Handling Missing Syndromes in Board-Level Functional-Fault Diagnosis

Functional fault diagnosis is widely used in board manufacturing to ensure product quality and improve product yield. Advanced machine-learning techniques have recently been advocated for reasoning-based diagnosis, these technologies are based on historical data of successfully repaired boards. However, traditional diagnosis systems fail to provide appropriate repair suggestions when the diagnostic logs are fragmented and some error outcomes, or syndromes, are not available during diagnosis. We describe the design of a diagnosis system, based on support vector machines, that can handle missing syndromes by using the method of imputation. Several imputation methods are discussed and compared in terms of their efficiency in handling missing syndromes. Two large-scale synthetic data sets generated from the log information of complex industrial boards in volume production are used to validate the proposed diagnosis system in terms of diagnosis accuracy and training time.