Stochastic Testing Simulator for Integrated Circuits and MEMS: Hierarchical and Sparse Techniques

TL;DR

This paper introduces a fast hierarchical stochastic spectral simulator for integrated circuits and MEMS, significantly reducing simulation time compared to Monte Carlo methods and identifying key variation sources affecting performance.

Contribution

The paper develops a hierarchical stochastic spectral simulator and an anchored ANOVA approach, offering faster and more insightful analysis of process variations in complex systems.

Findings

Achieves hundreds to thousands times speedup over Monte Carlo

Effectively identifies dominant variation sources

Demonstrates accuracy and efficiency on circuit and MEMS examples

Abstract

Process variations are a major concern in today's chip design since they can significantly degrade chip performance. To predict such degradation, existing circuit and MEMS simulators rely on Monte Carlo algorithms, which are typically too slow. Therefore, novel fast stochastic simulators are highly desired. This paper first reviews our recently developed stochastic testing simulator that can achieve speedup factors of hundreds to thousands over Monte Carlo. Then, we develop a fast hierarchical stochastic spectral simulator to simulate a complex circuit or system consisting of several blocks. We further present a fast simulation approach based on anchored ANOVA (analysis of variance) for some design problems with many process variations. This approach can reduce the simulation cost and can identify which variation sources have strong impacts on the circuit's performance. The simulation results of some circuit and MEMS examples are reported to show the effectiveness of our simulator