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Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes

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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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Abbreviations

CNN:

Convolutional neural networks

ECOC:

Error-correcting output codes

SVM:

Support vector machines

CNN-SVM:

SVM classification based on CNN features

OPTICS:

Ordering point to identify the cluster structure

CART:

Classification and regression trees

NB:

Naive Bayes

kNN:

k-nearest neighbors

ReLU:

Rectified linear unit

LDA:

Linear discriminant analysis

LOGISTIC:

Logistic regression

CNN-ECOC-X:

Use CNN features for ECOC classification where X is used as binary classifiers

ANOVA:

Analysis of variance

SVE:

Soft voting ensemble

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Acknowledgements

Most of this work was done when the first author was at BISTel. This work was supported by the World Class 300 Project (R&D) (S2641209, “Development of next generation intelligent Smart manufacturing solution based on AI & Big data to improve manufacturing yield and productivity”) of the MOTIE, MSS(Korea) and supported by the National Natural Science Foundation of China (Grant Nos. 61702324 and 61911540482) in People’s Republic of China.

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Authors and Affiliations

  1. ENN Research Institute of Digital Technology, Beijing, China

    Cheng Hao Jin

  2. BISTel, Seoul, 06754, South Korea

    Hyun-Jin Kim

  3. School of Medicine, Nankai University, Tianjin, China

    Yongjun Piao

  4. College of Information Engineering, Shanghai Maritime University, Shanghai, China

    Meijing Li

  5. Department of Computer Science, Chungbuk National University, Cheongju, 28644, South Korea

    Minghao Piao

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  1. Cheng Hao Jin
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  2. Hyun-Jin Kim
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  3. Yongjun Piao
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  4. Meijing Li
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  5. Minghao Piao
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Correspondence to Minghao Piao.

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Jin, C.H., Kim, HJ., Piao, Y. et al. Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes. J Intell Manuf 31, 1861–1875 (2020). https://doi.org/10.1007/s10845-020-01540-x

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