Identification of key aberrations that affect pattern imaging in EUVL

Jiashuo Wang; Xiaojing Su; Yayi Wei

doi:10.1117/12.3009886

10 April 2024 Identification of key aberrations that affect pattern imaging in EUVL

Jiashuo Wang, Xiaojing Su, Yayi Wei

Proceedings Volume 12954, DTCO and Computational Patterning III; 129541A (2024) https://doi.org/10.1117/12.3009886
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

In EUVL, aberrations play a crucial role in critical dimension (CD) and pattern shift (PS) errors. It is significant to decide aberration compensation and optimization strategies for compensating exposure errors. The modeling process of aberration is time-consuming, mainly because of the need to consider numerous aberrations. In order to save the runtime for aberration modeling, this paper proposes a methodology for identifying the key aberrations that have significant impacts on imaging results. Three different techniques are employed and compared, including single parameter sensitivity analysis, definitive screening design (DSD) method, and SOBOL method that consider the coupling effects of different orders. By comparing the deviations between the imaging results considering only key aberrations and all aberrations, it is found that the identified aberrations achieve extremely high accuracy for various patterns and illumination conditions. Even though the proportion of key aberrations among all aberrations is only a small fraction. All the three methods can achieve that the average CD and PS deviations do not surpass 1%, using 40% of the total 37 aberration items. Thereby, the feasibility of using identified key aberrations for aberration modeling is validated. In addition, we also compare the accuracies of three techniques under the same conditions and found that the SOBOL method is the most suitable technique for identifying key aberrations. Consequently, for specific illumination conditions and corresponding layout patterns, the use of key aberrations is an effective way to characterize the impacts of all 37 aberrations, accelerating the aberration compensation and optimization without sacrificing accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiashuo Wang, Xiaojing Su, and Yayi Wei "Identification of key aberrations that affect pattern imaging in EUVL", Proc. SPIE 12954, DTCO and Computational Patterning III, 129541A (10 April 2024); https://doi.org/10.1117/12.3009886

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