Inverse engineering of electromagnetically induced transparency in terahertz metamaterial via deep learning

Huang, Wei; Wei, Ziming; Tan, Benying; Yin, Shengyan; Zhang, Wentao

doi:10.1088/1361-6463/abd4a6

Cited by 31 publications

(15 citation statements)

References 29 publications

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“…Scattering properties are a subject of many design procedures [56,[74][75][76][77][78], including those devoted to the development of anisotropic [79] and bianisotropic [80] metasurfaces, as well as switchable reflectors [59]. DL was also exploited for achieving electromagnetically-induced transparency [81][82][83]. Chiral metasurfaces are also among the typical applications of the DL design procedures [60,[84][85][86][87][88].…”

Section: Transformative Metasurfacesmentioning

confidence: 99%

Intelligent metaphotonics empowered by machine learning

Krasikov,

Tranter,

Bogdanov

et al. 2021

Preprint

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In the recent years, we observe a dramatic boost of research in photonics empowered by the concepts of machine learning and artificial intelligence. The corresponding photonic systems, to which this new methodology is applied, can range from traditional optical waveguides to nanoantennas and metasurfaces, and these novel approaches underpin the fundamental principles of light-matter interaction developed for a smart design of intelligent photonic devices. Concepts and approaches of 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 opticallyinduced electric and magnetic resonances. Here, we introduce this new field with its application to metaphotonics and also present a summary of the basic concepts of machine learning with some specific examples developed and demonstrated for metasystems and metasurfaces.

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Section: Transformative Metasurfacesmentioning

confidence: 99%

Intelligent metaphotonics empowered by machine learning

Krasikov,

Tranter,

Bogdanov

et al. 2021

Preprint

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“…In recent years, deep learning has been introduced to many physical systems by many scholars, such as plasmonic nanostructure design [14], [15], [16], digital coding metasurfaces [17], [18], intelligent metasurfaces [19] and other systems [20], [21], [22], [23], [24], [25]. Deep learning algorithms can easily learn the correlation between the structural parameters and electromagnetic response of material replacing the traditional time-consuming electromagnetic simulation and avoiding the complicated Maxwell solution process [26].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for the Design of Toroidal Metasurfaces

et al. 2023

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In recent years, the toroidal dipoles have had a profound impact on several fields including electromagnetism. However, the on-demand design of toroidal metasurfaces is still a very time-consuming process. In this paper, a method of neural network simulating the nonlinear relationship between the structural parameters of metasurfaces and its multipole scattered powers is proposed based on a deep learning algorithm. The forward network can quickly predict the scattered powers from input structural parameters, which can achieve an accuracy comparable to the electromagnetic simulations. In addition, with the required scattering spectrum as input, the appropriate parameters of the structure could be automatically calculated and then output by the inverse network which can achieve a low mean square error of 0.074 in training set and 0.18 in the test set. Compared with the conventional design process, the proposed deep learning model can guide the design of the toroidal dipole metasurface faster and pave the way for the rapid development of toroidal metasurfaces.

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“…Bang et al used a five‐layers DNN to train the electric and magnetic field to realize the prediction of power density according to the phases of 4 × 1 array antenna 30 . Although a deep learning algorithm has been used to design the metasurface antenna, the tunable metasurface antenna working in the THz frequency is still limited and needs to be considered as it has potential applications in terahertz communications 31,32 . For the network, the training of DNN is more stable and little shows model collapse compared with that of generative adversarial networks (GAN).…”

Section: Introductionmentioning

confidence: 99%

The reverse design of a tunable terahertz metasurface antenna based on a deep neural network

Dao

et al. 2022

Micro & Optical Tech Letters

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The reverse design of a tunable terahertz metasurface antenna is proposed based on the deep neural network (DNN). To obtain the tunable properties of the terahertz antenna, the phase‐changed material vanadium dioxide (VO2) is introduced by controlling the voltage without changing the structures of the metasurface antenna. To improve the efficiency and accuracy of the terahertz antenna, the DNN is used to establish the relationship of amplitude and phase for the antenna unit. The prediction errors are below 10%. The radiation direction of the antenna can be changed from 0° to 52° by the VO2 with different states. The prediction error of angle is only 1° difference from the theoretical calculation. This reverse design method can be extended to other metasurface devices with improved efficiency and accuracy.

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Inverse engineering of electromagnetically induced transparency in terahertz metamaterial via deep learning

Cited by 31 publications

References 29 publications

Intelligent metaphotonics empowered by machine learning

Intelligent metaphotonics empowered by machine learning

Deep Learning for the Design of Toroidal Metasurfaces

The reverse design of a tunable terahertz metasurface antenna based on a deep neural network

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