Plasmonic lithography fast imaging model based on least square fitting for periodic patterns

Abstract

As a new and alternative lithography technology, plasmonic lithography can break through the diffraction limit of traditional lithography by exciting the surface plasmon polaritons to make the evanescent wave at the mask participate in imaging. Plasmonic lithography is capable of fabricating deep subwavelength structures for nanophotonics, metasurfaces, and various other applications, and it is expected to be applied to integrated circuit manufacturing. The photoresist aerial image distribution of different mask patterns can be calculated by establishing an imaging model, which is the basis for understanding and further optimizing imaging. Based on the idea of machine learning and least square fitting, a fast imaging model for plasmonic lithography is established, including a one-dimensional line/space periodic pattern and a two-dimensional square hole pattern, which can be used as a supplement to the previous model developed by Ding et al. [Opt. Express 31, 192 (2023)OPEXFF1094-408710.1364/OE.476825]. Compared with the rigorous numerical method, the fast imaging model can greatly improve the calculation speed with high accuracy. Under the same hardware conditions, the calculation speed of the 1D fast imaging model is improved by two orders of magnitude, and the 2D fast imaging model is improved by about 25 times, which creates conditions for the development of computational lithography technology.