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A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization

Author

Listed:
  • Liqiao Xia

    (The Hong Kong Polytechnic University)

  • Pai Zheng

    (The Hong Kong Polytechnic University)

  • Xiao Huang

    (The Hong Kong Polytechnic University)

  • Chao Liu

    (The Hong Kong Polytechnic University)

Abstract

The material removal rate (MRR) plays a critical role in the chemical mechanical planarization (CMP) process in the semiconductor industry. Many physics-based and data-driven approaches have been proposed to-date to predict the MRR. Nevertheless, most of them neglect the underlying equipment structure containing essential interaction mechanisms among different components. To fill the gap, this paper proposes a novel hypergraph convolution network (HGCN) based approach for predicting MRR in the CMP process. The main contributions include: (1) a generic hypergraph model to represent the interrelationships of complex equipment; and (2) a temporal-based prediction approach to learn the complex data correlation and high-order representation based on the hypergraph. To validate the effectiveness of the proposed approach, a case study is conducted by comparing with other cutting-edge models, of which it outperforms in several metrics. It is envisioned that this research can also bring insightful knowledge to similar scenarios in the manufacturing process.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01784-1
    DOI: 10.1007/s10845-021-01784-1
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    References listed on IDEAS

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    1. 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.
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