Design of an all-facet illuminator for high NA EUV lithography exposure tool based on deep reinforcement learning
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913899986550784 |
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| author | Li, Tong Chen, Yuqing Li, Yanqiu Liu, Lihui |
| author_facet | Li, Tong Chen, Yuqing Li, Yanqiu Liu, Lihui |
| contents | 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 optics, and removing relay system to achieve high transmission. The double facets is matched using the deep RL framework, which includes the policy network with improved trainability and low computational demands, and the reward function with great optimization direction and fast convergence rate, enabling to rapidly generate multiple matching results with high uniformity. An all-facet illuminator for a 0.55 NA EUV lithography exposure tool is designed by the proposed method. Simulation results indicate that the transmission is greater than 35%, and uniformity exceed 99% under multiple illumination pupil shapes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15558 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Design of an all-facet illuminator for high NA EUV lithography exposure tool based on deep reinforcement learning Li, Tong Chen, Yuqing Li, Yanqiu Liu, Lihui Optics 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 optics, and removing relay system to achieve high transmission. The double facets is matched using the deep RL framework, which includes the policy network with improved trainability and low computational demands, and the reward function with great optimization direction and fast convergence rate, enabling to rapidly generate multiple matching results with high uniformity. An all-facet illuminator for a 0.55 NA EUV lithography exposure tool is designed by the proposed method. Simulation results indicate that the transmission is greater than 35%, and uniformity exceed 99% under multiple illumination pupil shapes. |
| title | Design of an all-facet illuminator for high NA EUV lithography exposure tool based on deep reinforcement learning |
| topic | Optics |
| url | https://arxiv.org/abs/2506.15558 |