Matches in SemOpenAlex for { <https://semopenalex.org/work/W3007690832> ?p ?o ?g. }
- W3007690832 endingPage "2829" @default.
- W3007690832 startingPage "2829" @default.
- W3007690832 abstract "Near-field calculation for a three-dimensional (3D) mask is a fundamental task in extreme ultraviolet (EUV) lithography simulations. This paper develops a fast 3D mask near-field calculation method based on machine learning for EUV lithography. First, the training libraries of rigorous mask near fields are built based on a set of representative mask samples and reference source points. In the testing stage, the mask under consideration is first segmented into a set of non-overlapped patches. Then the local near field of each patch is calculated based on the non-parametric regression and data fusion techniques. Finally, the entire mask near field is synthesized based on the image stitching and data fitting methods. The proposed method is shown to achieve higher accuracy compared to the traditional domain decomposition method. In addition, the computational efficiency is improved up to an order of magnitude compared to the rigorous electromagnetic field simulator." @default.
- W3007690832 created "2020-03-06" @default.
- W3007690832 creator A5017385371 @default.
- W3007690832 creator A5031836304 @default.
- W3007690832 creator A5045558068 @default.
- W3007690832 creator A5049340999 @default.
- W3007690832 creator A5053453125 @default.
- W3007690832 creator A5082193589 @default.
- W3007690832 date "2020-03-18" @default.
- W3007690832 modified "2023-09-28" @default.
- W3007690832 title "Fast extreme ultraviolet lithography mask near-field calculation method based on machine learning" @default.
- W3007690832 cites W147998453 @default.
- W3007690832 cites W1950803081 @default.
- W3007690832 cites W1966400057 @default.
- W3007690832 cites W1971713783 @default.
- W3007690832 cites W1977944608 @default.
- W3007690832 cites W1978234343 @default.
- W3007690832 cites W1995660891 @default.
- W3007690832 cites W2019051362 @default.
- W3007690832 cites W2053376875 @default.
- W3007690832 cites W2055095265 @default.
- W3007690832 cites W2064921304 @default.
- W3007690832 cites W2075951560 @default.
- W3007690832 cites W2084540419 @default.
- W3007690832 cites W2096168689 @default.
- W3007690832 cites W2100507324 @default.
- W3007690832 cites W2105981176 @default.
- W3007690832 cites W2114365104 @default.
- W3007690832 cites W2114667122 @default.
- W3007690832 cites W2117675177 @default.
- W3007690832 cites W2287681961 @default.
- W3007690832 cites W2319458304 @default.
- W3007690832 cites W2373585396 @default.
- W3007690832 cites W2501873590 @default.
- W3007690832 cites W2741728759 @default.
- W3007690832 doi "https://doi.org/10.1364/ao.384407" @default.
- W3007690832 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32225832" @default.
- W3007690832 hasPublicationYear "2020" @default.
- W3007690832 type Work @default.
- W3007690832 sameAs 3007690832 @default.
- W3007690832 citedByCount "7" @default.
- W3007690832 countsByYear W30076908322020 @default.
- W3007690832 countsByYear W30076908322021 @default.
- W3007690832 countsByYear W30076908322022 @default.
- W3007690832 countsByYear W30076908322023 @default.
- W3007690832 crossrefType "journal-article" @default.
- W3007690832 hasAuthorship W3007690832A5017385371 @default.
- W3007690832 hasAuthorship W3007690832A5031836304 @default.
- W3007690832 hasAuthorship W3007690832A5045558068 @default.
- W3007690832 hasAuthorship W3007690832A5049340999 @default.
- W3007690832 hasAuthorship W3007690832A5053453125 @default.
- W3007690832 hasAuthorship W3007690832A5082193589 @default.
- W3007690832 hasConcept C105487726 @default.
- W3007690832 hasConcept C105795698 @default.
- W3007690832 hasConcept C11413529 @default.
- W3007690832 hasConcept C117251300 @default.
- W3007690832 hasConcept C120665830 @default.
- W3007690832 hasConcept C121332964 @default.
- W3007690832 hasConcept C146024833 @default.
- W3007690832 hasConcept C154945302 @default.
- W3007690832 hasConcept C162996421 @default.
- W3007690832 hasConcept C202444582 @default.
- W3007690832 hasConcept C204223013 @default.
- W3007690832 hasConcept C25227671 @default.
- W3007690832 hasConcept C2780150128 @default.
- W3007690832 hasConcept C29081049 @default.
- W3007690832 hasConcept C33923547 @default.
- W3007690832 hasConcept C41008148 @default.
- W3007690832 hasConcept C50644808 @default.
- W3007690832 hasConcept C520434653 @default.
- W3007690832 hasConcept C78371743 @default.
- W3007690832 hasConcept C9652623 @default.
- W3007690832 hasConceptScore W3007690832C105487726 @default.
- W3007690832 hasConceptScore W3007690832C105795698 @default.
- W3007690832 hasConceptScore W3007690832C11413529 @default.
- W3007690832 hasConceptScore W3007690832C117251300 @default.
- W3007690832 hasConceptScore W3007690832C120665830 @default.
- W3007690832 hasConceptScore W3007690832C121332964 @default.
- W3007690832 hasConceptScore W3007690832C146024833 @default.
- W3007690832 hasConceptScore W3007690832C154945302 @default.
- W3007690832 hasConceptScore W3007690832C162996421 @default.
- W3007690832 hasConceptScore W3007690832C202444582 @default.
- W3007690832 hasConceptScore W3007690832C204223013 @default.
- W3007690832 hasConceptScore W3007690832C25227671 @default.
- W3007690832 hasConceptScore W3007690832C2780150128 @default.
- W3007690832 hasConceptScore W3007690832C29081049 @default.
- W3007690832 hasConceptScore W3007690832C33923547 @default.
- W3007690832 hasConceptScore W3007690832C41008148 @default.
- W3007690832 hasConceptScore W3007690832C50644808 @default.
- W3007690832 hasConceptScore W3007690832C520434653 @default.
- W3007690832 hasConceptScore W3007690832C78371743 @default.
- W3007690832 hasConceptScore W3007690832C9652623 @default.
- W3007690832 hasFunder F4320321001 @default.
- W3007690832 hasFunder F4320335960 @default.
- W3007690832 hasIssue "9" @default.
- W3007690832 hasLocation W30076908321 @default.
- W3007690832 hasLocation W30076908322 @default.
- W3007690832 hasOpenAccess W3007690832 @default.