A scanner matching method based on covariance matrix adaptation evolution strategy

Yuguang Chen; Sikun Li; Shaobo Hu; Guodong Chen; Ming Tang; Weijie Shi; Xiangzhao Wang

doi:10.1117/12.2605879

1 November 2021 A scanner matching method based on covariance matrix adaptation evolution strategy

Yuguang Chen, Sikun Li, Shaobo Hu, Guodong Chen, Ming Tang, Weijie Shi, Xiangzhao Wang

Proceedings Volume 12057, Twelfth International Conference on Information Optics and Photonics; 120572F (2021) https://doi.org/10.1117/12.2605879
Event: Twelfth International Conference on Information Optics and Photonics, 2021, Xi'an, China

Abstract

A lithography tool-matching (scanner matching) method based on covariance matrix adaptation evolution strategy is proposed to effectively reduce critical dimension errors due to inter-scanners differences by optimizing illumination sources with pixelated sources sampling. With a one-dimensional line/space mask pattern as the matching target, we have simulated matching scanners with annular, dipole, quasar and freeform sources by the proposed method. The results of these simulations show that the root-mean-square of critical dimension error was reduced by more than 90% after matching. Compared with matching methods based on other heuristic algorithms, such as self-adaptive differential evolution (JADE), particle swarm optimization (PSO) and genetic algorithms (GA), this method has effectively improved the efficiency of scanner matching, and achieved excellent matching results at the same time. Simulations have shown the effectiveness of the proposed method on scanner matching of standard-illumination sources and freeform-illumination sources. Simulations verify the stability of the proposed method.

Citation Download Citation

Yuguang Chen, Sikun Li, Shaobo Hu, Guodong Chen, Ming Tang, Weijie Shi, and Xiangzhao Wang "A scanner matching method based on covariance matrix adaptation evolution strategy", Proc. SPIE 12057, Twelfth International Conference on Information Optics and Photonics, 120572F (1 November 2021); https://doi.org/10.1117/12.2605879

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KEYWORDS

Scanners

Particle swarm optimization

Lithography

Lithographic illumination

Computer simulations

Particles

Optimization (mathematics)

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