DC FieldValueLanguage
dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.contributorDepartment of Computingen_US
dc.creatorXia, Len_US
dc.creatorZheng, Pen_US
dc.creatorHuang, Xen_US
dc.creatorLiu, Cen_US
dc.date.accessioned2021-11-10T05:46:59Z-
dc.date.available2021-11-10T05:46:59Z-
dc.identifier.issn0956-5515en_US
dc.identifier.urihttp://hdl.handle.net/10397/91596-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectMaterial removal rateen_US
dc.subjectGraph convolutional networken_US
dc.subjectGate recurrent uniten_US
dc.subjectHypergraphen_US
dc.subjectChemical mechanical planarizationen_US
dc.titleA novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarizationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1007/s10845-021-01784-1en_US
dcterms.abstractThe 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.accessRightsembargoed accessen_US
dcterms.bibliographicCitationJournal of intelligent manufacturing, 2021, Online First, https://doi.org/10.1007/s10845-021-01784-1en_US
dcterms.isPartOfJournal of intelligent manufacturingen_US
dcterms.issued2021-
dc.identifier.isiWOS:000653594700001-
dc.description.validate202111 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1047-n07-
dc.identifier.SubFormID43848-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis 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.pubStatusPublisheden_US
dc.date.embargo2022.05.12en_US
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