Uncertainty quantification for integrated circuits: stochastic spectral methods
Zheng Zhang,Ibrahim M. Elfadel,Luca Daniel +2 more
- 18 Nov 2013
- pp 803-810
TL;DR: The recent advances of stochastic spectral circuit simulators based on generalized polynomial chaos (gPC) 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.
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Abstract: 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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Citations
Uncertainty quantification of silicon photonic devices with correlated and non-Gaussian random parameters.
TL;DR: An efficient numerical technique based on stochastic collocation to simulate silicon photonics with correlated and non-Gaussian random parameters and can be applied to a large class of photonic design cases as well as to many other engineering problems.
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Stochastic Testing Simulator for Integrated Circuits and MEMS: Hierarchical and Sparse Techniques
Zheng Zhang,Xiu Yang,Giovanni Marucci,Paolo Maffezzoni,Ibrahim,M. Elfadel,George Em Karniadakis,Luca Daniel +7 more
TL;DR: A fast simulation approach based on anchored ANOVA (analysis of variance) for some design problems with many process variations can reduce the simulation cost and can identify which variation sources have strong impacts on the circuit's performance.
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