X-ray scatterometry using deep learning

Shuo Liu; Tianjuan Yang; Jiahao Zhang; Jianyuan Ma; Shiyuan Liu; Xiuguo Chen

doi:10.1117/12.2612769

19 November 2021 X-ray scatterometry using deep learning

Shuo Liu, Tianjuan Yang, Jiahao Zhang, Jianyuan Ma, Shiyuan Liu, Xiuguo Chen

Proceedings Volume 12059, Tenth International Symposium on Precision Mechanical Measurements; 120591M (2021) https://doi.org/10.1117/12.2612769
Event: Tenth International Symposium on Precision Mechanical Measurements, 2021, Qingdao, China

Abstract

With the development of semiconductor manufacturing processes, critical dimension small-angle x-ray scattering (CDSAXS) has been identified as a potential solution for measurement. It is worthy of exploring how to achieve fast parameter extraction. In this paper, we propose a XSCNN model based on deep learning to reconstruct the parameters related to structure and measurement conditions. Simulation experiments performed on a trapezoidal grating have demonstrated that XSCNN can produce satisfactory results. It is expected that deep learning will provide a practical solution in CD-SAXS.

Citation Download Citation

Shuo Liu, Tianjuan Yang, Jiahao Zhang, Jianyuan Ma, Shiyuan Liu, and Xiuguo Chen "X-ray scatterometry using deep learning", Proc. SPIE 12059, Tenth International Symposium on Precision Mechanical Measurements, 120591M (19 November 2021); https://doi.org/10.1117/12.2612769

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