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Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing

Author

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  • Chia-Yu Hsu

    (National Taipei University of Technology)

  • Wei-Chen Liu

    (Yuan Ze University)

Abstract

The development of information technology and process technology have been enhanced the rapid changes in high-tech products and smart manufacturing, specifications become more sophisticated. Large amount of sensors are installed to record equipment condition during the manufacturing process. In particular, the characteristics of sensor data are temporal. Most the existing approaches for time series classification are not applicable to adaptively extract the effective feature from a large number of sensor data, accurately detect the fault, and provide the assignable cause for fault diagnosis. This study aims to propose a multiple time-series convolutional neural network (MTS-CNN) model for fault detection and diagnosis in semiconductor manufacturing. This study incorporates data augmentation with sliding window to generate amounts of subsequences and thus to enhance the diversity and avoid over-fitting. The key features of equipment sensor can be learned automatically through stacked convolution-pooling layers. The importance of each sensor is also identified through the diagnostic layer in the proposed MTS-CNN. An empirical study from a wafer fabrication was conducted to validate the proposed MTS-CNN and compare the performance among the other multivariate time series classification methods. The experimental results demonstrate that the MTS-CNN can accurately detect the fault wafers with high accuracy, recall and precision, and outperforms than other existing multivariate time series classification methods. Through the output value of the diagnostic layer in MTS-CNN, we can identify the relationship between each fault and different sensors and provider valuable information to associate the excursion for fault diagnosis.

Suggested Citation

  • Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:3:d:10.1007_s10845-020-01591-0
    DOI: 10.1007/s10845-020-01591-0
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    References listed on IDEAS

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    1. Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
    2. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    3. Zhiwen Huang & Jianmin Zhu & Jingtao Lei & Xiaoru Li & Fengqing Tian, 2020. "Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 953-966, April.
    4. Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
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    Cited by:

    1. Jeongsub Choi & Mengmeng Zhu & Jihoon Kang & Myong K. Jeong, 2024. "Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing," Annals of Operations Research, Springer, vol. 339(1), pages 185-201, August.
    2. Peng Zhan & Shaokun Wang & Jun Wang & Leigang Qu & Kun Wang & Yupeng Hu & Xueqing Li, 2021. "Temporal anomaly detection on IIoT-enabled manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1669-1678, August.
    3. Tobias Schlosser & Michael Friedrich & Frederik Beuth & Danny Kowerko, 2022. "Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1099-1123, April.
    4. Jinwoo Song & Prashant Kumar & Yonghawn Kim & Heung Soo Kim, 2024. "A Fault Detection System for Wiring Harness Manufacturing Using Artificial Intelligence," Mathematics, MDPI, vol. 12(4), pages 1-17, February.
    5. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.
    6. Chien-Chih Wang & Yi-Ying Yang, 2023. "A Machine Learning Approach for Improving Wafer Acceptance Testing Based on an Analysis of Station and Equipment Combinations," Mathematics, MDPI, vol. 11(7), pages 1-14, March.
    7. Hasan Tercan & Philipp Deibert & Tobias Meisen, 2022. "Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 283-292, January.

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