Transmission Electron Microscopy

Transmission electron microscopy (TEM) is a powerful imaging technique used to visualize materials at the atomic scale, with primary objectives of high-resolution imaging and detailed structural analysis. Current research heavily emphasizes the use of deep learning, particularly convolutional neural networks (CNNs) like U-Nets and Mask R-CNNs, along with other algorithms such as long short-term memory (LSTM) networks, to automate image processing, segmentation, and analysis tasks, addressing challenges like noise reduction and efficient handling of large datasets. This automated analysis significantly accelerates materials characterization, enabling faster identification of defects, phase transitions, and other crucial structural features, with broad applications in materials science, nanotechnology, and biological imaging.