Diagnosis of functional failures at the board level is critical for improving product yield and reducing manufacturing cost. State-of-the-art board-level diagnostic software is unable to cope with high complexity and ever-increasing clock frequencies, and the identification of the root cause of failure on a board is a major problem today. Ambiguous or incorrect repair suggestions lead to long debug times and even wrong repair actions, which significantly increases the repair cost and adversely impacts yield. We propose a smart diagnosis method based on artificial neural networks that can learn from repair history and accurately localize the root cause of a failure. Fine-grained fault syndromes extracted from failure logs and the corresponding repair actions are used to train the neural network. The proposed network structure is simple, it can be rapidly trained, and it is scalable to large datasets. Moreover, the relationship between typical syndromes and the most appropriate repair actions can be easily inferred from the network structure. An industrial board, which is currently in production, is used to validate the diagnosis approach in terms of diagnostic accuracy, resolution, and quantifiable improvement over current diagnostic software.
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