SEM Image Denoising and Contour Image Estimation using Deep Learning

Abstract

The estimation of line and contour geometries from real SEM images is a challenging problem due to the corruption of such images by Poisson noise, edge effects, and other SEM artifacts. We attempt simultaneous contour edge image prediction and SEM image denoising using a deep convolutional neural network LineNet2. To capture a range of edge effects in real SEM images, we simulate a training dataset of rough line SEM images with random edge effect parameters. We train the LineNet2 network on this training dataset and randomly rotate the images during the training phase. The retrained LineNet2 shows the ability to denoise real SEM images of line and contour geometries. We measure the line edge roughness (LER) parameter in isolated and dense regions of rough line images through multiple LER methods. Our experiments also demonstrate that the network can learn to recognize contour edges just by rotating rough line images.