Machine Learning Approach for On-Demand Rapid Constructing Metasurface

Sun, Zhichao; Xu, Bo; Fan, Jin; Zhou, Gongxue; Lü, Lin

doi:10.1109/jstqe.2021.3083565

Cited by 17 publications

(10 citation statements)

References 43 publications

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“…Metamaterials are a new class of artificial synthetic materials composed of periodic subwavelength metals or dielectrics arranged in accordance with certain rules. [5][6][7][8][9] Due to their extraordinary electromagnetic response characteristics, they can produce a resonance effect with electromagnetic waves in a specific frequency band. Therefore, metamaterials have been widely used in stealth devices, 10 sensors, 11 communication devices, 12,13 and imaging devices 14 in recent years.…”

Section: Introductionmentioning

confidence: 99%

A high-performance terahertz absorber based on synthetic-patterned vanadium dioxide metamaterials

Xue

Chen

Wang

et al. 2023

Phys. Chem. Chem. Phys.

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Section: Introductionmentioning

confidence: 99%

A high-performance terahertz absorber based on synthetic-patterned vanadium dioxide metamaterials

Xue

Chen

Wang

et al. 2023

Phys. Chem. Chem. Phys.

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“…Metasurface based designs have shown the potential for shaping the optical wavefronts by featuring only compact footprint in contrast to the bulky geometric optics. The designs become capable of providing the control by engineering the geometry and size of meta-atom [1][2][3] . The design of meta-atoms is of key importance because it is a fundamental building blocks in the design of metasurfaces and has a significant role in achieving the desired optical characteristics.…”

Section: Introductionmentioning

confidence: 99%

Application of machine-learning techniques for characteristic analysis of refractory materials

Ijaz

Noureen

Rehman

et al. 2022

Photonics for Energy II

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Flat optics have become capable of achieving unprecedented functionalities through electromagnetic (EM) wave manipulation by employing the metasurfaces. The most crucial part in the design of metasurface is the selection the constitutive component i.e. the meta-atom's material and structure so that it exhibits the precise operation as per the desired application. The unit-cell design calls for an iterative loop of simulations in order to explore the EM responses for intended operation. In this work, we have studied the absorption response of refractory materials under visible light radiations for their utilization in energy harvesting applications. The absorption response estimation using machine-learning techniques for the materials having very high melting-points, mechanical stabilities and inertness to the atmosphere has been carried out to investigate their performance in the broadband range. The presented regression models incorporate hybrid data format i.e. they simultaneously contend with 3-D and 1-D properties of various shapes of nano-resonators. The images' feature extraction is carried out by employing Singular Value Decomposition. The trained models are potent enough to bypass the repetitive sequence of optimization involved in conventional EM solvers. Additionally, the models are capable of predicting the optimum shape along with structural dimensions of unit-cell. For forward model, the MSEs for training and testing are 1.302×10 -2 and 3.269×10 -2 while R 2 scores are 0.9804 and 0.8764, respectively. The approach applied is so robust that, irrespective of complexity of unit-cell structure is, it serves the purpose of predicting the distinct structure with highest performance while bypassing the time-and computationally-intensive EM simulations.

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“…In [34], [35] a learning model has been developed for the correlation between the unit cell structure and its phase response. In the same manner, PS-KNN provides a model for the desired MS achievement [36]. However, the concentration of this work is only on designing the reflective MS.…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that there is no copper surrounding the center of each unit cell. As a result, Applying this symmetric manner reduces the number of network output by 2 36 , which is more efficient for our deep learning model.…”

Section: Introductionmentioning

confidence: 99%

Multifunctional Automatic Metasurface Design Using Deep Learning Approach

Mahdi¹,

Ghorbani²,

Beyraghi³

et al. 2022

Preprint

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<p>This paper presents a new efficient and robust method for the automatic design of Metasurface (MS) by using a Deep Neural Network (DNN), which has less complexity and design time compared to conventional MS design methods. The main contribution of the present method is its ability the design reflective/transmissive MS for wave manipulation in a wide range of frequencies (12 GHz - 18 GHz). To this end, several multi-bit encoded MSs are provided that are of potential interest in single/multi-beam, Orbital Angular Momentum (OAM), and Phase Gradient Metasurface (PGM) antenna applications. In each case, the proposed DNN provides the required unit cell in two transmission and reflection modes from 12 GHz to 18 GHz for different targets. In order to demonstrate the performance of the proposed DNN method, the following steps are implemented: (i) first, the architecture of the DNN method and the creation of pixelated unit cells by DNN are discussed. This step presents 3-bit coded unit cells to design Intelligent Reflective Surfaces (IRS) in wireless communication systems. (ii) several single/multibeam reflective OAM and transmissive PGM high gain antenna structures are presented. (iii) In order to validate the proposed design concept, a Transmissive PGM lens antenna prototype with 22.6 dBi gain and Aperture Efficiency (AE) of 40% is experimentally tested at 14 GHz. Simulation and measurement results are in good agreement, which confirms the accuracy of the proposed method. The proposed DNN method can be considered a new reliable approach for designing reflective/transmissive MSs in applications of antenna and microwave with their highly demanding market.</p>

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Machine Learning Approach for On-Demand Rapid Constructing Metasurface

Cited by 17 publications

References 43 publications

A high-performance terahertz absorber based on synthetic-patterned vanadium dioxide metamaterials

A high-performance terahertz absorber based on synthetic-patterned vanadium dioxide metamaterials

Application of machine-learning techniques for characteristic analysis of refractory materials

Multifunctional Automatic Metasurface Design Using Deep Learning Approach

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