Intelligent metaphotonics empowered by machine learning

被引:103
|
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
俄罗斯科学基金会; 澳大利亚研究理事会;
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
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