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 …

Abstract Reasoning-based methods have recently become popular since they overcome the
knowledge-acquisition bottleneck during volume production and they can automatically
generate an intelligent diagnostic system from existing resources [1, 2]. However,
knowledge acquisition is a major problem for a reasoning-based method at the initial
product ramp-up stage. Machine learning-based reasoning requires an adequate database
for training the reasoning engine, and such a database becomes available much later in the …