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

Liqiao Xia, Pai Zheng (), Xiao Huang and Chao Liu
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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

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 8, No 7, 2295-2306

Abstract: 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.

Keywords: Material removal rate; Graph convolutional network; Gate recurrent unit; Hypergraph; Chemical mechanical planarization (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10845-021-01784-1

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