Active Learning of Convolutional Neural Network for Cost-Effective Wafer Map Pattern Classification

Wafer maps provide important information for engineers for detecting root causes of failure in a semiconductor manufacturing process. Thus, there has been active research into the automation of wafer map pattern classification. With recent advances in deep learning, a convolutional neural network (CNN) has yielded state-of-the-art performance in wafer map pattern classification. Because a large amount of labeled training data is required, experienced engineers need to annotate large quantities of wafer maps manually which is costly. To construct a well-performing CNN model with a lower labeling cost, we propose a cost-effective wafer map pattern classification system based on the active learning of a CNN. In the system, a CNN model is constructed based on four main steps: uncertainty estimation, query wafer selection, query wafer labeling, and model update. By repetitively performing these steps, the performance of the CNN model is gradually and effectively increased. We compared several methods for uncertainty estimation and query wafer selection in our system. We demonstrated the effectiveness of the proposed system through experiments using real-world data from a semiconductor manufacturer.