Fast Optical Proximity Correction Using Graph Convolutional Network With Autoencoders

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dc.contributor.authorCho, Gangminko
dc.contributor.authorKim, Taeyoungko
dc.contributor.authorShin, Youngsooko
dc.date.accessioned2023-11-27T03:00:14Z-
dc.date.available2023-11-27T03:00:14Z-
dc.date.created2023-11-27-
dc.date.created2023-11-27-
dc.date.issued2023-11-
dc.identifier.citationIEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, v.36, no.4, pp.629 - 635-
dc.identifier.issn0894-6507-
dc.identifier.urihttp://hdl.handle.net/10203/315219-
dc.description.abstractOPC is a very time consuming process for mask synthesis. Quick and accurate OPC using GCN with layout encoder and mask decoder is proposed. (1) GCN performs a series of aggregation with MLP for correction process. A feature of a particular polygon is aggregated with weighted features of neighbor polygons; this is a key motivation of using GCN since one polygon should be corrected while its neighbors are taken into account for more accurate correction. (2) GCN inputs are provided by a layout encoder, which extracts a feature from each layout polygon. GCN outputs, features corresponding to corrected polygons, are processed by a mask decoder to yield the final mask pattern. (3) The encoder and decoder originate from respective autoencoders. High fidelity of decoder is a key for OPC quality. This is achieved by collective training of the two autoencoders with a single loss function while the encoder and decoder are connected. Experiments demonstrate that the proposed OPC achieves 47% smaller EPE than OPC using a simple MLP model.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleFast Optical Proximity Correction Using Graph Convolutional Network With Autoencoders-
dc.typeArticle-
dc.identifier.wosid001097335400018-
dc.identifier.scopusid2-s2.0-85168652688-
dc.type.rimsART-
dc.citation.volume36-
dc.citation.issue4-
dc.citation.beginningpage629-
dc.citation.endingpage635-
dc.citation.publicationnameIEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING-
dc.identifier.doi10.1109/TSM.2023.3306751-
dc.contributor.localauthorShin, Youngsoo-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorLayout-
dc.subject.keywordAuthorFeature extraction-
dc.subject.keywordAuthorDecoding-
dc.subject.keywordAuthorSemiconductor device measurement-
dc.subject.keywordAuthorGraph neural networks-
dc.subject.keywordAuthorOptical proximity correction-
dc.subject.keywordAuthorgraph convolutional network-
dc.subject.keywordAuthorautoencoder-
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