| 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, Online First, 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 |
| Appears in Collections: | Journal/Magazine Article |
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