A One-Shot Learning Approach for Similarity Retrieval of Wafer Bin Maps With Unknown Failure Pattern

Abstract

Due to the increasing complexity of semiconductor manufacturing, new process failures occur and cause unknown types of defective patterns on wafer bin maps (WBMs). Similar patterns have a high probability of being induced by the identical process failure. The commonality analysis of unknown patterns can help to track down the new faults. To explore the similarity between defective WBMs, a retrieval method based on deep learning is adopted to detect WBMs with unknown failure patterns. Since there are few available WBMs with unknown patterns at the initial troubleshooting stage, more WBMs with unknown patterns are generated by morphology transformation. The deep retrieval model is trained by real and generated WBMs, which has a similar structure to the generative adversarial networks (GANs) and can create more WBMs to provide more information. Experiments on a real-world dataset have demonstrated that the proposed method has a high precision for measuring WBM similarity and retrieving similar unknown pattern wafers.