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

Abstract

Diagnosis of functional failures at the board level is critical for improving product yield and reducing manufacturing cost. Reasoning techniques increase the accuracy of functionalfault 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 boardstructure 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 knowledgediscovery and knowledge-transfer approach in terms of the diagnosis accuracy obtained using the new diagnosis systems.

Transferring knowledge from universities to organizations by business students

“…Tho and Trang (2015) used the data of 843 in-service training business students in Vietnam and reported that innovative culture and intrinsic motivation affect the KT process from business universities to business organizations. Ye et al (2014) studied the effect of culture on knowledge sharing by using the data of multinational virtual classes and reported the culture and its dimensional effect and the knowledge-sharing behavior. Lee and Li (2016) reported that the KT process fosters the innovative culture and similarly the innovative culture of the organization encourages the KT.…”

Information-Theoretic Syndrome Evaluation, Statistical Root-Cause Analysis, and Correlation-Based Feature Selection for Guiding Board-Level Fault Diagnosis

“…The problem of inadequate database of failing boards can only be fully addressed by larger database of failing boards. To address the problem of inadequate database, Ye et al [23] proposed a knowledge transfer method to extend the knowledge database for diagnosis-system training at the ramp-up stage of volume production.…”