Triplet Convolutional Networks for Classifying Mixed-Type WBM Patterns with Noisy Labels

Wafer Bin Maps (WBM) frequently show various spatial failure patterns that provide crucial information for engineers to identify the root cause of failures and their consequent low yield. To shorten the root-cause diagnosis process, it is important to classify different failure patterns with high accuracy, especially when there is mixed type of failure patterns on the same wafer. The main challenges of mixed type classification in WBMs include: 1) Lack of accurately annotated real-world training dataset, 2) Imbalanced/long-tail distributions among classes, 3) Synthesized training data usually cannot reflect the practical application conditions. In this paper, we propose a weakly supervised learning approach and use an ensemble method based on triplet CNN models to classify mixed-type failure patterns in WBMs. We train the models based on the public WM-811K dataset, which is collected from real products but with only single-label annotations. We demonstrate that such models could mitigate the imbalanced class distribution and being able to learn efficiently from a weakly labeled dataset and achieve superior performances on the classification of real wafer maps with long-tail distributed mixed type failures. We also discuss the practical considerations of implementing such models and the advantages of using triplet over binary CNN models.