A Diagnosability Metric for Test Set Selection Targeting Better Fault Detection

Diagnosis is the methodology to identify the reason behind the failure of manufactured chips. This is particularly important from the yield enhancement viewpoint. The primary focus of a diagnosis algorithm is to accurately narrow down the list of suspected candidates. But for any diagnosis algorithm, the effectiveness will depend on the test set in use. If the test set used is not good enough to distinguish between fault pairs, the diagnosis algorithm can never be able to distinguish between a good number of faults. This problem leads us to find a metric which can characterize test sets in terms of their diagnostic power. In literature, several methods have been proposed for assessment of the diagnostic power of a test set. Though the methods are accurate in nature, the bottleneck is the space and time complexity. Thus, given a number of test sets (with same fault coverage) for a circuit, it is very difficult to select one of them for better diagnosis. In this paper, we have proposed a probability based approach to find out a metric to describe diagnostic power of a test set. We call this metric, the diagnosibility of the test set for a given circuit. Our method uses almost 99% less space compared to the proposed methods and is well accurate.