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

Liqiao Xia, Pai Zheng (Corresponding Author), Xiao Huang, Chao Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Number of pages12
JournalJournal of Intelligent Manufacturing
Publication statusPublished - 24 May 2021


  • Chemical mechanical planarization
  • Gate recurrent unit
  • Graph convolutional network
  • Hypergraph
  • Material removal rate

ASJC Scopus subject areas

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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