Knowledge Transfer in Board-Level Functional Fault Diagnosis Enabled by Domain Adaptation

High integration densities and design complexity make board-level functional fault diagnosis extremely difficult. Machine-learning techniques can identify functional faults with high accuracy, but they require a large volume of data to achieve high prediction accuracy. This drawback limits the effectiveness of traditional machine-learning algorithms for training a model in the early stage of manufacturing, when only a limited amount of fail data and repair records are available. We propose a board-level diagnosis workflow that utilizes domain adaptation to transfer the knowledge learned from mature boards to a new board in the ramp-up phase. First, based on the requirement of fault diagnosis, we select an appropriate domain-adaptation method to reduce differences between mature boards and the new board. Second, these domain adaptation methods utilize information from both the mature and the new boards with carefully designed domain-alignment rules and train a functional fault diagnosis classifier. Experimental results using three complex boards in volume production and one new board in the ramp-up phase show that, with the help of domain adaptation and the proposed workflow, the diagnosis accuracy is improved.