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.
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Funding
This research work was partially supported by the grants from the National Natural Research Foundation of China (No. 52005424), and Research Committee of The Hong Kong Polytechnic University (G-UAHH).
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Xia, L., Zheng, P., Huang, X. et al. A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization. J Intell Manuf 33, 2295–2306 (2022). https://doi.org/10.1007/s10845-021-01784-1
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DOI: https://doi.org/10.1007/s10845-021-01784-1