Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91596
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Title: A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization
Authors: Xia, L 
Zheng, P 
Huang, X 
Liu, C 
Issue Date: 2021
Source: Journal of intelligent manufacturing, 2021, https://doi.org/10.1007/s10845-021-01784-1
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
Publisher: Springer
Journal: Journal of intelligent manufacturing 
ISSN: 0956-5515
DOI: 10.1007/s10845-021-01784-1
Rights: © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10845-021-01784-1
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