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Deep learning enabled topological design of exceptional points for multi-optical-parameter control

文献类型：期刊论文

作者Fu, Peng5,6; Du, Shuo5,6; Lan, Wenze5,6; Hu, Leyong5,6; Wu, Yiqing1; Li, Zhenfei3; Huang, Xin5,6; Guo, Yang5,6; Zhu, Weiren3; Li, Junjie2,5,6
刊名COMMUNICATIONS PHYSICS
出版日期2023-09-16
卷号6期号:1页码:9
ISSN号2399-3650
DOI10.1038/s42005-023-01380-0
英文摘要Metasurfaces are 2D artificial nanostructures that exhibit fascinating optical phenomena and flexible capabilities. Multi-optical-parameter metasurfaces have advantages over single-function or single-dimensional metasurfaces, especially in practical applications like holography, sub-diffraction imaging, and vectorial fields. However, achieving multi-optical-parameter control is challenging due to a lack of design strategy, limited manipulation channels, and signal-to-noise ratio problems. Exceptional points (EPs) possess inherent polarization decoupling properties and allow for amplitude and wavelength modulation, opening up research prospects for multi-optical-parameter electromagnetic field modulation and developing compact integrated devices. Leveraging deep learning, we observe topological charge conservation and utilize the topologically protected optical parameter distribution around scattered EPs. Based on these, we introduce amplitude-phase multiplexing and wavelength division multiplexing devices. Our work allows rapid and precise discovery of EPs topology, offers a powerful tool for digging related physics, and provides a paradigm for multi-optical parametric manipulation with high performance and less crosstalk, which is critical for imaging, encryption, and information storage applications. Exceptional points (EPs) with inherent polarization decoupling, amplitude and wavelength modulation properties, provide a tool for multi-optical-parameter modulation. Leveraging deep learning, the authors observe topological charge conservation and utilize the topologically protected optical parameter distribution around EPs to introduce two kinds of multifunctional optical devices.
资助项目National Natural Science Foundation of China[61888102] ; National Natural Science Foundation of China[11974386] ; National Natural Science Foundation of China[61905274] ; National Natural Science Foundation of China[12074420] ; National Natural Science Foundation of China[U21A20140] ; National Natural Science Foundation of China[62071291] ; National Natural Science Foundation of China[92265110] ; National Natural Science Foundation of China[62174179] ; National Natural Science Foundation of China[62204259] ; National Key Research Program of China[2021YFA1400700] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB33000000] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB28000000] ; Strategic Priority Research Program of the Chinese Academy of Sciences[QYZDJ-SSW-SLH042] ; Key Research Program of Frontier Sciences of CAS[QYZDJSSWSLH042] ; Key Research Program of Frontier Sciences of CAS[XDPB22] ; Beijing Municipal Science & Technology Commission, Administrative Commission of Zhongguancun Science Park[Z211100004821009] ; Project for Young Scientists in Basic Research of CAS[YSBR021]
WOS研究方向Physics
语种英语
出版者NATURE PORTFOLIO
WOS记录号WOS:001066469500002
源URL[http://119.78.100.204/handle/2XEOYT63/21167]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Du, Shuo; Liu, Baoli; Gu, Changzhi
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc Chinese Acad Sci, Beijing, Peoples R China
2.Songshan Lake Mat Lab, Dongguan 523808, Guangdong, Peoples R China
3.Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai 200240, Peoples R China
4.Univ Chinese Acad Sci, CAS Ctr Excellence Topol Quantum Computat, CAS Key Lab Vacuum Phys, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Phys, Beijing Natl Lab Condensed Matter Phys, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Sch Phys Sci, CAS Key Lab Vacuum Phys, Key Lab Vacuum Phys, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Fu, Peng,Du, Shuo,Lan, Wenze,et al. Deep learning enabled topological design of exceptional points for multi-optical-parameter control[J]. COMMUNICATIONS PHYSICS,2023,6(1):9.
APA Fu, Peng.,Du, Shuo.,Lan, Wenze.,Hu, Leyong.,Wu, Yiqing.,...&Gu, Changzhi.(2023).Deep learning enabled topological design of exceptional points for multi-optical-parameter control.COMMUNICATIONS PHYSICS,6(1),9.
MLA Fu, Peng,et al."Deep learning enabled topological design of exceptional points for multi-optical-parameter control".COMMUNICATIONS PHYSICS 6.1(2023):9.

入库方式： OAI收割

来源：计算技术研究所

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