Intelligent metaphotonics empowered by machine learning

被引:134
作者
Krasikov, Sergey [1 ,2 ]
Tranter, Aaron [3 ]
Bogdanov, Andrey [2 ]
Kivshar, Yuri [1 ]
机构
[1] Australian Natl Univ, Res Sch Phys, Nonlinear Phys Ctr, Canberra, ACT 2601, Australia
[2] ITMO Univ, Sch Phys & Engn, St Petersburg 197101, Russia
[3] Australian Natl Univ, Res Sch Phys, Dept Quantum Sci, Ctr Quantum Computat & Commun Technol, Canberra, ACT 2601, Australia
基金
澳大利亚研究理事会; 俄罗斯科学基金会;
关键词
metaphotonics; machine learning; artificial intelligence; DEEP NEURAL-NETWORK; INVERSE-DESIGN; BROAD-BAND; ARTIFICIAL-INTELLIGENCE; EYE DISPLAY; SCATTERING; METASURFACES; METAMATERIALS; OPTIMIZATION; EFFICIENT;
D O I
10.29026/oea.2022.210147
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
In the recent years, a dramatic boost of the research is observed at the junction of photonics, machine learning and artificial intelligence. A new methodology can be applied to the description of a variety of photonic systems including optical waveguides, nanoantennas, and metasurfaces. These novel approaches underpin the fundamental principles of lightmatter interaction developed for a smart design of intelligent photonic devices. Artificial intelligence and machine learning penetrate rapidly into the fundamental physics of light, and they provide effective tools for the study of the field of metaphotonics driven by optically induced electric and magnetic resonances. Here we overview the evaluation of metaphotonics induced by artificial intelligence and present a summary of the concepts of machine learning with some specific examples developed and demonstrated for metasystems and metasurfaces.
引用
收藏
页数:24
相关论文
共 226 条
[1]   Programmable Metamaterials for Software-Defined Electromagnetic Control: Circuits, Systems, and Architectures [J].
Abadal, Sergi ;
Cui, Tie-Jun ;
Low, Tony ;
Georgiou, Julius .
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, 2020, 10 (01) :6-19
[2]  
Abdollahramezani S., 2021, ARXIV PREPRINT ARXIV
[3]   Supervised-Learning-Based Development of Multibit RCS-Reduced Coding Metasurfaces [J].
Abdullah, Muhammad ;
Koziel, Slawomir .
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2022, 70 (01) :264-274
[4]   Solar "Meta-Sails" for Agile Optical Force Control [J].
Achouri, Karim ;
Cespedes, Oscar V. ;
Caloz, Christophe .
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2019, 67 (11) :6924-6934
[5]  
Alexandropoulos G. C., 2021, arXiv
[6]   Multifunctional Metasurface Design with a Generative Adversarial Network [J].
An, Sensong ;
Zheng, Bowen ;
Tang, Hong ;
Shalaginov, Mikhail Y. ;
Zhou, Li ;
Li, Hang ;
Kang, Myungkoo ;
Richardson, Kathleen A. ;
Gu, Tian ;
Hu, Juejun ;
Fowler, Clayton ;
Zhang, Hualiang .
ADVANCED OPTICAL MATERIALS, 2021, 9 (05)
[7]   Broadband achromatic metalens design based on deep neural networks [J].
An, Xipeng ;
Cao, Yue ;
Wei, Yunxuan ;
Zhou, Zhihao ;
Hu, Tie ;
Feng, Xing ;
He, Guangqiang ;
Zhao, Ming ;
Yang, Zhenyu .
OPTICS LETTERS, 2021, 46 (16) :3881-3884
[8]  
[Anonymous], 2018, ARXIV171009099
[9]  
[Anonymous], 2015, P 3 INT C LEARN REPR
[10]   Multitask deep-learning-based design of chiral plasmonic metamaterials [J].
Ashalley, Eric ;
Acheampong, Kingsley ;
Besteiro, Lucas, V ;
Yu, Peng ;
Neogi, Arup ;
Govorov, Alexander O. ;
Wang, Zhiming .
PHOTONICS RESEARCH, 2020, 8 (07) :1213-1225