Abstract
Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.
Highlights
Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies
The thickness of a specimen for TEM analysis is required to be thinner than 100 nm, and this limitation becomes strict in the conventional TEM (CTEM) mode due to image blur caused by inelastic scattering, which cannot be overcome without adding dedicated equipment like a recently developed chromatic aberration corrector[27]
In order to pave a new way to operando 3D observation and make structural analysis available for thick specimens, here we propose a novel approach based on a deep learning method to solve the problem inherent in fast imaging in the STEM mode, i.e., non-trivial noises superimposed in images, which is nearly impossible to remove by conventional noise filtering methods
Summary
Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. One of the most emerging activities in the field of transmission electron microscopy (TEM) is developing novel techniques for dynamic observation of objects being functioning ideally in their natural environment or artificially controlled environment Such observation, so-called in situ or operando microscopy, has been advanced by innovative sampling techniques like a liquid cell[1,2], or functional holders for heating[3,4,5,6] or mechanically deforming[7,8,9,10,11,12] a specimen etc. While 3D atomic structure analysis has shown remarkable results for isolated single n anoparticles[22,23,24,25,26], the low penetrability due to the strong interaction of electron beam with matters often problematic in 3D TEM analysis This becomes severe when the targeted objects are nanometer sized and embedded in other materials as well as Scientific Reports | (2021) 11:20720. A high-angle annular dark-field imaging method is available for the STEM mode, which is suitable to quantify the mass density and chemical composition from the image intensity[30]
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