Efficient Board-Level Functional-Fault Diagnosis with Missing Syndromes

Jin, S

Ye, F

Zhang, Z

Chakrabarty, K

Gu, X

2015-07-01T19:10:49Z

2015-07-01T19:10:49Z

2015-07-01

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.

This research was supported by a grant from Huawei Technologies Co. Ltd.

https://hdl.handle.net/10161/10244

en_US

Institute of Electrical and Electronics Engineers (IEEE)

ECE-2015;03

Board-level fault diagnosis, machine learning, missing syndromes, statistical reasoning

Efficient Board-Level Functional-Fault Diagnosis with Missing Syndromes

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