Computer-aided fault to defect mapping (CAFDM) for defect diagnosis

Defect diagnosis in random logic is currently clone using the stuck-at fault model, while most defects seen in manufacturing result in bridging faults. In this work we use physical design and test failure information combined with bridging and stuck-at fault models to localize defects in random logic. We term this approach computer-aided fault to defect mapping (CAFDM). We build on top of the existing mature stuck-at diagnosis infrastructure. The performance of the CAFDM software was tested by injecting bridging faults into samples of a streaming audio controller chip and comparing the predicted defect locations and layers with the actual values. The correct defect location and layer was predicted in all 9 samples for which scan-based diagnosis could be performed. The experiment Mns repeated on production samples that failed scan test, with promising results.