Hidden Markov models with patterns and their application to integrated circuit testing

Abstract

We present a new model, derived from classical Hidden Markov Models (HMMs), to learn sequences of large Boolean vectors. Our model - Hidden Markov Model with Patterns, or HMMP - differs from HMM by the fact that it uses patterns to define the emission probability distributions attached to the states. We also present an efficient state merging algorithm to learn this model from training vector sequences. This model and our algorithm are applied to learn Boolean vector sequences used to test integrated circuits. The learned HMMPs are used as test sequence generators. They achieve very high fault coverage, despite their reduced size, which demonstrates the effectiveness of our approach.