Journal Article10.1109/TIE.2020.3013492
Two-Dimensional Principal Component Analysis-Based Convolutional Autoencoder for Wafer Map Defect Detection
Jianbo Yu,Jiatong Liu +1 more
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TL;DR: A novel deep neural network (DNN), two-dimensional principal component analysis-based convolutional autoencoder (PCACAE) for wafer map defect recognition and the experimental results demonstrate that PCACAE is superior to other well-known Convolutional neural networks on WMPR.
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Abstract: Due to the high complexity and dynamics of the semiconductor manufacturing process, various process abnormality could result in wafer map defects in many work stations. Thus, wafer map pattern recognition (WMPR) in the semiconductor manufacturing process can help operators to troubleshoot root causes of the out-of-control process and then accelerate the process adjustment. This article proposes a novel deep neural network (DNN), two-dimensional principal component analysis-based convolutional autoencoder (PCACAE) for wafer map defect recognition. First, a new convolution kernel based on conditional two-dimensional principal component analysis is developed to construct the first convolutional block of PCACAE. Second, a convolutional autoencoder is cascaded by considering the nonlinearity of data representation. The second convolutional block of PCACAE is constructed based on the encoding part. Finally, the pretrained PCACAE is fine-tuned to obtain the final classifier. PCACAE is successfully applied for feature learning and recognition of wafer map defects. The experimental results on a real-world case demonstrate that PCACAE is superior to other well-known convolutional neural networks (e.g., GoogLeNet, PCANet) on WMPR.
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Citations
Defect-aware transformer network for intelligent visual surface defect detection
TL;DR: Wang et al. as mentioned in this paper proposed defect-aware Transformer network (DAT-Net), which replaces convolution in encoder to overcome the difficulty of modeling long-range dependencies.
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Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review
Tong-Kyu Kim,Kamran Behdinan +1 more
TL;DR: A comprehensive review on the advancement of machine learning and deep learning applications for wafer map defect recognition and classification to enhance performance, overall yield, and cost-efficiency is provided.
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A Systematic Review of Deep Learning for Silicon Wafer Defect Recognition
Uzma Batool,Mohd Ibrahim Shapiai,Muhammad Tahir,Zool Hilmi Ismail,N. J. Zakaria,Ahmed Elfakharany +5 more
TL;DR: In this article, the authors present a review of the deep learning methods employed for wafer map defect recognition, which are grouped as supervised learning, unsupervised learning, and hybrid learning.
MS-SSPCANet: A powerful deep learning framework for tool wear prediction
TL;DR: Wang et al. as mentioned in this paper proposed a multi-scale stacked sparse principal component analysis network (MS-SSPCANet) to predict tool wear, which combines global averaging and spatial pyramid pooling layers.
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A TOPSIS-Based Relocalization Algorithm in Wireless Sensor Networks
TL;DR: An algorithm that can simultaneously screen drifting beacon nodes and malicious beacon nodes is proposed and Experimental results illustrate that the detection accuracy of drifting beacon node and malicious Beacon nodes of the proposed algorithm is 7.5% and 8.2% higher than that of the state-of-the-art algorithms, respectively.
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