OperaV1, PascalFrancis, Corpus, bibRecord, 000375

Stochastic analysis of interconnect performance in the presence of process variations

Identifieur interne : 000375 ( PascalFrancis/Corpus ); précédent : 000374; suivant : 000376

Stochastic analysis of interconnect performance in the presence of process variations

Auteurs : Janet Wang ; Praveen Ghanta ; Sarma Vrudhula

Source :

RBID : Pascal:06-0156613

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.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A09	`01`	`1`	`ENG`	`@1 ICCAD-2004 : International Conference on Computer Aided Design : November 7-11, 2004, DoubleTree Hotel, San Jose, CA`
A11	`01`	`1`		`@1 WANG (Janet)`
A11	`02`	`1`		`@1 GHANTA (Praveen)`
A11	`03`	`1`		`@1 VRUDHULA (Sarma)`
A14	`01`			`@1 ECE Dept., Univ. of Arizona @2 Tucson, Arizona @3 USA @Z 1 aut. @Z 2 aut. @Z 3 aut.`
A18	`01`	`1`		`@1 IEEE Circuits and Systems Society @3 USA @9 org-cong.`
A20				`@2 vol2, 880-886`
A21				`@1 2004`
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A25	`02`			`@1 ACM @2 New-York, N.Y.`
A26	`01`			`@0 0-7803-8702-3`
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A43	`01`			`@1 INIST @2 Y 38705 @5 354000138704991270`
A44				`@0 0000 @1 © 2006 INIST-CNRS. All rights reserved.`
A45				`@0 28 ref.`
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A60				`@1 C`
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C01	`01`		`ENG`	@0 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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C03	`06`	`X`	`ENG`	`@0 Timed system @5 20`
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C03	`07`	`X`	`FRE`	`@0 Effet retard @5 21`
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C03	`07`	`X`	`SPA`	`@0 Efecto retardo @5 21`
C03	`08`	`X`	`FRE`	`@0 Appel procédure @5 22`
C03	`08`	`X`	`ENG`	`@0 Procedure call @5 22`
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C03	`14`	`X`	`FRE`	`@0 Polynôme orthogonal @5 28`
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N21				`@1 093`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

Format Inist (serveur)

NO :	PASCAL 06-0156613 INIST
ET :	Stochastic analysis of interconnect performance in the presence of process variations
AU :	WANG (Janet); GHANTA (Praveen); VRUDHULA (Sarma)
AF :	ECE Dept., Univ. of Arizona/Tucson, Arizona/Etats-Unis (1 aut., 2 aut., 3 aut.)
DT :	Congrès; Niveau analytique
SO :	IEEE/ACM International Conference on Computer-Aided Design/2004-11-07/San Jose CA USA; Etats-Unis; Piscataway, N.J., New-York, N.Y.: IEEE; Da. 2004; vol2, 880-886; ISBN 0-7803-8702-3
LA :	Anglais
EA :	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.
CC :	001D02B11; 001D03F06B
FD :	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
ED :	Computer aided design; Circuit design; Stochastic process; Stochastic analysis; Performance evaluation; Timed system; Delay effect; Procedure call; Statistical analysis; Modeling; Stochastic method; Probabilistic approach; Hilbert space; Orthogonal polynomial; Galerkin method; Random variable; Monte Carlo method; Perturbation method
SD :	Concepción asistida; Diseño circuito; Proceso estocástico; Análisis estocástico; Evaluación prestación; Sistema temporizado; Efecto retardo; Llamada procedimiento; Análisis estadístico; Modelización; Método estocástico; Enfoque probabilista; Espacio Hilbert; Polinomio ortogonal; Método Galerkin; Variable aléatoria; Método Monte Carlo; Método perturbación
LO :	INIST-Y 38705.354000138704991270
ID :	06-0156613

Links to Exploration step

Pascal:06-0156613

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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<fC03 i1="08" i2="X" l="SPA"><s0>Llamada procedimiento</s0>
<s5>22</s5>
</fC03>
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<s5>23</s5>
</fC03>
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<s5>23</s5>
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<fC03 i1="09" i2="X" l="SPA"><s0>Análisis estadístico</s0>
<s5>23</s5>
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<s5>24</s5>
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<fC03 i1="10" i2="X" l="ENG"><s0>Modeling</s0>
<s5>24</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Modelización</s0>
<s5>24</s5>
</fC03>
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<s5>25</s5>
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<s5>25</s5>
</fC03>
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<s5>25</s5>
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<s5>26</s5>
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<s5>26</s5>
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<s5>26</s5>
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<s5>27</s5>
</fC03>
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<s5>27</s5>
</fC03>
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<s5>27</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Polynôme orthogonal</s0>
<s5>28</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Orthogonal polynomial</s0>
<s5>28</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Polinomio ortogonal</s0>
<s5>28</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Méthode Galerkin</s0>
<s5>29</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Galerkin method</s0>
<s5>29</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Método Galerkin</s0>
<s5>29</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Variable aléatoire</s0>
<s5>30</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Random variable</s0>
<s5>30</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Variable aléatoria</s0>
<s5>30</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Méthode Monte Carlo</s0>
<s5>31</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Monte Carlo method</s0>
<s5>31</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Método Monte Carlo</s0>
<s5>31</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Méthode perturbation</s0>
<s5>32</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Perturbation method</s0>
<s5>32</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Método perturbación</s0>
<s5>32</s5>
</fC03>
<fN21><s1>093</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
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<server><NO>PASCAL 06-0156613 INIST</NO>
<ET>Stochastic analysis of interconnect performance in the presence of process variations</ET>
<AU>WANG (Janet); GHANTA (Praveen); VRUDHULA (Sarma)</AU>
<AF>ECE Dept., Univ. of Arizona/Tucson, Arizona/Etats-Unis (1 aut., 2 aut., 3 aut.)</AF>
<DT>Congrès; Niveau analytique</DT>
<SO>IEEE/ACM International Conference on Computer-Aided Design/2004-11-07/San Jose CA USA; Etats-Unis; Piscataway, N.J., New-York, N.Y.: IEEE; Da. 2004; vol2, 880-886; ISBN 0-7803-8702-3</SO>
<LA>Anglais</LA>
<EA>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.</EA>
<CC>001D02B11; 001D03F06B</CC>
<FD>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</FD>
<ED>Computer aided design; Circuit design; Stochastic process; Stochastic analysis; Performance evaluation; Timed system; Delay effect; Procedure call; Statistical analysis; Modeling; Stochastic method; Probabilistic approach; Hilbert space; Orthogonal polynomial; Galerkin method; Random variable; Monte Carlo method; Perturbation method</ED>
<SD>Concepción asistida; Diseño circuito; Proceso estocástico; Análisis estocástico; Evaluación prestación; Sistema temporizado; Efecto retardo; Llamada procedimiento; Análisis estadístico; Modelización; Método estocástico; Enfoque probabilista; Espacio Hilbert; Polinomio ortogonal; Método Galerkin; Variable aléatoria; Método Monte Carlo; Método perturbación</SD>
<LO>INIST-Y 38705.354000138704991270</LO>
<ID>06-0156613</ID>
</server>
</inist>
</record>

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Stochastic analysis of interconnect performance in the presence of process variations

Stochastic analysis of interconnect performance in the presence of process variations

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