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 (DA) 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 DA 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 DA and the proposed workflow, the diagnosis accuracy is improved.