Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos
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1. stochastic testing.pdf
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Author(s)
Elfadel, Ibrahim M.
Zhang, Zheng
El Moselhy, Tarek Ali
Daniel, Luca
Date Issued
September 2013
Journal
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhang, Zheng; El-Moselhy, Tarek A.; Elfadel, Ibrahim M. and Daniel, Luca. "Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 32, no. 10 (October 2013): 1533-1545. © 2013 Institute of Electrical and Electronics Engineers (IEEE)
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Author's final manuscript
Abstract
Uncertainties have become a major concern in integrated circuit design. In order to avoid the huge number of repeated simulations in conventional Monte Carlo flows, this paper presents an intrusive spectral simulator for statistical circuit analysis. Our simulator employs the recently developed generalized polynomial chaos expansion to perform uncertainty quantification of nonlinear transistor circuits with both Gaussian and non-Gaussian random parameters. We modify the nonintrusive stochastic collocation (SC) method and develop an intrusive variant called stochastic testing (ST) method. Compared with the popular intrusive stochastic Galerkin (SG) method, the coupled deterministic equations resulting from our proposed ST method can be solved in a decoupled manner at each time point. At the same time, ST requires fewer samples and allows more flexible time step size controls than directly using a nonintrusive SC solver. These two properties make ST more efficient than SG and than existing SC methods, and more suitable for time-domain circuit simulation. Simulation results of several digital, analog and RF circuits are reported. Since our algorithm is based on generic mathematical models, the proposed ST algorithm can be applied to many other engineering problems.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1109/TCAD.2013.2263039
