Study on Expansion of Convolutional Compactors over Galois Field

Abstract

Convolutional compactors offer a promising technique of compacting test responses. In this study we expand the architecture of convolutional compactor onto a Galois field in order to improve compaction ratio as well as reduce X-masking probability, namely, the probability that an error is masked by unknown values. While each scan chain is independently connected by EOR gates in the conventional arrangement, the proposed scheme treats q signals as an element over GF(2^q), and the connections are configured on the same field. We show the arrangement of the proposed compactors and the equivalent expression over GF(2). We then evaluate the effectiveness of the proposed expansion in terms of X-masking probability by simulations with uniform distribution of X-values, as well as reduction of hardware overheads. Furthermore, we evaluate a multi-weight arrangement of the proposed compactors for non-uniform X distributions.