Stochastic analysis of interconnect performance in the presence of process variations
Identifieur interne : 000388 ( PascalFrancis/Checkpoint ); précédent : 000387; suivant : 000389Stochastic analysis of interconnect performance in the presence of process variations
Auteurs : Janet Wang [États-Unis] ; Praveen Ghanta [États-Unis] ; Sarma Vrudhula [États-Unis]Source :
Descripteurs français
- Pascal (Inist)
- Conception assistée, Conception circuit, Processus stochastique, Analyse stochastique, Evaluation performance, Système temporisé, Effet retard, Appel procédure, Analyse statistique, Modélisation, Méthode stochastique, Approche probabiliste, Espace Hilbert, Polynôme orthogonal, Méthode Galerkin, Variable aléatoire, Méthode Monte Carlo, Méthode perturbation.
English descriptors
- KwdEn :
- Circuit design, Computer aided design, Delay effect, Galerkin method, Hilbert space, Modeling, Monte Carlo method, Orthogonal polynomial, Performance evaluation, Perturbation method, Probabilistic approach, Procedure call, Random variable, Statistical analysis, Stochastic analysis, Stochastic method, Stochastic process, Timed system.
Abstract
Deformations in interconnect due to process variations can lead to significant performance degradation in deep sub-micron circuits. Timing analyzers attempt to capture the effects of variation on delay with simplified models. The timing verification of RC or RLC networks requires the substitution of such simplified models with spatial stochastic processes that capture the random nature of process variations. The present work proposes a new and viable method to compute the stochastic response of interconnects. The technique models the stochastic response in an infinite dimensional Hilbert space in terms of orthogonal polynomial expansions. A finite representation is obtained by using the Galerkin approach of minimizing the Hilbert space norm of the residual error. The key advance of the proposed method is that it provides a functional representation of the response of the system in terms of the random variables that represent the process variations. The proposed algorithm has been implemented in a procedure called OPERA. Results from OPERA simulations on commercial design test cases match well with those from the classical Monte Carlo SPICE simulations and from perturbation methods. Additionally OPERA shows good computational efficiency: speedup factor of 60 has been observed over Monte Carlo SPICE simulations.
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Timing analyzers attempt to capture the effects of variation on delay with simplified models. The timing verification of RC or RLC networks requires the substitution of such simplified models with spatial stochastic processes that capture the random nature of process variations. The present work proposes a new and viable method to compute the stochastic response of interconnects. The technique models the stochastic response in an infinite dimensional Hilbert space in terms of orthogonal polynomial expansions. A finite representation is obtained by using the Galerkin approach of minimizing the Hilbert space norm of the residual error. The key advance of the proposed method is that it provides a functional representation of the response of the system in terms of the random variables that represent the process variations. The proposed algorithm has been implemented in a procedure called OPERA. Results from OPERA simulations on commercial design test cases match well with those from the classical Monte Carlo SPICE simulations and from perturbation methods. Additionally OPERA shows good computational efficiency: speedup factor of 60 has been observed over Monte Carlo SPICE simulations.</div></front></TEI><inist><standard h6="B"><pA><fA08 i1="01" i2="1" l="ENG"><s1>Stochastic analysis of interconnect performance in the presence of process variations</s1></fA08><fA09 i1="01" i2="1" l="ENG"><s1>ICCAD-2004 : International Conference on Computer Aided Design : November 7-11, 2004, DoubleTree Hotel, San Jose, CA</s1></fA09><fA11 i1="01" i2="1"><s1>WANG (Janet)</s1></fA11><fA11 i1="02" i2="1"><s1>GHANTA (Praveen)</s1></fA11><fA11 i1="03" i2="1"><s1>VRUDHULA (Sarma)</s1></fA11><fA14 i1="01"><s1>ECE Dept., Univ. of Arizona</s1><s2>Tucson, Arizona</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ></fA14><fA18 i1="01" i2="1"><s1>IEEE Circuits and Systems Society</s1><s3>USA</s3><s9>org-cong.</s9></fA18><fA20><s2>vol2, 880-886</s2></fA20><fA21><s1>2004</s1></fA21><fA23 i1="01"><s0>ENG</s0></fA23><fA25 i1="01"><s1>IEEE</s1><s2>Piscataway, N.J.</s2></fA25><fA25 i1="02"><s1>ACM</s1><s2>New-York, N.Y.</s2></fA25><fA26 i1="01"><s0>0-7803-8702-3</s0></fA26><fA30 i1="01" i2="1" l="ENG"><s1>IEEE/ACM International Conference on Computer-Aided Design</s1><s3>San Jose CA USA</s3><s4>2004-11-07</s4></fA30><fA43 i1="01"><s1>INIST</s1><s2>Y 38705</s2><s5>354000138704991270</s5></fA43><fA44><s0>0000</s0><s1>© 2006 INIST-CNRS. 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The key advance of the proposed method is that it provides a functional representation of the response of the system in terms of the random variables that represent the process variations. The proposed algorithm has been implemented in a procedure called OPERA. Results from OPERA simulations on commercial design test cases match well with those from the classical Monte Carlo SPICE simulations and from perturbation methods. 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