Statistical Techniques for Predicting System-Level Failure using Stress-Test Data

Abstract

In this paper we describe a novel scheme for collecting and analyzing a chip\'s failure signature. Incorrect outputs of digital chips are forced by applying scan patterns under non-destructive stress conditions. From binary mismatch responses collected in continue-on-fail mode, numeric data features are formed by grouping and counting mismatches in each group, thus defining a chip\'s "analog" failure signature. We use machine learning to explore prediction models of system-level test (SLT) failures by comparing signatures of chip samples from known SLT pass/fail bins. Important features that clearly separate the SLT pass/fail chips are identified. Experimental results are presented for a 28-nm 1.2-GHz quad-core low-power processor.