Extraction of roughness measurements from thin resists with low signal-to-noise-ratio (SNR) SEM images by applying deep learning denoiser

Background: thin resists, below 30 nm, suffer from reduced imaging contrast and signal-to-noise ratio (SNR). This has an impact on the unbiased Line Width Roughness (uLWR) estimation, which becomes artificially low and hence is inaccurate when the SNR drops below certain limit, while reaching an accurate plateau value at higher SNR. Aim: improve the SNR of Scanning Electron Microscope (SEM) images in order to achieve a more reliable and robust roughness measurements on the thin resist, without having to increase the measurement electron dose. Approach: apply an unsupervised machine learning denoising algorithm to SEM raw images of thin resists on two different underlayers. The images were captured using different frame averaging (4, 8, 16, 32, and 64 frames). A systematic analysis is performed to compare the measurements before and after denoising. Results: after denoising, the SNR is improved, the mean CD stayed unchanged, and the roughness using smaller number of frames got closer to the accurate values, obtained using a larger number of frames. Conclusions: we have demonstrated that the use of a machine learning-based denoising algorithm enhances the SNR of SEM images without changing the mean CD and is beneficial for accurate and robust roughness measurements of thin resist.