DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Gangmin | ko |
dc.contributor.author | Kim, Taeyoung | ko |
dc.contributor.author | Shin, Youngsoo | ko |
dc.date.accessioned | 2023-11-27T03:00:14Z | - |
dc.date.available | 2023-11-27T03:00:14Z | - |
dc.date.created | 2023-11-27 | - |
dc.date.created | 2023-11-27 | - |
dc.date.issued | 2023-11 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, v.36, no.4, pp.629 - 635 | - |
dc.identifier.issn | 0894-6507 | - |
dc.identifier.uri | http://hdl.handle.net/10203/315219 | - |
dc.description.abstract | OPC 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.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Fast Optical Proximity Correction Using Graph Convolutional Network With Autoencoders | - |
dc.type | Article | - |
dc.identifier.wosid | 001097335400018 | - |
dc.identifier.scopusid | 2-s2.0-85168652688 | - |
dc.type.rims | ART | - |
dc.citation.volume | 36 | - |
dc.citation.issue | 4 | - |
dc.citation.beginningpage | 629 | - |
dc.citation.endingpage | 635 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING | - |
dc.identifier.doi | 10.1109/TSM.2023.3306751 | - |
dc.contributor.localauthor | Shin, Youngsoo | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Layout | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Decoding | - |
dc.subject.keywordAuthor | Semiconductor device measurement | - |
dc.subject.keywordAuthor | Graph neural networks | - |
dc.subject.keywordAuthor | Optical proximity correction | - |
dc.subject.keywordAuthor | graph convolutional network | - |
dc.subject.keywordAuthor | autoencoder | - |
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