Abstract
Functional fault diagnosis is widely used in board manufacturing to ensure product
quality and improve product yield. Advanced machine-learning techniques have recently
been advocated for reasoning-based diagnosis; these techniques are based on the historical
record of successfully repaired boards. However, traditional diagnosis systems fail
to provide appropriate repair suggestions when the diagnostic logs are fragmented
and some error outcomes, or syndromes, are not available during diagnosis. We describe
the design of a diagnosis system that can handle missing syndromes and can be applied
to four widely used machine-learning techniques. Several imputation methods are discussed
and compared in terms of their effectiveness for addressing missing syndromes. Moreover,
a syndrome-selection technique based on the minimumredundancy-maximum-relevance (mRMR)
criteria is also incorporated to further improve the efficiency of the proposed methods.
Two large-scale synthetic data sets generated from the log information of complex
industrial boards in volume production are used to validate the proposed diagnosis
system in terms of diagnosis accuracy and training time.
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