A new multiple weight set calculation algorithm

Abstract

The number of weighted random patterns depends on the sampling probability of the corresponding deterministic test pattern. Therefore if the weight set is extracted from the deterministic pattern set with high sampling probabilities, the test length can be shortened. In this paper we present a new multiple weight set generation algorithm that generates high performance weight sets by removing deterministic patterns with low sampling probabilities. In addition, the weight set that makes the variance of sampling probabilities for deterministic test patterns small, reduces the number of the deterministic test patterns with low sampling probability. Henceforth we present a new weight set calculation algorithm that uses the optimal candidate list and reduces the variance of the sampling probability. The results on ISCAS 85 and ISCAS 89 benchmark circuits prove the effectiveness of the new weight set calculation algorithm.