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.

翻译：高数值孔径极紫外光刻（High NA EUVL）的技术进步为薄光刻胶（低于30nm）的广泛研究打开了大门，这对High NA EUVL的工业实施至关重要。因此，扫描电子显微镜（SEM）图像存在成像对比度降低和信噪比（SNR）低的问题，影响无偏线边缘粗糙度（uLER）和线宽粗糙度（uLWR）的测量。本研究旨在通过使用深度学习去噪器增强SEM图像的SNR，实现对薄光刻胶的稳健粗糙度提取。为此，我们获取了不同厚度（15nm、20nm、25nm、30nm）、不同底层（旋涂玻璃-SOG、有机底层-OUL）以及不同平均帧数（4、8、16、32和64帧）的化学放大光刻胶（CAR）线-空间（L/S）图案的SEM图像。去噪后，使用开源计量软件SMILE 2.3.2对有噪声和去噪后的图像进行系统分析，研究平均CD、SNR提升因子、有偏和无偏LWR/LER功率谱密度（PSD）。结果表明，帧数较少的去噪图像展现出未改变的关键尺寸（CD）、增强的SNR（尤其对于低积分帧数），以及以与具有一致较高帧数的噪声图像相同精度测量uLER和uLWR的能力。因此，积分帧数少且SNR < 2的图像可成功去噪，并有利地用于提高计量通量，同时保持薄光刻胶的可靠粗糙度测量。