AI classification of wafer map defect patterns by using dual-channel convolutional neural network. (December 2021)

• Record Type: Journal Article
• Authors: Chen, Shouhong; Zhang, Yuxuan; Yi, Mulan; Shang, Yuling; Yang, Ping
• Is Part Of: Engineering failure analysis. Volume 130(2021)
• Publication Date: 2021-12
• Subjects: Wafer map -- Pattern recognition -- Two-channel DCNN -- Multi-source -- ECOC-SVM; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2021.105756
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: The systematic failure pattern recognition method for wafer map classification based on multi-source and two-channel convolutional neural network is explored. A deep convolutional neural network model is established for the problem of fault pattern recognition of wafer map. We detect the modeling way to enhance the reliability and efficiency of fault pattern recognition for wafer map. It implies that we can improve the reliability and efficiency of fault pattern recognition for wafer map by proposed modeling method and promote the efficiency of semiconductor manufacturing processes for electronic devices. Abstract: In semiconductor manufacturing, the presence of abnormal chips in wafer products will reduce the yield of wafer products. The identification of the wafer map can find related problems in the wafer production process, and post-analysis is a necessary means to improve the wafer yield. In this study, we proposed the method that image-based multi-source and two-channel convolutional neural network for wafer map classification. This method consists of the following three main steps. First, using two different deep convolution neural networks (DCNN) to form a two-channel DCNN feature extraction model and extracting multiple sets of advanced features from multi-source data. Second, processing the multi-group data features extracted from different channels to obtain two sets of new multi-source features. Third, the multi-source features in the two channels are further processed to become a new set of classification features. Then input the new features into the combined classification model of error correction code and support vector machine (ECOC-SVM) to classify the defect pattern of the wafer map. The data used in the experiment comes from data sets in actual production (WM-811K). The experimental results showed that the proposed method has a good effect on the defect pattern recognition of wafer maps, and the effectiveness of the proposed method is confirmed. There are a total of 33, 256 wafer maps, and the overall classification accuracy of 6552 test sets has reached 96.4%.