Increasing the Utilization of Deep Neural Networks for SEM Measurements Through Multiple Task Formulation and Visualization

Abstract

Scanning electron microscopy images are an attractive option to estimate the roughness of nanostructures. Convolutional neural network (CNN) based algorithms have improved scanning electron microscope (SEM) image denoising and estimation of line roughness measurements. However, these algorithms need improvements to run at high speeds with a low memory footprint and without compromising accuracy. We introduce two approaches to reduce computation time and memory. We first propose deep CNNs LineNet1 and LineNet2 to perform simultaneous denoising and edge estimation on rough line SEM images. This multiple task formulation in LineNet1 and LineNet2 reduces training time, inference time and model sizes. LineNet2 also facilitates edge estimation in the multiple-line images and generalizes the approach for other geometries. Our training method uses supervised learning datasets of single-line SEM images and multiple-line SEM images together with edge positions information. We next consider multiple visualization tools to improve our understanding of the LineNet1 architecture and use the resulting insights from these visualizations to motivate a study of two variations of LineNet1 with fewer neural network layers. One of these visualization techniques is new to the visualization of denoising CNNs. Our results show that these approaches significantly reduce the memory and computation needed for edge estimation with a slight impact on accuracy.