Statistical defect-detection analysis of test sets using readily-available tester data

Authors:
Xiaochun Yu;R. D. (Shawn) Blanton
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
Proceedings of the International Conference on Computer-Aided Design
Year:
2011

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Abstract

At substantial cost, conventional methods for evaluating test quality apply a specially-generated test set to a large population of manufactured chips. In contrast, a new time-efficient framework for evaluating test quality (FETQ) that uses tester data from normal production has been developed and validated. FETQ estimates the quality of both static and adaptive test metrics, where the latter guides test using the results of statistical data analysis. FETQ is innovative since instead of evaluating a single measure of effectiveness (e.g., number of unique defects detected), it provides a confidence interval of effectiveness based on the analysis of a collection of test sets. FETQ is demonstrated by measuring the chip-detection capability of several static and adaptive test metrics using tester data from actual ICs.