Stochastic behavioral modeling and analysis for analog/mixed-signal circuits

Abstract

It has become increasingly challenging to model the stochastic behavior of analog/mixed-signal (AMS) circuits under large-scale process variations. In this paper, a novel moment-matching based method has been proposed to accurately extract the probabilistic behavioral distributions of AMS circuits. This method first utilizes Latin Hypercube Sampling (LHS) coupling with a correlation control technique to generate a few samples (e.g., sample-size is in linear with number of variable parameters) and further analytically evaluate the high-order moments of the circuit behavior with high accuracy. In this way, the “arbitrary” probabilistic distributions of the circuit behavior can be extracted using moment-matching method. More importantly, the proposed method has been successfully applied to high-dimensional prob-lems with linear complexity. The experiments demonstrate that the proposed method can provide up to 1666X speedup over crude Monte Carlo method for the same accuracy.