Data-driven models for statistical testing: measurements, estimates and residuals

This paper is the second of a three paper series on statistical analysis of deep-submicron semiconductor test data. The subjects of this paper are the models and methods for computing healthy die estimates of the test response. Production data are used to demonstrate the ideas in this paper. The conceptual skeleton for the analysis is the computed difference between the measurement and a data-driven model of the healthy response. Uni-variate and multi-variate estimates are used to show the potential of the concept. Within the framework of estimating healthy response it is shown that significant reductions of distribution variance can be obtained with a corresponding improvement in outlier detection