Analysis of error-masking and X-masking probabilities for convolutional compactors

Convolutional compactors offer a promising technique of compacting test responses that include unknown values. One drawback of this compaction technique is error masking, i.e., some errors appearing in the test responses cannot be detected due to mutual cancellation. In this work, we theoretically analyze error-masking probability. First, we apply weight distributions of binary linear error-correcting codes to derive the error-masking probability. We then present a fast calculation scheme for 4- and 6-error-masking probabilities. Numerical examples reveal that they are about the same as those obtained by Monte-Carlo simulations. We also analyze X-masking probability, that is, the probability that an error is masked by unknown values. We present tree-search-based calculation, as well as approximated value