Unsupervised Representation Learning for Large-Scale Wafer Maps in Micro-Electronic Manufacturing

Abstract

Recognition of wafer map defect patterns is essential for evaluating the reliability of micro-electronic manufacturing. Due to the difficulty of labeling, the available large-scale wafer maps are raw data without labeling. The scarcity of labeled samples reduces the defect pattern recognition accuracy of popular deep convolutional neural networks. To overcome this problem, we propose a wafer map deep clustering (WMDC) model. It learns generic representations from unlabeled datasets in an unsupervised manner. A prototype metric loss during training helps to learn the semantic features of the categories. We improve the recognition accuracy of the model when trained using scarce labeled data by transferring the weights of unsupervised pretraining. Experiments on WM811K and MixedWM38 wafer datasets demonstrate that the WMDC model is capable of obtaining robust prior representations from the unlabeled wafer maps. Accuracies of 97.43% and 98.74% are obtained when fine-tuning using scarce labeled data from both datasets, respectively.