Joint Feature and Label Adversarial Network for Wafer Map Defect Recognition

Abstract

Deep neural networks (DNNs), e.g., convolutional neural network (CNN), are able to learn effective features from wafer maps for dimensional reduction and feature extraction. However, very large image data are needed to train DNNs to obtain high generalization performance. It is still a difficult task due to the lack of sufficient labeled images with various defects. This article proposes a semisupervised deep transfer learning algorithm called joint feature and label adversarial network (JFLAN). JFLAN uses CNNs to extract transferable features of wafer maps and then introduces a multilayer domain adaptation and pseudolabel learning block based on the generative adversarial network (GAN). This effectively reduces the distribution discrepancy and the among-class distance of the transferable features. Finally, JFLAN transfers knowledge from wafer image source data collected offline and then achieves the goal of significantly improved accuracy of wafer defect recognition and realizes online adaptive defect recognition. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The defect recognition on wafer maps plays a key role to recognize fault sources of the semiconductor manufacturing processes. Transfer learning is able to use the existing labeled data to assist in the classification of unlabeled data and is very effective to solve the problem of small samples and nonstationary generalization errors. In particular, the infusion of adversarial learning in transfer learning will provide a new idea for deep feature learning. This article provides a novel method based on transfer learning to implement wafer map defect recognition (WMDR) to quickly identify defect root causes for yield enhancement. This article provides a novel way for quality control of semiconductor manufacturing processes based on transfer and adversarial learning.