| Records |
| Author |
Denisov, N.; Jannis, D.; Orekhov, A.; Müller-Caspary, K.; Verbeeck, J. |
| Title |
Characterization of a Timepix detector for use in SEM acceleration voltage range |
Type |
A1 Journal article |
| Year |
2023 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
| Volume |
253 |
Issue |
|
Pages |
113777 |
| Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
| Abstract |
Hybrid pixel direct electron detectors are gaining popularity in electron microscopy due to their excellent properties. Some commercial cameras based on this technology are relatively affordable which makes them attractive tools for experimentation especially in combination with an SEM setup. To support this, a detector characterization (Modulation Transfer Function, Detective Quantum Efficiency) of an Advacam Minipix and Advacam Advapix detector in the 15–30 keV range was made. In the current work we present images of Point Spread Function, plots of MTF/DQE curves and values of DQE(0) for these detectors. At low beam currents, the silicon detector layer behaviour should be dominant, which could make these findings transferable to any other available detector based on either Medipix2, Timepix or Timepix3 provided the same detector layer is used. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Wos |
001026912700001 |
Publication Date |
2023-06-08 |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0304-3991 |
ISBN |
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Additional Links |
UA library record; WoS full record |
| Impact Factor |
2.2 |
Times cited |
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Open Access  |
OpenAccess |
| Notes |
The authors acknowledge the financial support of the Research Foundation Flanders (FWO, Belgium) project SBO S000121N. The authors are grateful to Dr. Lobato for productive discussion of methods. |
Approved |
Most recent IF: 2.2; 2023 IF: 2.843 |
| Call Number |
EMAT @ emat @c:irua:198258 |
Serial |
8815 |
| Permanent link to this record |
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| Author |
Annys, A.; Jannis, D.; Verbeeck, J.; Annys, A.; Jannis, D.; Verbeeck, J. |
| Title |
Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy |
Type |
A1 Journal article |
| Year |
2023 |
Publication |
Scientific reports |
Abbreviated Journal |
|
| Volume |
13 |
Issue |
1 |
Pages |
13724 |
| Keywords |
A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
| Abstract |
Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner. One of the persisting limitations of EELS is the requirement for manual identification of core-loss edges and their corresponding elements. This can be especially bothersome in spectrum imaging, where a large amount of spectra are recorded when spatially scanning over a sample area. This paper introduces a synthetic dataset with 736,000 labeled EELS spectra, computed from available generalized oscillator strength tables, that represents 107 K, L, M or N core-loss edges and 80 chemical elements. Generic lifetime broadened peaks are used to mimic the fine structure due to band structure effects present in experimental core-loss edges. The proposed dataset is used to train and evaluate a series of neural network architectures, being a multilayer perceptron, a convolutional neural network, a U-Net, a residual neural network, a vision transformer and a compact convolutional transformer. An ensemble of neural networks is used to further increase performance. The ensemble network is used to demonstrate fully automated elemental mapping in a spectrum image, both by directly mapping the predicted elemental content and by using the predicted content as input for a physical model-based mapping. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Wos |
001052937600046 |
Publication Date |
2023-08-22 |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2045-2322 |
ISBN |
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Additional Links |
UA library record; WoS full record |
| Impact Factor |
4.6 |
Times cited |
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Open Access |
OpenAccess |
| Notes |
A.A. would like to acknowledge the resources and services used in this work provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government. J.V. acknowledges the IMPRESS project. The IMPRESS project has received funding from the HORIZON EUROPE framework program for research and innovation under grant agreement n. 101094299. |
Approved |
Most recent IF: 4.6; 2023 IF: 4.259 |
| Call Number |
UA @ admin @ c:irua:198647 |
Serial |
8846 |
| Permanent link to this record |
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| Author |
Friedrich, T.; Yu, C.-P.; Verbeeck, J.; Van Aert, S. |
| Title |
Phase object reconstruction for 4D-STEM using deep learning |
Type |
A1 Journal article |
| Year |
2023 |
Publication |
Microscopy and microanalysis |
Abbreviated Journal |
|
| Volume |
29 |
Issue |
1 |
Pages |
395-407 |
| Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
| Abstract |
In this study, we explore the possibility to use deep learning for the reconstruction of phase images from 4D scanning transmission electron microscopy (4D-STEM) data. The process can be divided into two main steps. First, the complex electron wave function is recovered for a convergent beam electron diffraction pattern (CBED) using a convolutional neural network (CNN). Subsequently, a corresponding patch of the phase object is recovered using the phase object approximation. Repeating this for each scan position in a 4D-STEM dataset and combining the patches by complex summation yields the full-phase object. Each patch is recovered from a kernel of 3x3 adjacent CBEDs only, which eliminates common, large memory requirements and enables live processing during an experiment. The machine learning pipeline, data generation, and the reconstruction algorithm are presented. We demonstrate that the CNN can retrieve phase information beyond the aperture angle, enabling super-resolution imaging. The image contrast formation is evaluated showing a dependence on the thickness and atomic column type. Columns containing light and heavy elements can be imaged simultaneously and are distinguishable. The combination of super-resolution, good noise robustness, and intuitive image contrast characteristics makes the approach unique among live imaging methods in 4D-STEM. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Wos |
001033590800038 |
Publication Date |
2023-01-12 |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1431-9276 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
2.8 |
Times cited |
1 |
Open Access |
OpenAccess |
| Notes |
We acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no. 770887 PICOMETRICS) and funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 823717 ESTEEM3. J.V. and S.V.A acknowledge funding from the University of Antwerp through a TOP BOF project. The direct electron detector (Merlin, Medipix3, Quantum Detectors) was funded by the Hercules fund from the Flemish Government. This work was supported by the FWO and FNRS within the 2Dto3D project of the EOS program (grant number 30489208). |
Approved |
Most recent IF: 2.8; 2023 IF: 1.891 |
| Call Number |
UA @ admin @ c:irua:198221 |
Serial |
8912 |
| Permanent link to this record |
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| Author |
Grünewald, L.; Chezganov, D.; De Meyer, R.; Orekhov, A.; Van Aert, S.; Bogaerts, A.; Bals, S.; Verbeeck, J. |
| Title |
In Situ Plasma Studies Using a Direct Current Microplasma in a Scanning Electron Microscope |
Type |
A1 Journal Article |
| Year |
2024 |
Publication |
Advanced Materials Technologies |
Abbreviated Journal |
Adv Materials Technologies |
| Volume |
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Issue |
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Pages |
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| Keywords |
A1 Journal Article; Electron Microscopy for Materials Science (EMAT) ; |
| Abstract |
Microplasmas can be used for a wide range of technological applications and to improve the understanding of fundamental physics. Scanning electron microscopy, on the other hand, provides insights into the sample morphology and chemistry of materials from the mm‐ down to the nm‐scale. Combining both would provide direct insight into plasma‐sample interactions in real‐time and at high spatial resolution. Up till now, very few attempts in this direction have been made, and significant challenges remain. This work presents a stable direct current glow discharge microplasma setup built inside a scanning electron microscope. The experimental setup is capable of real‐time in situ imaging of the sample evolution during plasma operation and it demonstrates localized sputtering and sample oxidation. Further, the experimental parameters such as varying gas mixtures, electrode polarity, and field strength are explored and experimental<italic>V</italic>–<italic>I</italic>curves under various conditions are provided. These results demonstrate the capabilities of this setup in potential investigations of plasma physics, plasma‐surface interactions, and materials science and its practical applications. The presented setup shows the potential to have several technological applications, for example, to locally modify the sample surface (e.g., local oxidation and ion implantation for nanotechnology applications) on the µm‐scale. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Wos |
001168639900001 |
Publication Date |
2024-02-25 |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2365-709X |
ISBN |
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Additional Links |
UA library record; WoS full record |
| Impact Factor |
6.8 |
Times cited |
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Open Access |
OpenAccess |
| Notes |
L.G., S.B., and J.V. acknowledge support from the iBOF-21-085 PERsist research fund. D.C., S.V.A., and J.V. acknowledge funding from a TOPBOF project of the University of Antwerp (FFB 170366). R.D.M., A.B., and J.V. acknowledge funding from the Methusalem project of the University of Antwerp (FFB 15001A, FFB 15001C). A.O. and J.V. acknowledge funding from the Research Foundation Flanders (FWO, Belgium) project SBO S000121N. |
Approved |
Most recent IF: 6.8; 2024 IF: NA |
| Call Number |
EMAT @ emat @c:irua:204363 |
Serial |
8995 |
| Permanent link to this record |
