Wafer Map Defect Patterns Classification using Deep Selective Learning

With the continuous drive toward integrated circuits scaling, efficient yield analysis is becoming more crucial yet more challenging. In this paper, we propose a novel methodology for wafer map defect pattern classification using deep selective learning. Our proposed approach features an integrated reject option where the model chooses to abstain from predicting a class label when misclassification risk is high. Thus, providing a trade-off between prediction coverage and misclassification risk. This selective learning scheme allows for new defect class detection, concept shift detection, and resource allocation. Besides, and to address the class imbalance problem in the wafer map classification, we propose a data augmentation framework built around a convolutional auto-encoder model for synthetic sample generation. The efficacy of our proposed approach is demonstrated on the WM-811k industrial dataset where it achieves 94% accuracy under full coverage and 99% with selective learning while successfully detecting new defect types.