On test syndrome merging for reasoning-based board-level functional fault diagnosis

Machine learning algorithms are advocated for automated diagnosis of board-level functional failures due to the extreme complexity of the problem. Such reasoning-based solutions, however, remain ineffective at the early stage of the product cycle, simply because there are insufficient historical data for training the diagnostic system that has a large number of test syndromes. In this paper, we present a novel test syndrome merging methodology to tackle this problem. That is, by leveraging the domain knowledge of the diagnostic tests and the board structural information, we adaptively reduce the feature size of the diagnostic system by selectively merging test syndromes such that it can effectively utilize the available training cases. Experimental results demonstrate the effectiveness of the proposed solution.