Online Thickness Determination with Position Averaged Convergent Beam Electron Diffraction using Convolutional Neural Networks

Abstract

Position averaged convergent beam electron diffraction (PACBED) is one of the most convenient and precise thickness determination techniques available in a scanning transmission electron microscope. The thickness is determined by finding the best match of the recorded PACBED pattern with a series of simulated diffraction patterns by visual inspection. The automatization of this process can be enhanced by convolutional neural networks (CNNs), making the method fast and easy to apply. However, the simulation of a synthetic dataset and the training of the CNNs carry a high computational cost. With the aim to simplify this process, we propose to build a server-based database of pretrained CNN models that is accessed by the user via a web service directly from the data acquisition and analysis software. We demonstrate a working prototype comprised of a shared CNN database containing three material systems. By this, the microscope operator can determine the specimen thickness by PACBED within a few seconds in a reproducible way during a microscope session, without any prior knowledge about machine learning or multislice modeling. Furthermore, the service is integrated into other software and workflows through the API.