Abstract

In the domain of computational lithography, the performance of an optimized imaging solution is usually qualified with a full-chip posted-optical-proximity-correction lithography printing check to ensure the printing is defect free before committed for mask writing. It is thus highly preferable for the optimization process itself to be driven by the same defect detection mechanism towards a defect-free solution. On the other hand, the huge data size of chip layout poses great challenge to such optimization process, in terms of runtime and data storage. A gradient-based optimization scheme thus becomes necessary. To date, no successful engineering tool is capable of accommodating these two requirements at the same time. We demonstrate the technology of defect-driven gradient-based optimization to achieve a defect-free solution within practical runtime specification, using ASML’s computational lithography product Tachyon SMO.

© 2021 Optical Society of America

Efficient informatics-based source and mask optimization for optical lithography

Yihua Pan, Xu Ma, Shengen Zhang, Javier Garcia-Frias, and Gonzalo R. Arce
Appl. Opt. 60(27) 8307-8315 (2021)

Critical pattern selection method for full-chip source and mask optimization

Lufeng Liao, Sikun Li, Xiangzhao Wang, Libin Zhang, Pengzheng Gao, Yayi Wei, and Weijie Shi
Opt. Express 28(14) 20748-20763 (2020)

Multi-objective adaptive source optimization for full chip

Guanghui Liao, Yiyu Sun, Pengzhi Wei, Miao Yuan, Zhaoxuan Li, and Yanqiu Li
Appl. Opt. 60(9) 2530-2536 (2021)

Exposure latitude aware source and mask optimization for extreme ultraviolet lithography

Lulu Zou, Yiyu Sun, Pengzhi Wei, Miao Yuan, Zhaoxuan Li, Lihui Liu, and Yanqiu Li
Appl. Opt. 60(30) 9404-9410 (2021)

Source mask optimization for extreme-ultraviolet lithography based on thick mask model and social learning particle swarm optimization algorithm

Zinan Zhang, Sikun Li, Xiangzhao Wang, Wei Cheng, and Yuejing Qi
Opt. Express 29(4) 5448-5465 (2021)