TY - GEN
T1 - Stochastic analysis of interconnect performance in the presence of process variations
AU - Wang, Janet
AU - Ghanta, Praveen
AU - Vrudhula, Sarma
PY - 2004
Y1 - 2004
N2 - Deformations in interconnect due to process variations can lead to significant performance degradation in deep submicron 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.
AB - Deformations in interconnect due to process variations can lead to significant performance degradation in deep submicron 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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U2 - 10.1109/ICCAD.2004.1382698
DO - 10.1109/ICCAD.2004.1382698
M3 - Conference contribution
AN - SCOPUS:16244379528
SN - 0780387023
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
SP - 880
EP - 886
BT - ICCAD-2004 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers
T2 - ICCAD-2004 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers
Y2 - 7 November 2004 through 11 November 2004
ER -