| Autor: |
Jianghan Bao, Weihan Li, Siqi Huang, Wen Ming Yu, Che Liu, Tie Jun Cui |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
iScience, Vol 27, Iss 9, Pp 110595- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2589-0042 |
| DOI: |
10.1016/j.isci.2024.110595 |
| Popis: |
Summary: Programmable metasurfaces have garnered significant attention for their capacity to dynamically manipulate electromagnetic (EM) waves. In particular, the programmable metasurfaces offer to generate a wide range of EM beams when the appropriate digital coding patterns are designed. Traditionally, optimizing the coding patterns involves time-consuming nonlinear optimization algorithms due to the high computational complexity. In this study, we propose a physics-assisted deep learning (DL) model that can calculate the coding pattern in milliseconds, requiring only a simple depiction of the desired beam. An extended version of the macroscopic model for digital coding metasurface is introduced as the physics-driven component, which can compute the radiation pattern rapidly based on the provided coding pattern. The integration of the macroscopic model ensures to generate the physics-compliant coding designs. We validate the proposed method experimentally by measuring several coding patterns for both single-beam and dual-beam scenarios, which demonstrate good performance of beamforming. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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