Spatio-temporal wafer-level correlation modeling with progressive sampling: A pathway to HVM yield estimation

Abstract

Wafer-level spatial correlation modeling of probetest measurements has been explored in the past as an avenue to test cost and test time reduction. In this work, we first improve the accuracy of a popular Gaussian process-based wafer-level spatial correlation method through two key enhancements: (i) confidence estimation-based progressive sampling, and, (ii) inclusion of spatio-temporal features for inter-wafer trend learning. We then explore a new application of the enhanced correlation modeling method in estimating High Volume Manufacturing (HVM) yield from a small set of early wafers and we demonstrate its effectiveness on a large set of actual industrial test data.