Prediction of 3D morphological features of nano-patterns from SEM images using a frequency domain approach

Ge Liu; Libin Zhang; Le Ma; Yayi Wei; Yajuan Su

doi:10.1117/12.3034496

12 November 2024 Prediction of 3D morphological features of nano-patterns from SEM images using a frequency domain approach

Ge Liu, Libin Zhang, Le Ma, Yayi Wei, Yajuan Su

Author Affiliations +

Proceedings Volume 13215, International Conference on Extreme Ultraviolet Lithography 2024; 1321511 (2024) https://doi.org/10.1117/12.3034496
Event: SPIE Photomask Technology + EUV Lithography, 2024, Monterey, California, United States

Conference Poster

Abstract

In advanced lithography nodes, variations in the sidewall angle and height deformation of photoresists can significantly impact production quality, presenting new challenges for metrology techniques. To address these challenges, we first developed a 3D nano-structure modeling toolkit, T3S, capable of generating a wide range of 3D models with diverse nano-structural features. We then employed a Monte Carlo simulator to generate SEM images of these nano-structures under various primary electron energies and detector settings. Then we analyzed the grayscale profiles of these images. By applying fast Fourier transform, we extracted the frequency domain information from the grayscale profiles of the SEM images, successfully establishing a comprehensive SEM frequency domain database of nano-structures with varying 3D features and SEM operating conditions. Furthermore, we developed a frequency domain-based algorithm to match and predict 3D nano-structural features, such as sidewall angle and height, from unknown SEM images. This database and algorithm enable the rapid and reliable assessment of photoresist shape quality, enhancing process control in advanced lithography nodes.

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

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Ge Liu, Libin Zhang, Le Ma, Yayi Wei, and Yajuan Su "Prediction of 3D morphological features of nano-patterns from SEM images using a frequency domain approach", Proc. SPIE 13215, International Conference on Extreme Ultraviolet Lithography 2024, 1321511 (12 November 2024); https://doi.org/10.1117/12.3034496

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