Simultaneous Denoising and Edge Estimation from SEM Images using Deep Convolutional Neural Networks

Abstract

We propose deep convolutional neural networks LineNet1 and LineNet2 for simultaneous denoising and edge image prediction from low-dose scanning electron microscope images. Edge estimation of nanostructures from SEM images is needed for line edge roughness (LER) and line width roughness (LWR) estimation. Our method uses supervised learning datasets of single-line SEM images and multiple-line SEM images together with edge positions information for the training of LineNet1 and LineNet2. We simulate single-line and multiple-line SEM images with Poisson noise and other artifacts using the ARTIMAGEN library developed by the National Institute of Standards and Technology. The line edges were generated using the Thorsos method and the Palasantzas spectral model. The convolutional neural networks LineNet1 and LineNet2 each contain 17 con- volutional layers, 16 batch-normalization layers and 16 dropout layers. Our results show that this approach (1) facilitates edge estimation in multiple-line images and (2) significantly reduces the memory needed for edge estimation in single-line images with a slight impact on accuracy.