Name
Abstract | ||
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Due to significant manufacturing process variations, the performance of integrated circuits (ICs) has become increasingly uncertain. Such uncertainties must be carefully quantified with efficient stochastic circuit simulators. This paper discusses the recent advances of stochastic spectral circuit simulators based on generalized polynomial chaos (gPC). Such techniques can handle both Gaussian and non-Gaussian random parameters, showing remarkable speedup over Monte Carlo for circuits with a small or medium number of parameters. We focus on the recently developed stochastic testing and the application of conventional stochastic Galerkin and stochastic collocation schemes to nonlinear circuit problems. The uncertainty quantification algorithms for static, transient and periodic steady-state simulations are presented along with some practical simulation results. Some open problems in this field are discussed.
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Year | Venue | Keywords |
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2013 | Computer-Aided Design | periodic steady-state simulations,stochastic spectral circuit,static steady-state simulations,stochastic spectral circuit simulators,stochastic collocation scheme,stochastic processes,uncertainty quantification,integrated circuits,medium number,circuit problem,spectral analysis,stochastic spectral method,nonlinear circuit problems,uncertainty quantification algorithms,efficient stochastic circuit simulator,gpc,collocation schemes,transient steady-state simulations,non-gaussian random parameters,stochastic testing,monte carlo algorithms,monte carlo methods,generalized polynomial chaos,integrated circuit,manufacturing process variations,gaussian random parameters,galerkin method,galerkin schemes,conventional stochastic,monte carlo,finite state machines |
DocType | Volume | ISSN |
Conference | abs/1409.4824 | 1933-7760 |
ISBN | Citations | PageRank |
978-1-4799-1069-4 | 5 | 0.42 |
References | Authors | |
31 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Zhang | 1 | 125 | 12.54 |
| Ibrahim M. Elfadel | 2 | 242 | 44.16 |
| Luca Daniel | 3 | 497 | 50.96 |