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Author |
Friedrich, T.; Yu, C.-P.; Verbeeck, J.; Van Aert, S. |
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Title |
Phase object reconstruction for 4D-STEM using deep learning |
Type |
A1 Journal article |
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Year  |
2023 |
Publication |
Microscopy and microanalysis |
Abbreviated Journal |
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Volume |
29 |
Issue |
1 |
Pages |
395-407 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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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. |
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Wos |
001033590800038 |
Publication Date |
2023-01-12 |
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ISSN |
1431-9276 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.8 |
Times cited |
1 |
Open Access |
OpenAccess |
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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 |
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Call Number |
UA @ admin @ c:irua:198221 |
Serial |
8912 |
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Author |
Yu, C.-P. |
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Title |
Novel imaging methods of transmission electron microscopy based on electron beam scattering and modulation |
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Doctoral thesis |
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Year |
2023 |
Publication |
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Pages |
x, 154 p. |
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Keywords |
Doctoral thesis; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
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Abstract |
Transmission electron microscopy (TEM) is a technique that uses an electron beam to analyze materials. This analysis is based on the interaction between the electron beam and the sample, such as photon emission and electron diffraction pattern, to name a few. Sample damage, however, also occurs when such interaction alters the structure of the sample. To ensure information from the undamaged material can be acquired, the electron expense to probe the material is thus limited. In this work, we propose efficient methods for acquiring and processing the information originating from the electron-sample interaction so that the study of the material and the conducting of the TEM experiment can be less hindered by the limited dose usage. In the first part of the work, the relationship between the scattering of the electron and the local physical property of the sample is studied. Based on this relationship, two reconstruction schemes are proposed capable of producing high-resolution images at low-dose conditions. Besides, the proposed reconstructions are not restricted to complete datasets but instead work on pieces of data, therefore allowing live feedback during data acquisition. Such feature of the methods allows the whole TEM experiment to be carried out under low dose conditions and thus further reduces possible beam damage on the studied material. In the second part of the work, we discuss our approach to modulating the electron beam and its benefits. An electrostatic device that can alter the wavefront of the passing electron wave is introduced and characterized. The beam-modulation ability is demonstrated by creating orthogonal beam sets, and applications that exploit the adaptability of the wave modulator are demonstrated with both simulation and experiments. |
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987-90-5728-534-7 |
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UA library record |
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no |
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Call Number |
UA @ admin @ c:irua:200885 |
Serial |
9064 |
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Author |
De wael, A.; De Backer, A.; Yu, C.-P.; Sentürk, D.G.; Lobato, I.; Faes, C.; Van Aert, S. |
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Title |
Three Approaches for Representing the Statistical Uncertainty on Atom-Counting Results in Quantitative ADF STEM |
Type |
A1 Journal article |
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Year |
2022 |
Publication |
Microscopy and microanalysis |
Abbreviated Journal |
Microsc Microanal |
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Pages |
1-9 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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A decade ago, a statistics-based method was introduced to count the number of atoms from annular dark-field scanning transmission electron microscopy (ADF STEM) images. In the past years, this method was successfully applied to nanocrystals of arbitrary shape, size, and composition (and its high accuracy and precision has been demonstrated). However, the counting results obtained from this statistical framework are so far presented without a visualization of the actual uncertainty about this estimate. In this paper, we present three approaches that can be used to represent counting results together with their statistical error, and discuss which approach is most suited for further use based on simulations and an experimental ADF STEM image. |
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Wos |
000854930500001 |
Publication Date |
2022-09-19 |
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ISSN |
1431-9276 |
ISBN |
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Additional Links |
UA library record; WoS full record |
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Impact Factor |
2.8 |
Times cited |
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Open Access |
OpenAccess |
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Notes |
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 770887 and No. 823717 ESTEEM3). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through grants to A.D.w. and A.D.B. and projects G.0502.18N, G.0267.18N, and EOS 30489208. S.V.A. acknowledges TOP BOF funding from the University of Antwerp. The authors are grateful to L.M. Liz-Marzán (CIC biomaGUNE and Ikerbasque) for providing the samples. esteem3reported; esteem3jra |
Approved |
Most recent IF: 2.8 |
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Call Number |
EMAT @ emat @c:irua:190585 |
Serial |
7119 |
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Author |
Yu, C.-P.; Friedrich, T.; Jannis, D.; Van Aert, S.; Verbeeck, J. |
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Title |
Real-Time Integration Center of Mass (riCOM) Reconstruction for 4D STEM |
Type |
A1 Journal article |
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Year |
2022 |
Publication |
Microscopy and microanalysis |
Abbreviated Journal |
Microsc Microanal |
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Volume |
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Issue |
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Pages |
1-12 |
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Keywords |
A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
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Abstract |
A real-time image reconstruction method for scanning transmission electron microscopy (STEM) is proposed. With an algorithm requiring only the center of mass of the diffraction pattern at one probe position at a time, it is able to update the resulting image each time a new probe position is visited without storing any intermediate diffraction patterns. The results show clear features at high spatial frequency, such as atomic column positions. It is also demonstrated that some common post-processing methods, such as band-pass filtering, can be directly integrated in the real-time processing flow. Compared with other reconstruction methods, the proposed method produces high-quality reconstructions with good noise robustness at extremely low memory and computational requirements. An efficient, interactive open source implementation of the concept is further presented, which is compatible with frame-based, as well as event-based camera/file types. This method provides the attractive feature of immediate feedback that microscope operators have become used to, for example, conventional high-angle annular dark field STEM imaging, allowing for rapid decision-making and fine-tuning to obtain the best possible images for beam-sensitive samples at the lowest possible dose. |
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Wos |
000792176100001 |
Publication Date |
2022-04-25 |
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ISSN |
1431-9276 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.8 |
Times cited |
7 |
Open Access |
OpenAccess |
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Notes |
Bijzonder Onderzoeksfonds UGent; H2020 European Research Council, 770887 ; H2020 European Research Council, 823717 ; H2020 European Research Council, ESTEEM3 / 823717 ; H2020 European Research Council, PICOMETRICS / 770887 ; Fonds Wetenschappelijk Onderzoek, 30489208 ; Herculesstichting; esteem3reported; esteem3jra |
Approved |
Most recent IF: 2.8 |
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Call Number |
EMAT @ emat @c:irua:188538 |
Serial |
7068 |
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Author |
Friedrich, T.; Yu, C.-P.; Verbeek, J.; Pennycook, T.; Van Aert, S. |
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Title |
Phase retrieval from 4-dimensional electron diffraction datasets |
Type |
P1 Proceeding |
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Year |
2021 |
Publication |
Proceedings
T2 – IEEE International Conference on Image Processing (ICIP), SEP 19-22, 2021, Electr. network |
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Pages |
3453-3457 |
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Keywords |
P1 Proceeding; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
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Abstract |
We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition. |
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000819455103114 |
Publication Date |
2021-08-23 |
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978-1-6654-4115-5 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Open Access |
OpenAccess |
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Notes |
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Approved |
Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:189462 |
Serial |
7089 |
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