Knowledge discovery and knowledge transfer in board-level functional fault diagnosis

Diagnosis of functional failures at the board level is critical for improving product yield and reducing manufacturing cost. Reasoning techniques increase the accuracy of functional-fault diagnosis based on the history of successfully repaired boards. However, depending on the complexity of the product, it usually takes several months to accumulate an adequate database for training a reasoning-based diagnosis system. During the initial product ramp-up phase, reasoning-based diagnosis is not feasible for yield learning, since the required database is not available due to lack of volume. We propose a knowledge-discovery method and a knowledge-transfer method for facilitating board-level functional fault diagnosis. First, an analysis technique based on machine learning is used to discover knowledge from syndromes, which can be used for training a diagnosis engine. Second, knowledge from diagnosis engines used for earlier-generation products can be automatically transferred through root-cause mapping and syndrome mapping based on keywords and board-structure similarities. Two complex boards in volume production and with a mature diagnosis system, and three new boards in the ramp-up phase, are used to validate the proposed knowledge-discovery and knowledge-transfer approach in terms of the diagnosis accuracy obtained using the new diagnosis systems.