Deep learning denoiser assisted roughness measurements extraction from thin resists with low signal-to-noise-ratio (SNR) SEM images: analysis with SMILE

Sara Sacchi; Bappaditya Dey; Iacopo Mochi; Sandip Halder; Philippe Leray

doi:10.1117/12.2687639

21 November 2023 Deep learning denoiser assisted roughness measurements extraction from thin resists with low signal-to-noise-ratio (SNR) SEM images: analysis with SMILE

Sara Sacchi, Bappaditya Dey, Iacopo Mochi, Sandip Halder, Philippe Leray

Author Affiliations +

Proceedings Volume 12750, International Conference on Extreme Ultraviolet Lithography 2023; 1275010 (2023) https://doi.org/10.1117/12.2687639
Event: SPIE Photomask Technology + EUV Lithography, 2023, Monterey, California, United States

Conference Poster

Abstract

The technological advance of High Numerical Aperture Extreme Ultraviolet Lithography (High NA EUVL) has opened the gates to extensive researches on thinner photoresists (below 30∼nm), necessary for the industrial implementation of High NA EUVL. Consequently, images from Scanning Electron Microscopy (SEM) suffer from reduced imaging contrast and low Signal-to-Noise Ratio (SNR), impacting the measurement of unbiased Line Edge Roughness (uLER) and Line Width Roughness (uLWR). Thus, the aim of this work is to enhance the SNR of SEM images by using a Deep Learning denoiser and enable robust roughness extraction of the thin resist. For this study, we acquired SEM images of Line-Space (L/S) patterns with a Chemically Amplified Resist (CAR) with different thicknesses (15∼nm, 20∼nm, 25∼nm, 30∼nm), underlayers (Spin-On-Glass - SOG, Organic Underlayer - OUL) and frames of averaging (4, 8, 16, 32, and 64∼Fr). After denoising, a systematic analysis has been carried out on both noisy and denoised images using an open-source metrology software, SMILE 2.3.2, for investigating mean CD, SNR improvement factor, biased and unbiased LWR/LER Power Spectral Density (PSD). Denoised images with lower number of frames present unaltered Critical Dimensions (CDs), enhanced SNR (especially for low number of integration frames), and accurate measurements of uLER and uLWR, with the same accuracy as for noisy images with a consistent higher number of frames. Therefore, images with a small number of integration frames and with SNR∼< 2 can be successfully denoised, and advantageously used in improving metrology throughput while maintaining reliable roughness measurements for the thin resist.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Sara Sacchi, Bappaditya Dey, Iacopo Mochi, Sandip Halder, and Philippe Leray "Deep learning denoiser assisted roughness measurements extraction from thin resists with low signal-to-noise-ratio (SNR) SEM images: analysis with SMILE", Proc. SPIE 12750, International Conference on Extreme Ultraviolet Lithography 2023, 1275010 (21 November 2023); https://doi.org/10.1117/12.2687639

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