Deep learning denoising of SEM images towards noise-reduced LER measurements

As chip sizes decrease and node dimensions break the sub-10 nm barrier, Line Edge Roughness (LER) metrology becomes a critical issue for the semiconductor research and industry. Scanning Electron Microscopy (SEM) imaging being the widely used tool for LER metrology suffers from the presence of noise that degrades measurement accuracy. To solve this issue without damaging the measured pattern, the applicability of deep Convolutional Neural Networks (CNNs) is explored, tackling the problem at the image level. The SEM image Denoising model (SEMD) is trained on synthesized image data to detect the variations of noise and provides as an output a denoised image of the pattern. The results are presented and compared with the state-of-art predictions, showing the effectiveness and enhanced performance of the SEMD method.