Robust Deep-learning Based Autofocus Score Prediction for Scanning Electron Microscope

Abstract

Introduction: Deep-learning has come into its own in analyzing scanning electron microscope (SEM) images [1]. As the image analysis part is becoming autonomous with the aid of deep-learning, the main challenge in the use of SEMs is to get high quality images by deftly controlling SEM parameters such as brightness, contrast, focus, and etc. While the other studies attempt to use deep-learning as a classifier or analyzer for images, our previous work [2] has shown that deep-learning can be potentially used as a controller of SEM parameters. To automate the SEM control, the most crucial part is to accurately evaluate the quality of input SEM images as if SEM experts do, because existing mathematical autofocus (AF) metrics cannot capture the image quality by scrutinizing both regional features and the entire image for a variety of types of samples. The deep-learning based algorithm proposed in [2] has shown to mimic SEM experts better in judging the quality of SEM images than the existing AF algorithms [3], [4], [5], which ultimately results in a full automation of SEM control.