Functional fault diagnosis is necessary for board-level product qualification. However, ambiguous diagnosis results can lead to long debug times and wrong repair actions, which significantly increase repair cost and adversely impact yield. A state-of-the-art functional fault diagnosis system involves several key components: (1) design of functional test programs, (2) collection of functional-failure syndromes, (3) building of the diagnosis engine, (4) isolation of root causes, and (5) evaluation of the diagnosis engine. Advances in each of these components can pave the way for a more effective diagnosis system, thus improving diagnosis accuracy and reducing diagnosis time. Machine-learning and data analysis techniques offer an unprecedented opportunity to develop an automated and adaptive diagnosis system to increase diagnosis accuracy and reduce diagnosis time. This paper describes how all the above components of an advanced diagnosis system can benefit from machine learning and information theory. Topics discussed include incremental learning, decision trees, root-cause analysis and evaluation metrics, data acquisition, and knowledge transfer.
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