Probe and object function reconstruction in incoherent stem imaging

Nellist, P.D.; Pennycook, S.J.

doi:10.2172/367250

Cited by 1 publication

(5 citation statements)

References 3 publications

(3 reference statements)

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“…Exit-wave reconstruction and object function reconstruction have been developed to avoid this misinterpretation. 21 − 24 It facilitates studies of rapid movement of atoms in atomic-resolution TEM. However, the high-angle annular dark-field STEM (HAADF-STEM) analysis relies on HAADF-STEM image simulation.…”

Section: Introductionmentioning

confidence: 99%

“… 25 However, it divides out the contrast transfer function in the reciprocal lattice, resulting in a constant transfer function without decay. 24 The constant transfer function from the Wiener filter can introduce artifacts in the STEM image. 24 Our solution is deep-learning-based object function reconstruction from the considered factors by the probe function.…”

Section: Introductionmentioning

confidence: 99%

“… 24 The constant transfer function from the Wiener filter can introduce artifacts in the STEM image. 24 Our solution is deep-learning-based object function reconstruction from the considered factors by the probe function. Deep learning has been solving challenges in electron microscopy.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, in the case of in situ STEM, light elements, specimen drift, and low-dose imaging for time resolution, as well as electron beam-sensitive materials, make it difficult to understand the exact atomic structure and evolution of 2D materials. Exit-wave reconstruction and object function reconstruction have been developed to avoid this misinterpretation. − It facilitates studies of rapid movement of atoms in atomic-resolution TEM. However, the high-angle annular dark-field STEM (HAADF-STEM) analysis relies on HAADF-STEM image simulation.…”

Section: Introductionmentioning

confidence: 99%

“…The most prominent method is Wiener filtering, which is a linear denoising solution . However, it divides out the contrast transfer function in the reciprocal lattice, resulting in a constant transfer function without decay . The constant transfer function from the Wiener filter can introduce artifacts in the STEM image .…”

Section: Introductionmentioning

confidence: 99%

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In Situ Scanning Transmission Electron Microscopy Study of MoS₂ Formation on Graphene with a Deep-Learning Framework

Lee

Chung

et al. 2021

ACS Omega

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Atomic-scale information is essential for understanding and designing unique structures and properties of two-dimensional (2D) materials. Recent developments in in situ transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) enable research to provide abundant insights into the growth of nanomaterials. In this study, 2D MoS 2 is synthesized on a suspended graphene substrate inside a TEM column through thermolysis of the ammonium tetrathiomolybdate (NH 4 ) 2 MoS 4 precursor at 500 °C. To avoid misinterpretation of the in situ STEM images, a deep-learning framework, DeepSTEM, is developed. The DeepSTEM framework successfully reconstructs an object function in atomic-resolution STEM imaging for accurate determination of the atomic structure and dynamic analysis. In situ STEM imaging with DeepSTEM enables observation of the edge configuration, formation, and reknitting progress of MoS 2 clusters with the formation of a mirror twin boundary. The synthesized MoS 2 /graphene heterostructure shows various twist angles, as revealed by atomic-resolution TEM. This deep-learning framework-assisted in situ STEM imaging provides atomic information for in-depth studies on the growth and structure of 2D materials and shows the potential use of deep-learning techniques in 2D material research.

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Section: Introductionmentioning

confidence: 99%