Deep-learning design of graphene metasurfaces for quantum control and Dirac electron holography
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914917405163520 |
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| author | Han, Chen-Di Ye, Li-Li Lin, Zin Kovanis, Vassilios Lai, Ying-Cheng |
| author_facet | Han, Chen-Di Ye, Li-Li Lin, Zin Kovanis, Vassilios Lai, Ying-Cheng |
| contents | Metasurfaces are sub-wavelength patterned layers for controlling waves in physical systems. In optics, meta-surfaces are created by materials with different dielectric constants and are capable of unconventional functionalities. We develop a deep-learning framework for Dirac-material metasurface design for controlling electronic waves. The metasurface is a configuration of circular graphene quantum dots, each created by an electric potential. Employing deep convolutional neural networks, we show that the original scattering wave can be reconstructed with fidelity over 95$\%$, suggesting the feasibility of Dirac electron holography. Additional applications such as plane wave generation, designing broadband, and multi-functionality graphene metasurface systems are illustrated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05975 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep-learning design of graphene metasurfaces for quantum control and Dirac electron holography Han, Chen-Di Ye, Li-Li Lin, Zin Kovanis, Vassilios Lai, Ying-Cheng Computational Physics Materials Science Applied Physics Optics Quantum Physics Metasurfaces are sub-wavelength patterned layers for controlling waves in physical systems. In optics, meta-surfaces are created by materials with different dielectric constants and are capable of unconventional functionalities. We develop a deep-learning framework for Dirac-material metasurface design for controlling electronic waves. The metasurface is a configuration of circular graphene quantum dots, each created by an electric potential. Employing deep convolutional neural networks, we show that the original scattering wave can be reconstructed with fidelity over 95$\%$, suggesting the feasibility of Dirac electron holography. Additional applications such as plane wave generation, designing broadband, and multi-functionality graphene metasurface systems are illustrated. |
| title | Deep-learning design of graphene metasurfaces for quantum control and Dirac electron holography |
| topic | Computational Physics Materials Science Applied Physics Optics Quantum Physics |
| url | https://arxiv.org/abs/2405.05975 |