Deep-learning design of graphene metasurfaces for quantum control and Dirac electron holography

Autor:	Han, Chen-Di, Ye, Li-Li, Lin, Zin, Kovanis, Vassilios, Lai, Ying-Cheng
Rok vydání:	2024
Předmět:	Physics - Computational Physics Condensed Matter - Materials Science Physics - Applied Physics Physics - Optics Quantum Physics
Druh dokumentu:	Working Paper
DOI:	10.1063/5.0216271
Popis:	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. Comment: 13 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.05975 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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