Frequency-informed deep-learning denoising method supporting sub-nm metrology for high NA EUV lithography

Minjung Kim; Dorin Cerbu; Selim Dogru; Kumara Sastry; Gian Lorusso; Mohamed Zidan; Mohamed Saib; Joren Severi; Danilo De Simone; Vivek Singh

doi:10.1117/12.2661138

28 April 2023 Frequency-informed deep-learning denoising method supporting sub-nm metrology for high NA EUV lithography

Minjung Kim, Dorin Cerbu, Selim Dogru, Kumara Sastry, Gian Lorusso, Mohamed Zidan, Mohamed Saib, Joren Severi, Danilo De Simone, Vivek Singh

Author Affiliations +

Proceedings Volume 12495, DTCO and Computational Patterning II; 124951D (2023) https://doi.org/10.1117/12.2661138
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

Depth of focus reduction due to the increasing numerical aperture (NA) for High NA Extreme Ultraviolet (EUV) lithography and decreasing feature sizes of the latest process nodes necessitate smaller resist thicknesses. Reduced resist thickness degrades scanning electron microscope (SEM) image contrast significantly due to a lower signal-to-noise ratio (SNR). It is possible to improve SNR by changing the number of frames averaging or using higher resolution SEM images. However, these techniques limit high-throughput defect screening and can potentially impact the measurements due to electron beam damage. In this work, we present a deep-learning-based denoising method for sub-nm metrology. Power spectral density analysis of artificial intelligence (AI) reconstructed images shows the developed AI model is capable of denoising SEM images to provide comparable measurements such as line width roughness (LWR) that are only attainable with SEM images with higher SNR.

Conference Presentation

Citation Download Citation

Minjung Kim, Dorin Cerbu, Selim Dogru, Kumara Sastry, Gian Lorusso, Mohamed Zidan, Mohamed Saib, Joren Severi, Danilo De Simone, and Vivek Singh "Frequency-informed deep-learning denoising method supporting sub-nm metrology for high NA EUV lithography", Proc. SPIE 12495, DTCO and Computational Patterning II, 124951D (28 April 2023); https://doi.org/10.1117/12.2661138

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