Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/91596
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Industrial and Systems Engineering | en_US |
dc.contributor | Department of Computing | en_US |
dc.creator | Xia, L | en_US |
dc.creator | Zheng, P | en_US |
dc.creator | Huang, X | en_US |
dc.creator | Liu, C | en_US |
dc.date.accessioned | 2021-11-10T05:46:59Z | - |
dc.date.available | 2021-11-10T05:46:59Z | - |
dc.identifier.issn | 0956-5515 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/91596 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.rights | © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 | en_US |
dc.rights | 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 | en_US |
dc.subject | Material removal rate | en_US |
dc.subject | Graph convolutional network | en_US |
dc.subject | Gate recurrent unit | en_US |
dc.subject | Hypergraph | en_US |
dc.subject | Chemical mechanical planarization | en_US |
dc.title | A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 2295 | en_US |
dc.identifier.epage | 2306 | en_US |
dc.identifier.volume | 33 | en_US |
dc.identifier.issue | 8 | en_US |
dc.identifier.doi | 10.1007/s10845-021-01784-1 | en_US |
dcterms.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. | en_US |
dcterms.bibliographicCitation | Journal of intelligent manufacturing, Dec. 2022, v. 33, no. 8, p. 2295-2306 | en_US |
dcterms.isPartOf | Journal of intelligent manufacturing | en_US |
dcterms.issued | 2022-12 | - |
dc.identifier.isi | WOS:000653594700001 | - |
dc.description.validate | 202111 bchy | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a1047-n07, a1288 | - |
dc.identifier.SubFormID | 43848, 44466 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Others: 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). | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Xia_Hypergraph_Material_Removal.pdf | Pre-Published version | 1.32 MB | Adobe PDF | View/Open |
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