Line roughness estimation and Poisson denoising in scanning electron microscope images using deep learning

Abstract. We propose the use of deep supervised learning for the estimation of line edge roughness (LER) and line width roughness (LWR) in low-dose scanning electron microscope (SEM) images. We simulate a supervised learning dataset of 100,800 SEM rough line images constructed by means of the Thorsos method and the ARTIMAGEN library developed by the National Institute of Standards and Technology. We also devise two separate deep convolutional neural networks called SEMNet and EDGENet, each of which has 17 convolutional layers, 16 batch normalization layers, and 16 dropout layers. SEMNet performs the Poisson denoising of SEM images, and it is trained with a dataset of simulated noisy-original SEM image pairs. EDGENet directly estimates the edge geometries from noisy SEM images, and it is trained with a dataset of simulated noisy SEM image-edge array pairs. SEMNet achieved considerable improvements in peak signal-to-noise ratio as well as the best LER/LWR estimation accuracy compared with standard image denoisers. EDGENet offers excellent LER and LWR estimation as well as roughness spectrum estimation.