Title :
Board-Level Functional Fault Diagnosis Using Artificial Neural Networks, Support-Vector Machines, and Weighted-Majority Voting
Author :
Fangming Ye ; Zhaobo Zhang ; Chakrabarty, Krishnendu ; Xinli Gu
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
Abstract :
Increasing integration densities and high operating speeds lead to subtle manifestation of defects at the board level. Functional fault diagnosis is, therefore, necessary for board-level product qualification. However, ambiguous diagnosis results lead to long debug times and even wrong repair actions, which significantly increase repair cost and adversely impact yield. Advanced machine-learning (ML) techniques offer an unprecedented opportunity to increase the accuracy of board-level functional diagnosis and reduce high-volume manufacturing cost through successful repair. We propose a smart diagnosis method based on two ML classification models, namely, artificial neural networks (ANNs) and support-vector machines (SVMs) that can learn from repair history and accurately localize the root cause of a failure. Fine-grained fault syndromes extracted from failure logs and corresponding repair actions are used to train the classification models. We also propose a decision machine based on weighted-majority voting, which combines the benefits of ANNs and SVMs. Three complex boards from the industry, currently in volume production, and additional synthetic data, are used to validate the proposed methods in terms of diagnostic accuracy, resolution, and quantifiable improvement over current diagnostic software.
Keywords :
application specific integrated circuits; electronic engineering computing; fault diagnosis; integrated circuit testing; learning (artificial intelligence); neural nets; support vector machines; ANN; SVM; artificial neural network; board-level functional fault diagnosis; board-level product qualification; classification model; decision machine; failure logs; fine-grained fault syndrome; high-volume manufacturing cost; machine-learning; smart diagnosis method; support-vector machine; volume production; weighted-majority voting; Artificial neural networks; Circuit faults; Fault diagnosis; Maintenance engineering; Neurons; Training; Vectors; Board-level; diagnosis; functional failure; machine learning (ML); neural networks; support-vector machines (SVMs);
Journal_Title :
Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on
DOI :
10.1109/TCAD.2012.2234827