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Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process

Author

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  • Ki Bum Lee

    (Yonsei University)

  • Chang Ouk Kim

    (Yonsei University)

Abstract

In semiconductor manufacturing, the chemical mechanical planarization (CMP) process produces higher thickness variability in the edge area of the wafer than that in the center area due to the characteristics of the polishing operation. To address this problem, advanced CMP equipment includes a function that controls the removal rate of each area. However, to take full advantage of this capability, effective advanced process control systems must be implemented with a virtual metrology (VM) model. However, the prediction performance of the VM model often deteriorates due to process drift. Here, we present a deep learning-based VM model that demonstrates high performance in the presence of nonlinear process drift. The proposed model combines a recurrent neural network and a convolutional neural network to extract time-dependent and time-independent features. A two-stage model training method is proposed that alternately updates the weights of each network to improve prediction performance. In the experiments using on-site CMP process data, the performance of the deep learning models exceeded that of standard machine learning models. The proposed model showed an 8.48% decrease in process variability relative to the best-performing machine learning model, which was elastic nets.

Suggested Citation

  • Ki Bum Lee & Chang Ouk Kim, 2020. "Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 73-86, January.
  • Handle: RePEc:spr:joinma:v:31:y:2020:i:1:d:10.1007_s10845-018-1437-4
    DOI: 10.1007/s10845-018-1437-4
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    References listed on IDEAS

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    1. D. Yu. Pimenov & A. Bustillo & T. Mikolajczyk, 2018. "Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1045-1061, June.
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    Cited by:

    1. Youngju Kim & Hoyeop Lee & Chang Ouk Kim, 2023. "A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 529-540, February.
    2. Yupeng Wei & Dazhong Wu, 2024. "Material removal rate prediction in chemical mechanical planarization with conditional probabilistic autoencoder and stacking ensemble learning," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 115-127, January.
    3. Liqiao Xia & Pai Zheng & Xiao Huang & Chao Liu, 2022. "A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2295-2306, December.
    4. 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.
    5. Sangho Lee & Youngdoo Son, 2021. "Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks," Mathematics, MDPI, vol. 9(12), pages 1-21, June.

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