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

Author:

Zhao Rongbo,Hu Ziyu,Wang Xiaolin,Tao Peipei,Wang Yimeng,Liu Tianqi,Wei Yayi12,Xu Hong,He XiangmingORCID

Affiliation:

1. Institute of Microelectronics of Chinese Academy of Sciences

2. University of Chinese Academy of Sciences

Abstract

Resolution, line edge/width roughness, and sensitivity (RLS) are critical indicators for evaluating the imaging performance of resists. As the technology node gradually shrinks, stricter indicator control is required for high-resolution imaging. However, current research can improve only part of the RLS indicators of resists for line patterns, and it is difficult to improve the overall imaging performance of resists in extreme ultraviolet lithography. Here, we report a lithographic process optimization system of line patterns, where RLS models are first established by adopting a machine learning method, and then these models are optimized using a simulated annealing algorithm. Finally, the process parameter combination with optimal imaging quality of line patterns can be obtained. This system can control resist RLS indicators, and it exhibits high optimization accuracy, which facilitates the reduction of process optimization time and cost and accelerates the development of the lithography process.

Funder

National Natural Science Foundation of China

Tsinghua University Initiative Scientific Research Program

Beijing Municipal Science & Technology Commission, Administrative Commission of Zhongguancun Science Park

China Postdoctoral Science Foundation

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering

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