Material removal rate (MRR) plays a critical role in the operation of chemical mechanical planarization (CMP) process in the semiconductor industry. To date, many physics-based and data-driven approaches have been proposed to predict the MRR. Nevertheless, most of the existing methodologies neglect the potential source of its well-organized and underlying equipment structure containing interaction mechanisms among different components. To address its limitation, this paper proposes a novel hypergraph neural network-based approach for predicting the MRR in CMP. Two main scientific contributions are presented in this work: 1) establishing a generic modeling technique to construct the complex equipment knowledge graph with a hypergraph form base on the comprehensive understanding and analysis of equipment structure and mechanism, and 2) proposing a novel prediction method by combining the Recurrent Neural Network based model and the Hypergraph Neural Network to learn the complex data correlation and high-order representation base on the Spatio-temporal equipment hypergraph. To validate the proposed approach, a case study is conducted based on an open-source dataset. The experimental results prove that the proposed model can capture the hidden data correlation effectively. It is also envisioned that the proposed approach has great potentials to be applied in other similar smart manufacturing scenarios.
|Title of host publication||41st Computers and Information in Engineering Conference (CIE)|
|Publisher||American Society of Mechanical Engineers(ASME)|
|Number of pages||7|
|Publication status||Published - 17 Nov 2021|
|Event||41st Computers and Information in Engineering Conference, CIE 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021 - Virtual, Online|
Duration: 17 Aug 2021 → 19 Aug 2021
|Name||Proceedings of the ASME Design Engineering Technical Conference|
|Conference||41st Computers and Information in Engineering Conference, CIE 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021|
|Period||17/08/21 → 19/08/21|
Bibliographical noteFunding Information:
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), Hong Kong SAR, China.
- Chemical mechanical planarization
- Graph neural network
- Material removal rate
- Recurrent neural network