A CNN-based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing

In this paper, we focus on the defect analysis task that requires engineers to identify the causes of yield reduction from defect classification results. We organize defect analysis work for yield quality control falls roughly into three phases: defect classification, defect trend monitoring and detailed classification. To support the 1st and 3rd engineer’s analysis work, we use a convolutional neural network based transfer learning method to automatic defect classification. We conclude that the proposed method can classify defect images with high accuracy while saving labor equivalent to one-third of the labor required for manual inspection work.