A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management

Abstract

Transmission electron microscopy (TEM) enables users to study materials at their fundamental, atomic scale. Complex experiments routinely generate thousands of images with numerous parameters that require time-consuming and complicated analysis. AXON synchronicity is a machine-vision synchronization (MVS) software solution designed to address the pain points inherent to TEM studies. Once installed on the microscope, it enables the continuous synchronization of images and metadata generated by the microscope, detector, and in situ systems during an experiment. This connectivity enables the application of machine-vision algorithms that apply a combination of spatial, beam, and digital corrections to center and track a region of interest within the field of view and provide immediate image stabilization. In addition to the substantial improvement in resolution afforded by such stabilization, metadata synchronization enables the application of computational and image analysis algorithms that calculate variables between images. This calculated metadata can be used to analyze trends or identify key areas of interest within a dataset, leading to new insights and the development of more sophisticated machine-vision capabilities in the future. One such module that builds on this calculated metadata is dose calibration and management. The dose module provides state-of-the-art calibration, tracking, and management of both the electron fluence (e^-/Ų·s^-1) and cumulative dose (e^-/Ų) that is delivered to specific areas of the sample on a pixel-by-pixel basis. This enables a comprehensive overview of the interaction between the electron beam and the sample. Experiment analysis is streamlined through a dedicated analysis software in which datasets consisting of images and corresponding metadata are easily visualized, sorted, filtered, and exported. Combined, these tools facilitate efficient collaborations and experimental analysis, encourage data mining and enhance the microscopy experience.