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
The technological advance of High Numerical Aperture Extreme Ultraviolet
Lithography (High NA EUVL) has opened the gates to extensive researches on
thinner photoresists (below 30nm), 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
(15nm, 20nm, 25nm, 30nm), 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.