensional model representation (HDMR)
dc.subjectinterconnects
dc.subjectmulticonductor transmission lines (MTLs)
dc.subjectmultielement probabilistic collocation (ME-PC) method
dc.subjectstochastic analysis
dc.subjectsurrogate model
dc.subjecttolerance analysis
dc.subjectuncertainty quantification
dc.titleAn ME-PC Enhanced HDMR Method for Efficient Statistical Analysis of Multiconductor Transmission Line Networks
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE Transactions on Components, Packaging and Manufacturing Technology
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA
kaust.personBagci, Hakan
refterms.dateFOA2018-06-13T10:03:47Z
dc.date.published-online2015-05-05
dc.date.published-print2015-05


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