Design of an all-facet illuminator for high NA EUV lithography exposure tool based on deep reinforcement learning
Tong Li, Yuqing Chen, Yanqiu Li, Lihui Liu

TL;DR
This paper presents a novel design for an all-facet illuminator in high NA EUV lithography, utilizing deep reinforcement learning to optimize uniformity and transmission, enabling sub-2 nm node production.
Contribution
It introduces a deep RL-based matching method for double facets and a matrix optics design for high transmission in all-facet illuminators.
Findings
Transmission exceeds 35% in simulations
Uniformity surpasses 99% under multiple pupil shapes
Design enables sub-2 nm node lithography
Abstract
Using the illuminator for high numerical aperture (NA) extreme ultraviolet (EUV) exposure tool in EUV lithography can lead to support volume production of sub-2 nm logic nodes and leading-edge DRAM nodes. However, the typical design method of the illuminator has issues with the transmission owing to the limitation of optical structure that cannot further reduce process parameter k1, and uniformity due to the restriction of matching method that can only consider one factor affecting uniformity. The all-facet illuminator can improve transmission by removing relay system. Deep reinforcement learning (RL) can improve the uniformity by considering multiple factors. In this paper, a design method of the all-facet illuminator for high NA EUV lithography exposure tool and a matching method based on deep RL for the double facets are proposed. The all-facet illuminator is designed using matrix…
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Taxonomy
TopicsAdvancements in Photolithography Techniques · CCD and CMOS Imaging Sensors · Image Processing Techniques and Applications
