期刊
OPTICS EXPRESS
卷 29, 期 4, 页码 5448-5465出版社
Optica Publishing Group
DOI: 10.1364/OE.418242
关键词
-
类别
资金
- National Major Science and Technology Projects of China [2017ZX02101004-002]
- Natural Science Foundation of Shanghai [17ZR1434100]
This paper introduces an EUV lithography SMO method based on the thick mask model and SL-PSO algorithm, which effectively improves imaging quality and reduces pattern errors.
Extreme ultraviolet (EUV) lithography plays a vital role in the advanced technology nodes of integrated circuits manufacturing. Source mask optimization (SMO) is a critical resolution enhancement technique (RET) or EUV lithography. In this paper, an SMO method for EUV lithography based on the thick mask model and social learning particle swarm optimization (SL-PSO) algorithm is proposed to improve the imaging quality. The thick mask model's parameters are pre-calculated and stored, then SL-PSO is utilized to optimize the source and mask. Rigorous electromagnetic simulation is then carried out to validate the optimization results. Besides, an initialization parameter of the mask optimization (MO) stage is tuned to increase the optimization efficiency and the optimized mask's manufacturability. Optimization is carried out with three target patterns. Results show that the pattern errors (PE) between the print image and target pattern are reduced by 94.7%, 76.9%, 80.6%, respectively. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据