High-volume manufacturing of complex electronic products involves functional test at board level to ensure low defect escapes. Machine-learning techniques have recently been proposed for reasoning-based functional-fault diagnosis system to achieve high diagnosis accuracy. However, machine learning requires a rich set of test items (syndromes) and a sizable database of faulty boards. An insufficient number of failed boards, ambiguous root-cause identification, and redundant or irrelevant syndromes can render machine learning ineffective. We propose an evaluation and enhancement framework based on information theory for guiding diagnosis systems using syndrome and root-cause analysis. Syndrome analysis based on subset selection provides a representative set of syndromes with minimum redundancy and maximum relevance. Root-cause analysis measures the discriminative ability of differentiating a given root cause from others. The metrics obtained from the proposed framework can also provide guidelines for test redesign to enhance diagnosis. A real board from industry, currently in volume production, and an additional synthetic board, based on data extrapolated from another real board, are used to demonstrate the effectiveness of the proposed framework.
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