Probabilistic mixed-model fault diagnosis

Previously-proposed strategies for VLSI fault diagnosis have suffered from a variety of self-imposed limitations. Some techniques are limited to a specific fault model, and many will fail in the face of any unmodeled behavior or unexpected data. Others apply ad-hoc or arbitrary scoring mechanisms to fault candidates, making the results difficult to interpret or to compare with the results from other algorithms. This paper outlines an approach to fault diagnosis that is robust, comprehensive, extendable, and practical. By introducing a probabilistic framework for diagnostic prediction, it is designed to incorporate disparate diagnostic algorithms, different sets of data, and a mixture of fault models into a single diagnostic result. Results from diagnosis experiments on a Hewlett-Packard ASIC and FIB inserted defects are presented.