Paper
23 March 2020 Analysis of trade-off relationships between resolution, line edge roughness, and sensitivity in extreme ultraviolet lithography using machine learning
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Abstract
High-volume production of semiconductor devices by extreme ultraviolet (EUV) lithography has started since 2019. A high numerical aperture tool is planned to extend the use of EUV lithography. The trade-off relationships between resolution, line edge roughness (LER), and sensitivity are a significant concern for the extendability of EUV lithography. In previous study, the dependences of chemical gradient (an indicator of LER) on the half-pitch of line-and-space patterns, the sensitivity, the sensitizer concentration, and the effective reaction radius for deprotection were investigated using a simulation on the basis of the sensitization and reaction mechanisms of chemically amplified EUV resists. Although the relationships between resolution, LER, and sensitivity were formulated in the half-pitch range lager than 10 nm, they were deviated from the equations in the sub-10 nm half-pitch resolution region. Recently, the application of information science to the material engineering has attracted much attention. In this study, the feasibility of the application of machine learning to the analysis of trade-off relationships was investigated. The sub-10 nm region was fitted well using lasso.
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Kazuki Azumagawa and Takahiro Kozawa "Analysis of trade-off relationships between resolution, line edge roughness, and sensitivity in extreme ultraviolet lithography using machine learning", Proc. SPIE 11323, Extreme Ultraviolet (EUV) Lithography XI, 1132329 (23 March 2020); https://doi.org/10.1117/12.2551826
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KEYWORDS
Line edge roughness

Extreme ultraviolet lithography

Machine learning

Extreme ultraviolet

Monte Carlo methods

Information science

Semiconductors

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