Article2016

Sparse Linear Regression (SPLINER) Approach for Efficient Multidimensional Uncertainty Quantification of High-Speed Circuits

68 citations0/0 files storedIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

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Probabilistic and Robust Engineering Design Low-power high-performance VLSI design Advanced Multi-Objective Optimization Algorithms

Abstract

This paper presents a novel linear regression-based polynomial chaos (PC) approach for the efficient multidimensional uncertainty quantification of general distributed and lumped high-speed circuit networks. The key feature of this paper is the development of a modified Fedorov search algorithm based on the D -optimal criterion that expeditiously locates a highly sparse set of nodes within the multidimensional random space where the original network needs to be probed. Specifically, the number of selected nodes is kept equal to the number of unknown PC coefficients of the network response, thereby making this approach substantially more efficient than the conventional linear regression approach which is based on an oversampling methodology. Additionally, due to the D -optimal criterion, this approach ensures highly accurate recovery of the PC coefficients. The validity of this paper is established through multiple numerical examples.

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References

2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations 1972Theory of Optimal Experiments.2010Numerical Methods for Stochastic Computations: A Spectral Method Approach 2009Recent Advances in Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Analysis and Design 2013Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos 1992A review of some exchange algorithms for constructing discrete D-optimal designs 2011Stochastic Analysis of Multiconductor Cables and Interconnects 2015Generalized Decoupled Polynomial Chaos for Nonlinear Circuits With Many Random Parameters

Cited by

2016Big-Data Tensor Recovery for High-Dimensional Uncertainty Quantification of Process Variations 2018Stochastic Collocation With Non-Gaussian Correlated Process Variations: Theory, Algorithms, and Applications 2016Multidimensional Uncertainty Quantification of Microwave/RF Networks Using Linear Regression and Optimal Design of Experiments 2017Accurate Reduced Dimensional Polynomial Chaos for Efficient Uncertainty Quantification of Microwave/RF Networks 2021A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression

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