Improved path clustering for adaptive path-delay testing

Adaptive path-delay testing is a testing methodology that reduces redundant test patterns based on the measured process condition of a die under test (DUT). To improve testing efficiency, process conditions are clustered into a limited number of clusters, each of which has a corresponding set of test patterns. The test pattern set of a cluster must include all potential timing-critical paths of all process conditions in the cluster. Hence, high-quality clustering is needed to minimize redundant test paths. In this paper, we propose a new clustering heuristic to minimize the expected number of redundant test paths in adaptive path-delay testing. Our experimental results on randomly generated testcases show that the proposed clustering heuristic can reduce the expected number of test paths by up to 40% compared to the previous Greedy clustering algorithm of Uezono et al. [5]. To address unique attributes of an industrial testcase obtained from the authors of [5], we integrate the dynamic-programming restricted-partitioning technique of [1], which improves the expected number of test paths by up to 5% compared to the Greedy algorithm.