KEYWORDS: Education and training, Machine learning, Electron beam lithography, Point spread functions, Monte Carlo methods, Data processing, Data modeling, Optical proximity correction, Scattering
Electron Beam Lithography (EBL) has advantages in high resolution imaging. However, its resolution was limited by the proximity effect due to electron backscattering in solid materials. Current methods for correction of proximity effects, such as dose and shape corrections, are effective in improving accuracy, but their time cost is not negligible. This paper presents a method that uses TransUNet, trained with the results of traditional dose correction methods, to create an end-to-end model. This method maintains accuracy, simplifies data processing to improve computational efficiency, and enhances ease of use.
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