Journal Article10.1007/S10845-020-01540-X
Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes
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TL;DR: Without any specific preprocessing, high-level features are extracted from convolutional neural network and then the extracted features are fed to combination of error-correcting output codes and support vector machines for wafer map defect pattern classification.
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Abstract: Defect clusters on the wafer map can provide important clue to identify the process failures so that it is important to accurately classify the defect patterns into corresponding pattern types. In this research, we present an image-based wafer map defect pattern classification method. The presented method consists of two main steps: without any specific preprocessing, high-level features are extracted from convolutional neural network and then the extracted features are fed to combination of error-correcting output codes and support vector machines for wafer map defect pattern classification. To the best of our knowledge, no prior work has applied the presented method for wafer map defect pattern classification. Experimental results tested on 20,000 wafer maps show the superiority of presented method and the overall classification accuracy is up to 98.43%.
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Citations
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.
Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification
TL;DR: An ensemble convolutional neural network (ECNN) framework is proposed for WBM pattern classification, in which a weighted majority function is adopted to select higher weights for the base classifiers that have higher predictive performance.
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Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels
TL;DR: Zhang et al. as discussed by the authors used explainable artificial intelligence techniques to analyze the predicted results of the defect image classification model, which is considered as a black-box model, to produce human-understandable results.
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A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification
Hyun-Gu Kang,Seokho Kang +1 more
TL;DR: This study presents a hybrid method that leverages the advantages of both approaches to improve the classification accuracy and demonstrates the effectiveness of the proposed method using real-world data from a semiconductor manufacturer.
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