Fault Diagnosis With Convolutional Compactors

This paper presents new nonadaptive fault-diagnosis techniques for scan-based designs. They guarantee accurate and time-efficient identification of failing scan cells based on results of convolutional compaction of test responses. The essence of the method is to use a branch-and-bound algorithm to narrow the set of scan cells down to certain sites that are most likely to capture faulty signals. This search is guided by a number of heuristics and self-learned information used to accelerate the diagnosis process for the subsequent test patterns. A variety of experimental results for benchmark circuits, industrial designs, and real fail logs confirm the feasibility of the proposed approach even in the presence of unknown states. The scheme remains consistent with a single test session scenario and allows high-volume in-production diagnosis.