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Deep Convolutional Neural Networks with Residual Blocks for Wafer Map Defect Pattern Recognition

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Advances in Computational Intelligence (IWANN 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12861))

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Abstract

Different deep convolution neural network (DCNN) models have been proposed for wafer map pattern identification and classification tasks in previous studies. However, factors such as the effect of input image resolution on the classification performance of the proposed models and class imbalance in the training set after splitting the data into training and test sets have not been considered in the previous studies. This study proposes a DCNN model with residual blocks, called Opt-ResDCNN model, for wafer map defect pattern identification and classification by considering 26 * 26 input image resolutions and class imbalance issues during the model training. The proposed model is compared with the previously published defect pattern recognition and classification models in terms of accuracy, precision, recall, and F1 score for 26 * 26 input image size. Using a publicly available wafer map dataset (WM-811K), the proposed method can obtain an average accuracy, precision, recall, and F1 score results of 99.672%, 99.664%, 99.695%, 99.692%, respectively for the 26 * 26 input image resolution.

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Authors and Affiliations

  1. Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan

    Zemenu Endalamaw Amogne, Fu-Kwun Wang & Jia-Hong Chou

Authors
  1. Zemenu Endalamaw Amogne
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  2. Fu-Kwun Wang
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  3. Jia-Hong Chou
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Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Málaga, Málaga, Spain

    Gonzalo Joya

  3. Technical University of Catalonia, Barcelona, Spain

    Andreu Català

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Amogne, Z.E., Wang, FK., Chou, JH. (2021). Deep Convolutional Neural Networks with Residual Blocks for Wafer Map Defect Pattern Recognition. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham. https://doi.org/10.1007/978-3-030-85030-2_31

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  • DOI: https://doi.org/10.1007/978-3-030-85030-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85029-6

  • Online ISBN: 978-3-030-85030-2

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