Fast source mask co-optimization method for high-NA EUV lithography
用于高NA EUV光刻的快速源掩模协同优化方法
高NA EUVリソグラフィ用高速ソースマスクの共同最適化方法
고NA EUV 광각을 위한 빠른 소스 마스크 공동 최적화 방법
Método de optimización colaborativa de máscaras de fuente rápida para litografía EUV de alto na
Méthode rapide de co - optimisation du masque de source pour la lithographie EUV à haute na
Метод совместной оптимизации шаблонов быстрого источника для фотолитографии с высокой NA EUV
Ziqi Li 李梓棋 ¹ ³, Lisong Dong 董立松 ¹ ³ ⁴, Xu Ma 马旭 ², Yayi Wei 韦亚一 ¹ ³ ⁴
¹ EDA Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
中国 北京 中国科学院微电子研究所EDA中心
² Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
中国 北京 北京理工大学光电学院 光电成像技术与系统教育部重点实验室
³ University of Chinese Academy of Sciences, Beijing 100049, China
中国 北京 中国科学院大学
⁴ Guangdong Greater Bay Area Applied Research Institute of Integrated Circuit and Systems, Guangzhou 510700, China
中国 广州 广东省大湾区集成电路与系统应用研究院
Opto-Electronic Advances, 25 April 2024

Extreme ultraviolet (EUV) lithography with high numerical aperture (NA) is a future technology to manufacture the integrated circuit in sub-nanometer dimension. Meanwhile, source mask co-optimization (SMO) is an extensively used approach for advanced lithography process beyond 28 nm technology node.

This work proposes a novel SMO method to improve the image fidelity of high-NA EUV lithography system. A fast high-NA EUV lithography imaging model is established first, which includes the effects of mask three-dimensional structure and anamorphic magnification. Then, this paper develops an efficient SMO method that combines the gradient-based mask optimization algorithm and the compressive-sensing-based source optimization algorithm.

A mask rule check (MRC) process is further proposed to simplify the optimized mask pattern. Results illustrate that the proposed SMO method can significantly reduce the lithography patterning error, and maintain high computational efficiency.
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