A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization
نویسندگان
چکیده
The material removal rate (MRR) plays a critical role in the chemical mechanical planarization (CMP) process semiconductor industry. Many physics-based and data-driven approaches have been proposed to-date to predict MRR. Nevertheless, most of them neglect underlying equipment structure containing essential interaction mechanisms among different components. To fill gap, this paper proposes novel hypergraph convolution network (HGCN) based approach for predicting MRR CMP process. main contributions include: (1) generic model represent interrelationships complex equipment; (2) temporal-based prediction learn data correlation high-order representation on hypergraph. validate effectiveness approach, case study is conducted by comparing with other cutting-edge models, which it outperforms several metrics. It envisioned that research can also bring insightful knowledge similar scenarios manufacturing
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ژورنال
عنوان ژورنال: Journal of Intelligent Manufacturing
سال: 2021
ISSN: ['1572-8145', '0956-5515']
DOI: https://doi.org/10.1007/s10845-021-01784-1