Uncertainty Quantification for Integrated Circuits: Stochastic Spectral Methods

TL;DR

This paper reviews recent stochastic spectral methods, especially generalized polynomial chaos, for efficiently quantifying uncertainties in integrated circuit performance caused by manufacturing variations.

Contribution

It highlights recent advances in stochastic spectral circuit simulators, including stochastic testing, Galerkin, and collocation schemes, applied to various circuit simulation scenarios.

Findings

Stochastic spectral methods outperform Monte Carlo in speed for small to medium parameter sets.

The paper demonstrates the application of these methods to static, transient, and steady-state circuit simulations.

Open problems and future directions in the field are discussed.

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.