Deep Learning-Based Autonomous Scanning Electron Microscope

Jonggyu Jang, Hyeonsu Lyu, Hyun Jong Yang, Moohyun Oh, Junhee Lee

Abstract: By virtue of their ultra high resolution, scanning electron microscopes (SEMs) are essential to study topography, morphology, composition, and crystallography of materials, and thus are widely used for advanced researches in physics, chemistry, pharmacy, geology, etc. The major hindrance of using SEMs is that obtaining high quality images from SEMs requires a professional control of many control parameters. Therefore, it is not an easy task even for an experienced researcher to get high quality sample images without any help from SEM experts. In this paper, we propose and implement a deep learning-based autonomous SEM machine, which assesses image quality and controls parameters autonomously to get high quality sample images just as if human experts do. This world's first autonomous SEM machine may be the first step to bring SEMs, previously used only for advanced researches due to its difficulty in use, into much broader applications such as education, manufacture, and mechanical diagnosis, which are previously meant for optical microscopes.