Adaptive Board-Level Functional Fault Diagnosis Using Incremental Decision Trees

Board-level functional fault diagnosis is needed for high-volume production to improve product yield. However, to ensure diagnosis accuracy and effective board repair, a large number of syndromes must be used. Therefore, the diagnosis cost can be prohibitively high due to the increase in diagnosis time and the complexity of test execution and analysis. We propose an adaptive diagnosis method based on incremental decision trees (DTs). Faulty components are classified according to the discriminative ability of the syndromes in DT training. The diagnosis procedure is constructed as a binary tree, with the most discriminative syndrome as the root and final repair suggestions are available as the leaf nodes of the tree. The syndrome to be used in the next step is determined based on the observation of syndromes thus far in the diagnosis procedure. The number of syndromes required for diagnosis can be significantly reduced compared to the total number of syndromes used for system training. Moreover, online learning is facilitated in the proposed diagnosis system using an incremental version of DTs, so as to bridge the knowledge obtained at test-design stage with the knowledge gained during volume production. The diagnosis system can thus adapt to occurrences of new error scenarios on-the-fly. Diagnosis results for three complex boards from industry, currently in volume production, highlight the effectiveness of the proposed approach.