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Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification

Author

Listed:
  • Chia-Yu Hsu

    (National Taipei University of Technology)

  • Ju-Chien Chien

    (National Tsing Hua University
    Artificial Intelligence for Intelligent Manufacturing Systems (AIMS) Research Center, Ministry of Science & Technology)

Abstract

Wafer bin maps (WBM) provides crucial information regarding process abnormalities and facilitate the diagnosis of low-yield problems in semiconductor manufacturing. Most studies of WBM classification and analysis apply a statistical-based method or machine learning method operating on raw wafer data and extracted features. With increasing WBM pattern diversity and complexity, the useful features for effective WBM recognition are highly dependent on domain knowledge. This study proposes an ensemble convolutional neural network (ECNN) framework 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. An industrial WBM dataset (namely, WM-811K) from a wafer fabrication process was used to demonstrate the effectiveness of the proposed ECNN framework. The proposed ECNN has superior performance in terms of precision, recall, F1 and other conventional machine learning classifiers such as linear regression, random forest, gradient boosting machine, and artificial neural network. The experimental results show that the proposed ECNN framework is able to identify common WBM defect patterns effectively.

Suggested Citation

  • Chia-Yu Hsu & Ju-Chien Chien, 2022. "Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 831-844, March.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01687-7
    DOI: 10.1007/s10845-020-01687-7
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    References listed on IDEAS

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    1. Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
    2. Eryun Liu & Kangping Chen & Zhiyu Xiang & Jun Zhang, 2020. "Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1037-1049, April.
    3. Hsu, Shao-Chung & Chien, Chen-Fu, 2007. "Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 107(1), pages 88-103, May.
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    5. Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
    6. Carlos Gonzalez-Val & Adrian Pallas & Veronica Panadeiro & Alvaro Rodriguez, 2020. "A convolutional approach to quality monitoring for laser manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 789-795, March.
    7. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
    8. Yuan, Tao & Kuo, Way, 2008. "Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference," European Journal of Operational Research, Elsevier, vol. 190(1), pages 228-240, October.
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    Cited by:

    1. Shijie Wang & Haiyong Chen & Kun Liu & Ying Zhou & Huichuan Feng, 2023. "Meta-FSDet: a meta-learning based detector for few-shot defects of photovoltaic modules," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3413-3427, December.

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