Stochastic finite elements: a spectral approach
Stochastic finite elements: a spectral approach
Hierarchical analysis of power distribution networks
Proceedings of the 37th Annual Design Automation Conference
Proceedings of the 37th Annual Design Automation Conference
Proceedings of the 37th Annual Design Automation Conference
Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th annual Design Automation Conference
Statistical Verification of Power Grids Considering Process-Induced Leakage Current Variations
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Modeling Within-Die Spatial Correlation Effects for Process-Design Co-Optimization
ISQED '05 Proceedings of the 6th International Symposium on Quality of Electronic Design
Stochastic analysis of interconnect performance in the presence of process variations
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Robust extraction of spatial correlation
Proceedings of the 2006 international symposium on Physical design
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Stochastic variational analysis of large power grids considering intra-die correlations
Proceedings of the 43rd annual Design Automation Conference
Generating realistic stimuli for accurate power grid analysis
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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This article presents a comprehensive methodology for analyzing the impact of device and metal process variations on the power supply noise and hence the signal integrity of on-chip power grids. This approach models the power grid using modified nodal-analysis equations, and is based on representing the voltage response as an orthogonal polynomial series in the process variables. The series is truncated, and coefficients of the series are optimally obtained by using the Galerkin method. The authors thus obtain an analytical representation of the voltage response in the process variables that can be directly sampled to obtain the voltage response at different process corners. The authors have verified their analysis exhaustively on several industrial power grids as large as 1.3 million nodes, and considering up to 20 process variables. Results from their method demonstrate a very good match with those from Monte Carlo simulations, while providing significant speedups of the order of 100 to 1,000 times for comparable accuracy.