Data-driven Azimuthal RHEED construction for in-situ crystal growth characterization
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916890888110080 |
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| author | Khaireh-Walieh, Abdourahman Arnoult, Alexandre Plissard, Sébastien Wiecha, Peter R. |
| author_facet | Khaireh-Walieh, Abdourahman Arnoult, Alexandre Plissard, Sébastien Wiecha, Peter R. |
| contents | Reflection High-Energy Electron Diffraction (RHEED) is a powerful tool to probe the surface reconstruction during MBE growth. However, raw RHEED patterns are difficult to interpret, especially when the wafer is rotating. A more accessible representation of the information is therefore the so-called Azimuthal RHEED (ARHEED), an angularly resolved plot of the electron diffraction pattern during a full wafer rotation. However, ARHEED requires precise information about the rotation angle as well as of the position of the specular spot of the electron beam. We present a Deep Learning technique to automatically construct the Azimuthal RHEED from bare RHEED images, requiring no further measurement equipment. We use two artificial neural networks: an image segmentation model to track the center of the specular spot and a regression model to determine the orientation of the crystal with respect to the incident electron beam of the RHEED system. Our technique enables accurate, and potentially real-time ARHEED construction on any growth chamber equipped with a RHEED system. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_15339 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven Azimuthal RHEED construction for in-situ crystal growth characterization Khaireh-Walieh, Abdourahman Arnoult, Alexandre Plissard, Sébastien Wiecha, Peter R. Mesoscale and Nanoscale Physics Materials Science Reflection High-Energy Electron Diffraction (RHEED) is a powerful tool to probe the surface reconstruction during MBE growth. However, raw RHEED patterns are difficult to interpret, especially when the wafer is rotating. A more accessible representation of the information is therefore the so-called Azimuthal RHEED (ARHEED), an angularly resolved plot of the electron diffraction pattern during a full wafer rotation. However, ARHEED requires precise information about the rotation angle as well as of the position of the specular spot of the electron beam. We present a Deep Learning technique to automatically construct the Azimuthal RHEED from bare RHEED images, requiring no further measurement equipment. We use two artificial neural networks: an image segmentation model to track the center of the specular spot and a regression model to determine the orientation of the crystal with respect to the incident electron beam of the RHEED system. Our technique enables accurate, and potentially real-time ARHEED construction on any growth chamber equipped with a RHEED system. |
| title | Data-driven Azimuthal RHEED construction for in-situ crystal growth characterization |
| topic | Mesoscale and Nanoscale Physics Materials Science |
| url | https://arxiv.org/abs/2503.15339 |