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| Author | Lobato, I.; De Backer, A.; Van Aert, S. | ||||
| Title | Real-time simulations of ADF STEM probe position-integrated scattering cross-sections for single element fcc crystals in zone axis orientation using a densely connected neural network | Type | A1 Journal article | ||
| Year | 2023 | Publication | Ultramicroscopy | Abbreviated Journal | Ultramicroscopy |
| Volume | 251 | Issue | Pages | 113769 | |
| Keywords | A1 Journal article; Electron microscopy for materials research (EMAT) | ||||
| Abstract | Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM) images in terms of composition or thickness often relies on probe-position integrated scattering cross sections (PPISCS). In order to compare experimental PPISCS with theoretically predicted ones, expensive simulations are needed for a given specimen, zone axis orientation, and a variety of microscope settings. The computation time of such simulations can be in the order of hours using a single GPU card. ADF STEM simulations can be efficiently parallelized using multiple GPUs, as the calculation of each pixel is independent of other pixels. However, most research groups do not have the necessary hardware, and, in the best-case scenario, the simulation time will only be reduced proportionally to the number of GPUs used. In this manuscript, we use a learning approach and present a densely connected neural network that is able to perform real-time ADF STEM PPISCS predictions as a function of atomic column thickness for most common face-centered cubic (fcc) crystals (i.e., Al, Cu, Pd, Ag, Pt, Au and Pb) along [100] and [111] zone axis orientations, root-mean-square displacements, and microscope parameters. The proposed architecture is parameter efficient and yields accurate predictions for the PPISCS values for a wide range of input parameters that are commonly used for aberration-corrected transmission electron microscopes. |
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| Language | Wos | 001011617200001 | Publication Date | 2023-06-01 | |
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | 0304-3991 | ISBN | Additional Links | UA library record; WoS full record | |
| Impact Factor | 2.2 | Times cited | Open Access | OpenAccess | |
| Notes | This work was supported by the European Research Council (Grant 770887 PICOMETRICS to S. Van Aert). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G034621N and G0A7723N) and a postdoctoral grant to A. De Backer. S. Van Aert acknowledges funding from the University of Antwerp Research fund (BOF), Belgium. | Approved | Most recent IF: 2.2; 2023 IF: 2.843 | ||
| Call Number | EMAT @ emat @c:irua:197275 | Serial | 8812 | ||
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| Author | Lobato, I.; Friedrich, T.; Van Aert, S. | ||||
| Title | Deep convolutional neural networks to restore single-shot electron microscopy images | Type | A1 Journal Article | ||
| Year | 2024 | Publication | N P J Computational Materials | Abbreviated Journal | npj Comput Mater |
| Volume | 10 | Issue | 1 | Pages | 10 |
| Keywords | A1 Journal article; Electron microscopy for materials research (EMAT) | ||||
| Abstract | Advanced electron microscopy techniques, including scanning electron microscopes (SEM), scanning transmission electron microscopes (STEM), and transmission electron microscopes (TEM), have revolutionized imaging capabilities. However, achieving high-quality experimental images remains a challenge due to various distortions stemming from the instrumentation and external factors. These distortions, introduced at different stages of imaging, hinder the extraction of reliable quantitative insights. In this paper, we will discuss the main sources of distortion in TEM and S(T)EM images, develop models to describe them, and propose a method to correct these distortions using a convolutional neural network. We validate the effectiveness of our method on a range of simulated and experimental images, demonstrating its ability to significantly enhance the signal-to-noise ratio. This improvement leads to a more reliable extraction of quantitative structural information from the images. In summary, our findings offer a robust framework to enhance the quality of electron microscopy images, which in turn supports progress in structural analysis and quantification in materials science and biology. | ||||
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| Language | Wos | 001138183000001 | Publication Date | 2024-01-09 | |
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| ISSN | 2057-3960 | ISBN | Additional Links | UA library record; WoS full record | |
| Impact Factor | Times cited | Open Access | OpenAccess | ||
| Notes | This work was supported by the European Research Council (Grant 770887 PICOMETRICS to S.V.A.). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G034621N, G0A7723N and EOS 40007495). S.V.A. acknowledges funding from the University of Antwerp Research Fund (BOF). The authors thank Lukas Grünewald for data acquisition and support for Fig. 7. | Approved | Most recent IF: NA | ||
| Call Number | EMAT @ emat @c:irua:202714 | Serial | 8994 | ||
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