Predictive subset testing: optimizing IC parametric performance testing for quality, cost, and yield

Abstract

A formal methodology for IC parametric performance testing, called predictive subset testing, is presented. It is based on a statistical model of parametric process variation. In this Monte-Carlo-based approach, a statistical process simulation is used together with circuit simulation 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 are used to reduce the number of performances that need to be explicitly tested. Once a subset of performances for explicit testing has been identified, regression models are constructed for the untested performances, and from the confidence intervals test limits are assigned for the tested performances. In this manner, the values of the untested performances within desired quality levels are predicted, reducing test complexity and cost.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>