Abstract
Visual defect inspection and classification are significant steps of most manufacturing processes in the semiconductor and electronics industries. Known and unknown defects on wafer maps tend to cluster, and these spatial patterns provide valuable process information for supporting manufacturing in determining the root causes of abnormal processes. In previous studies, data augmentation-based deep learning (DL) techniques were most commonly used for the identification of wafer map defect patterns (WMDP). Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations were manually designed for the WMDP problem. In this study, we propose a DL-based method with automatic data augmentation for the WMDP task. Basically, it focuses on learning effective discriminative features, from wafer maps, through a deep network structure. The network consists of a convolution-based variational autoencoder (CVAE) sequentially. First, we pre-trained the CVAE on large training data in an unsupervised manner. Second, we fine-tuned the encoder of the CVAE, which was followed by a neural network (NN) classifier, in a supervised manner. Additionally, we describe a simple procedure for automatically searching for improved data augmentation policies. The policy mainly consists of five image processing functions: rotation, flipping, shifting, shearing range, and zooming. The effectiveness of the proposed method was demonstrated through experimental results obtained from a simulation dataset and a real-world wafer map dataset (WM-811K). This study provides guidance for the application of deep learning in semiconductor manufacturing processes to improve product quality and yield.
Highlights
In conjunction with the fourth industrial revolution, the semiconductor market has been expanding rapidly [1]–[3]
convolutional neural network (CNN) is the basic technique adopted in the identification tasks of wafer map defect patterns, and data augmentation techniques are generally used for data imbalance problems
In this study, we developed a deep learning (DL)-based method, that is, convolution-based variational autoencoder (CVAE) for wafer map defect patterns (WMDP), which employs CNN as a feature extractor, and CVAE exploits the full connection between the features and the subsequent convolved images in an unsupervised manner
Summary
In conjunction with the fourth industrial revolution, the semiconductor market has been expanding rapidly [1]–[3]. As semiconductor manufacturing becomes complicated, and the difficulty of the refined process techniques increases, a new type of wafer defect map appears. There are various types of defect patterns based on the manufacturing methods or features of abnormal unit processes These defect patterns can be detected using wafer-map data from the test step of a wafer. It is necessary to develop a model that recognizes a new types of defective wafer map pattern Another common problem in many data-oriented realworld semiconductor applications is class-imbalance [17]. We consider the data imbalance problem by developing a deep learning-based method It automatically classifies wafer map defect patterns without manual data augmentation or feature extraction. We proposed an automatic classification method that employs deep learning techniques, such as CNN and VAE, for wafer map defect patterns without manual data augmentation and feature engineering.
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