Analysis of trade-off relationships between resolution, line edge roughness, and sensitivity in extreme ultraviolet lithography using machine learning

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.