Publications

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Elsaka, B., Yang, X., Kästner, P., Dingel, K., Sick, B., Lehmann, P., Buhmann, S.Y., Hillmer, H.: Casimir Effect in MEMS: Materials, Geometries, and Metrologies—A Review. Materials 2024, Vol. 17, Page 3393. 17, 3393 (2024). https://doi.org/10.3390/MA17143393.
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Casimir force densities, i.e., force per area, become very large if two solid material surfaces come closer together to each other than 10 nm. In most cases, the forces are attractive. In some cases, they can be repulsive depending on the solid materials and the fluid medium in between. This review provides an overview of experimental and theoretical studies that have been performed and focuses on four main aspects: (i) the combinations of different materials, (ii) the considered geometries, (iii) the applied experimental measurement methodologies and (iv) a novel self-assembly methodology based on Casimir forces. Briefly reviewed is also the influence of additional parameters such as temperature, conductivity, and surface roughness. The Casimir effect opens many application possibilities in microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS), where an overview is also provided. The knowledge generation in this fascinating field requires interdisciplinary approaches to generate synergetic effects between technological fabrication metrology, theoretical simulations, the establishment of adequate models, artificial intelligence, and machine learning. Finally, multiple applications are addressed as a research roadmap.
@article{Elsaka2024, abstract = {Casimir force densities, i.e., force per area, become very large if two solid material surfaces come closer together to each other than 10 nm. In most cases, the forces are attractive. In some cases, they can be repulsive depending on the solid materials and the fluid medium in between. This review provides an overview of experimental and theoretical studies that have been performed and focuses on four main aspects: (i) the combinations of different materials, (ii) the considered geometries, (iii) the applied experimental measurement methodologies and (iv) a novel self-assembly methodology based on Casimir forces. Briefly reviewed is also the influence of additional parameters such as temperature, conductivity, and surface roughness. The Casimir effect opens many application possibilities in microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS), where an overview is also provided. The knowledge generation in this fascinating field requires interdisciplinary approaches to generate synergetic effects between technological fabrication metrology, theoretical simulations, the establishment of adequate models, artificial intelligence, and machine learning. Finally, multiple applications are addressed as a research roadmap.}, author = {Elsaka, Basma and Yang, Xiaohui and Kästner, Philipp and Dingel, Kristina and Sick, Bernhard and Lehmann, Peter and Buhmann, Stefan Yoshi and Hillmer, Hartmut}, journal = {Materials 2024, Vol. 17, Page 3393}, keywords = {effect,Van,Waals}, month = {07}, number = 14, pages = 3393, publisher = {Multidisciplinary Digital Publishing Institute}, title = {Casimir Effect in MEMS: Materials, Geometries, and Metrologies—A Review}, volume = 17, year = 2024 }
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Akhundzada, S., Dingel, K., Bischof, D., Janzen, C., Sick, B., Ehresmann, A.: Domain Imaging in Periodic Submicron Wide Nanostructures by Digital Drift Correction in Kerr Microscopy. Advanced Photonics Research. 4, 2300170 (2023). https://doi.org/https://doi.org/10.1002/adpr.202300170.
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Magneto-optical Kerr microscopy is a powerful method for imaging magnetic domains. Even though domain imaging below the diffractive resolution limit is possible, such investigations are getting increasingly complex with decreasing structure size due to the decreasing Kerr contrast. As magnetic domain images free of topographical artifacts are obtained by subtracting a reference image from the actual image, the corresponding challenges are additionally increased by unavoidable sample motion in the time interval between acquiring the two images. Software-based drift corrections typically rely on a unique structure in the image's region of interest (ROI), recognized automatically or selected manually by the user. By digital image shifting, the ROI positions in the actual and reference images are aligned, and the sample motion is compensated. For magnetic domain imaging in periodically arranged micro- or nano-objects, unique topographical features are not given, making the drift correction by ROIs difficult, often even impossible. Herein, a novel software-based approach is presented for drift corrections to image domains with features close/below the optical resolution limit and for investigating periodically arranged micro- or nano-objects without utilizing ROIs. High-contrast images are obtained, enabling the characterization of periodically arranged 1D, 2D, and 3D magnetic objects with lateral dimensions below 100 nm.
@article{https://doi.org/10.1002/adpr.202300170, abstract = {Magneto-optical Kerr microscopy is a powerful method for imaging magnetic domains. Even though domain imaging below the diffractive resolution limit is possible, such investigations are getting increasingly complex with decreasing structure size due to the decreasing Kerr contrast. As magnetic domain images free of topographical artifacts are obtained by subtracting a reference image from the actual image, the corresponding challenges are additionally increased by unavoidable sample motion in the time interval between acquiring the two images. Software-based drift corrections typically rely on a unique structure in the image's region of interest (ROI), recognized automatically or selected manually by the user. By digital image shifting, the ROI positions in the actual and reference images are aligned, and the sample motion is compensated. For magnetic domain imaging in periodically arranged micro- or nano-objects, unique topographical features are not given, making the drift correction by ROIs difficult, often even impossible. Herein, a novel software-based approach is presented for drift corrections to image domains with features close/below the optical resolution limit and for investigating periodically arranged micro- or nano-objects without utilizing ROIs. High-contrast images are obtained, enabling the characterization of periodically arranged 1D, 2D, and 3D magnetic objects with lateral dimensions below 100 nm.}, author = {Akhundzada, Sapida and Dingel, Kristina and Bischof, David and Janzen, Christian and Sick, Bernhard and Ehresmann, Arno}, journal = {Advanced Photonics Research}, keywords = {imported}, number = 10, pages = 2300170, title = {Domain Imaging in Periodic Submicron Wide Nanostructures by Digital Drift Correction in Kerr Microscopy}, volume = 4, year = 2023 }
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Huhnstock, R., Paetzold, L., Merkel, M., Kuświk, P., Ehresmann, A.: Combined Funnel, Concentrator, and Particle Valve Functional Element for Magnetophoretic Bead Transport Based on Engineered Magnetic Domain Patterns. Small. n/a, 2305675 (2023). https://doi.org/https://doi.org/10.1002/smll.202305675.
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Abstract Controlled actuation of superparamagnetic beads (SPBs) within a microfluidic environment using tailored dynamic magnetic field landscapes (MFLs) is a potent approach for the realization of point-of-care diagnostics within Lab-on-a-chip (LOC) systems. Making use of an engineered magnetic domain pattern as the MFL source, a functional LOC-element with combined magnetophoretic “funnel”, concentrator, and “valve” functions for micron-sized SPBs is presented. A parallel-stripe domain pattern design with periodically decreasing/increasing stripe lengths is fabricated in a topographically flat continuous exchange biased (EB) thin film system by ion bombardment induced magnetic patterning (IBMP). It is demonstrated that, upon application of external magnetic field pulses, a fully reversible concentration of SPBs at the domain pattern's focal point occurs. In addition, it is shown that this functionality may be used as an SPB “funnel”, allowing only a maximum number of particles to pass through the focal point. Adjusting the pulse time length, the focal point can be clogged up for incoming SPBs, resembling an on/off switchable particle “valve”. The observations are supported by quantitative theoretical force considerations.
@article{https://doi.org/10.1002/smll.202305675, abstract = {Abstract Controlled actuation of superparamagnetic beads (SPBs) within a microfluidic environment using tailored dynamic magnetic field landscapes (MFLs) is a potent approach for the realization of point-of-care diagnostics within Lab-on-a-chip (LOC) systems. Making use of an engineered magnetic domain pattern as the MFL source, a functional LOC-element with combined magnetophoretic “funnel”, concentrator, and “valve” functions for micron-sized SPBs is presented. A parallel-stripe domain pattern design with periodically decreasing/increasing stripe lengths is fabricated in a topographically flat continuous exchange biased (EB) thin film system by ion bombardment induced magnetic patterning (IBMP). It is demonstrated that, upon application of external magnetic field pulses, a fully reversible concentration of SPBs at the domain pattern's focal point occurs. In addition, it is shown that this functionality may be used as an SPB “funnel”, allowing only a maximum number of particles to pass through the focal point. Adjusting the pulse time length, the focal point can be clogged up for incoming SPBs, resembling an on/off switchable particle “valve”. The observations are supported by quantitative theoretical force considerations.}, author = {Huhnstock, Rico and Paetzold, Lukas and Merkel, Maximilian and Kuświk, Piotr and Ehresmann, Arno}, journal = {Small}, keywords = {imported}, number = {n/a}, pages = 2305675, title = {Combined Funnel, Concentrator, and Particle Valve Functional Element for Magnetophoretic Bead Transport Based on Engineered Magnetic Domain Patterns}, volume = {n/a}, year = 2023 }
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Meier, D., Ragunathan, R., Degener, S., Liehr, A., Vollmer, M., Niendorf, T., Sick, B.: Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning. Scientific Reports 2023 13:1. 13, 1–12 (2023). https://doi.org/10.1038/s41598-023-31580-1.
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X-ray diffraction crystallography allows non-destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X-ray diffraction has been highly time-consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep-learning-based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU-based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.
@article{Meier2023, abstract = {X-ray diffraction crystallography allows non-destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X-ray diffraction has been highly time-consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep-learning-based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU-based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.}, author = {Meier, David and Ragunathan, Rishan and Degener, Sebastian and Liehr, Alexander and Vollmer, Malte and Niendorf, Thomas and Sick, Bernhard}, journal = {Scientific Reports 2023 13:1}, keywords = {methods,Imaging}, month = {04}, number = 1, pages = {1-12}, publisher = {Nature Publishing Group}, title = {Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning}, volume = 13, year = 2023 }
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Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses. Scientific Reports. 12, 17809- (2022). https://doi.org/10.1038/s41598-022-21646-x.
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X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.
@article{dingel2022artificial, abstract = {X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.}, author = {Dingel, Kristina and Otto, Thorsten and Marder, Lutz and Funke, Lars and Held, Arne and Savio, Sara and Hans, Andreas and Hartmann, Gregor and Meier, David and Viefhaus, Jens and Sick, Bernhard and Ehresmann, Arno and Ilchen, Markus and Helml, Wolfram}, journal = {Scientific Reports}, keywords = {imported}, number = 1, pages = {17809–}, title = {Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses}, volume = 12, year = 2022 }
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Meier, D., Ramirez, L.V., Völker, J., Viefhaus, J., Sick, B., Hartmann, G.: Optimizing a superconducting radio-frequency gun using deep reinforcement learning. Physical Review Accelerators and Beams. 25, 104604 (2022). https://doi.org/10.1103/PHYSREVACCELBEAMS.25.104604/FIGURES/11/MEDIUM.
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Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.
@article{Meier2022, abstract = {Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.}, author = {Meier, David and Ramirez, Luis Vera and Völker, Jens and Viefhaus, Jens and Sick, Bernhard and Hartmann, Gregor}, journal = {Physical Review Accelerators and Beams}, keywords = {aimed}, month = 10, number = 10, pages = 104604, publisher = {American Physical Society}, title = {Optimizing a superconducting radio-frequency gun using deep reinforcement learning}, volume = 25, year = 2022 }
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Huhnstock, R., Reginka, M., Sonntag, C., Merkel, M., Dingel, K., Sick, B., Vogel, M., Ehresmann, A.: Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes. Scientific Reports. 12, 20890- (2022). https://doi.org/10.1038/s41598-022-25391-z.
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The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle–substrate interactions. Thus, the detection of a modified particle–substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a “hopping”-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.
@article{huhnstock2022threedimensional, abstract = {The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle–substrate interactions. Thus, the detection of a modified particle–substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a “hopping”-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.}, author = {Huhnstock, Rico and Reginka, Meike and Sonntag, Claudius and Merkel, Maximilian and Dingel, Kristina and Sick, Bernhard and Vogel, Michael and Ehresmann, Arno}, journal = {Scientific Reports}, keywords = {imported}, number = 1, pages = {20890–}, title = {Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes}, volume = 12, year = 2022 }
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Huhnstock, R., Reginka, M., Tomita, A., Merkel, M., Dingel, K., Holzinger, D., Sick, B., Vogel, M., Ehresmann, A.: Translatory and rotatory motion of Exchange-Bias capped Janus particles controlled by dynamic magnetic field landscapes. Scientific Reports. (2021).
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@article{huhnstock2021, author = {Huhnstock, Rico and Reginka, Meike and Tomita, Andreea and Merkel, Maximilian and Dingel, Kristina and Holzinger, Dennis and Sick, Bernhard and Vogel, Michael and Ehresmann, Arno}, journal = {Scientific Reports}, keywords = {imported}, publisher = {Nature Publishing Group}, title = {Translatory and rotatory motion of Exchange-Bias capped Janus particles controlled by dynamic magnetic field landscapes}, year = 2021 }
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Reginka, M., Hoang, H., Efendi, Özge, Merkel, M., Huhnstock, R., Holzinger, D., Dingel, K., Sick, B., Bertinetti, D., Herberg, F.W., Ehresmann, A.: Transport Efficiency of Biofunctionalized Magnetic Particles Tailored by Surfactant Concentration. Langmuir. 37, 8498–8507 (2021). https://doi.org/10.1021/acs.langmuir.1c00900.
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@article{doi:10.1021/acs.langmuir.1c00900, author = {Reginka, Meike and Hoang, Hai and Efendi, Özge and Merkel, Maximilian and Huhnstock, Rico and Holzinger, Dennis and Dingel, Kristina and Sick, Bernhard and Bertinetti, Daniela and Herberg, Friedrich W. and Ehresmann, Arno}, journal = {Langmuir}, keywords = {imported}, note = {PMID: 34231364}, number = 28, pages = {8498-8507}, title = {Transport Efficiency of Biofunctionalized Magnetic Particles Tailored by Surfactant Concentration}, volume = 37, year = 2021 }
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Dingel, K., Huhnstock, R., Knie, A., Ehresmann, A., Sick, B.: AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems. Computer Physics Communications. 262, 107859 (2021). https://doi.org/10.1016/j.cpc.2021.107859.
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Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.
@article{Dingel2021, abstract = {Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.}, author = {Dingel, Kristina and Huhnstock, Rico and Knie, André and Ehresmann, Arno and Sick, Bernhard}, journal = {Computer Physics Communications}, keywords = {imported}, month = {01}, pages = 107859, publisher = {Elsevier BV}, title = {AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems}, volume = 262, year = 2021 }
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Dingel, K., Liehr, A., Vogel, M., Degener, S., Meier, D., Niendorf, T., Ehresmann, A., Sick, B.: AI-Based On The Fly Design of Experiments in Physics and Engineering. In: Workshop on Self-Improving System Integration (2021).
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@inproceedings{Dingel2021, author = {Dingel, Kristina and Liehr, Alexander and Vogel, Michael and Degener, Sebastian and Meier, David and Niendorf, Thomas and Ehresmann, Arno and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration}, keywords = {imported}, title = {{AI-Based On The Fly Design of Experiments in Physics and Engineering}}, year = 2021 }
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Meier, D., Hartmann, G., Völker, J., Viefhaus, J., Sick, B.: Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm. In: Chubar, O. en Sawhney, K. (reds) Advances in Computational Methods for X-Ray Optics V. bll 71–77. SPIE (2020). https://doi.org/10.1117/12.2568001.
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@inproceedings{MHV20, author = {Meier, David and Hartmann, Gregor and Völker, Jens and Viefhaus, Jens and Sick, Bernhard}, booktitle = {Advances in Computational Methods for X-Ray Optics V}, editor = {Chubar, Oleg and Sawhney, Kawal}, keywords = {itegpub}, organization = {International Society for Optics and Photonics}, pages = {71 – 77}, publisher = {SPIE}, title = {{Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm}}, volume = 11493, year = 2020 }