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• W4313305500 abstract "Plasmonic lithography can make the evanescent wave at the mask be resonantly amplified by exciting surface plasmon polaritons (SPPs) and participate in imaging, which breaks through the diffraction limit in conventional lithography. It provides a reliable technical way for the study of low-cost, large-area and efficient nanolithography technology. This paper introduces the characteristics of plasmonic lithography, the similarities and the differences with traditional DUV projection lithography. By comparing and analyzing the already existed fast imaging model of mask diffraction near-field (DNF) of DUV/EUV lithography, this paper introduces the decomposition machine learning method of mask diffraction near-field into the fast imaging of plasmonic lithography. A fast imaging model of plasmonic lithography for arbitrary two-dimensional pattern is proposed for the first time. This model enables fast imaging of the input binary 0&1 matrix of the mask directly to the light intensity distribution of photoresist image (PRI). The illumination method employs the normal incidence with x polarization, the normal incidence with y polarization and the quadrupole illumination with TM polarization respectively. The error and the operating efficiency between this fast imaging model and the rigorous electromagnetic model is compared. The test results show that compared with the rigorous electromagnetic computation model, the fast imaging model can greatly improve the calculation efficiency and maintain high accuracy at the same time, which provides great conditions for the development of computational lithography such as SMO/OPC for plasmonic lithography technology." @default.
• W4313305500 created "2023-01-06" @default.
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• W4313305500 date "2022-12-20" @default.
• W4313305500 modified "2023-09-30" @default.
• W4313305500 title "Plasmonic lithography fast imaging model based on the decomposition machine learning method" @default.
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• W4313305500 doi "https://doi.org/10.1364/oe.476825" @default.
• W4313305500 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36606960" @default.
• W4313305500 hasPublicationYear "2022" @default.
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