Predictive subset testing for IC performance

Abstract

Predictive subset testing is based on a statistical model of parametric process variation. In this Monte Carlo approach, a statistical process simulation, coupled with circuit simulation, is used to determine the joint probability distribution of a set of circuit performances. By evaluating the joint probability distribution, rather than assuming the performances to be independent, correlations that exist between them can be exploited and the number of performances that need to be explicitly tested can be reduced. Once a subset of performances for explicit testing has been identified, regression models for the untested performances are constructed, and, from the confidence intervals, limits are assigned for the tested performances. In this manner, the values of the untested performances can be predicted, reducing test complexity and cost.<>