Board-level fault diagnosis using Bayesian inference

Increasing integration densities and high operating speeds are leading to subtle manifestations of defects at the board level. Board-level functional test is therefore necessary for product qualification. The diagnosis of functional failures is especially challenging, and the cost associated with board-level diagnosis is escalating rapidly. An effective and cost-efficient board-level diagnosis strategy is needed to reduce manufacturing cost and time-to-market, as well as to improve product quality. In this paper, we use Bayesian inference to develop a new board-level diagnosis framework that allows us to identify faulty devices or faulty modules within a device on a failing board with high confidence. Bayesian inference offers a powerful probabilistic method for pattern analysis, classification, and decision making under uncertainty. We apply this inference technique by first generating a database of fault syndromes obtained using fault-insertion test at the module pin level on a fault-free board, and then use this database along with the observed erroneous behavior of a failing board to infer the most likely faulty device. Results on a case study using an open-source RISC system-on-chip highlight the effectiveness of the proposed framework in terms of fault-localization accuracy and correctness of diagnosis.