Abstract

The critical dimension (CD), roughness, and sensitivity are extremely significant indicators for evaluating the imaging performance of photoresists in extreme ultraviolet lithography. As the CD gradually shrinks, tighter indicator control is required for high fidelity imaging. However, current research primarily focuses on the optimization of one indicator of one-dimensional line patterns, and little attention has been paid to two-dimensional patterns. Here, we report an image quality optimization method of two-dimensional contact holes. This method takes horizontal and vertical contact widths, contact edge roughness, and sensitivity as evaluation indicators, and uses machine learning to establish the corresponding relationship between process parameters and each indicator. Then, the simulated annealing algorithm is applied to search for the optimal process parameters, and finally, a set of process parameters with optimum image quality is obtained. Rigorous imaging results of lithography demonstrate that this method has very high optimization accuracy and can improve the overall performance of the device, dramatically accelerating the development of the lithography process.

© 2023 Optica Publishing Group

Process optimization of line patterns in extreme ultraviolet lithography using machine learning and a simulated annealing algorithm

Rongbo Zhao, Ziyu Hu, Xiaolin Wang, Peipei Tao, Yimeng Wang, Tianqi Liu, Yayi Wei, Hong Xu, and Xiangming He
Appl. Opt. 62(11) 2892-2898 (2023)

Aberration optimization in an extreme ultraviolet lithography projector via a BP neural network and simulated annealing algorithm

Rongbo Zhao, Lisong Dong, Rui Chen, and Yayi Wei
Appl. Opt. 60(5) 1341-1348 (2021)

Aberration budget analysis of EUV lithography from the imaging performance of a contact layer in a 5 nm technology node

Zhishu Chen, Lisong Dong, Huwen Ding, and Yayi Wei
Appl. Opt. 62(27) 7270-7279 (2023)

Gradient-based inverse extreme ultraviolet lithography

Xu Ma, Jie Wang, Xuanbo Chen, Yanqiu Li, and Gonzalo R. Arce
Appl. Opt. 54(24) 7284-7300 (2015)

Analysis and modulation of aberration in an extreme ultraviolet lithography projector via rigorous simulation and a back propagation neural network

Rongbo Zhao, Lisong Dong, Chunyue Bo, Yayi Wei, and Xiaojing Su
Appl. Opt. 59(23) 7074-7082 (2020)