An ultra-wideband metasurface for RCS reduction

Shu, Yuan-Liang; Zhang, Y. H.; He, Siyuan; Zhu, Guo‐Qiang; Chen, K.; Yin, Jianing

doi:10.23919/acess.2018.8669341

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Application field: Electromagnetics 159 Li, Chen, Zeng, et al [368] 2017 Adaptive GA optimization framework 160 Zhang and Cuii [369] 2017 BPSO optimization framework 161 Ahmed, Chandra, and Al-Behadili [370] 2017 GA Inverse design 162 Han, Cao, Gao, et al [371] 2017 GA optimization framework 163 Pfeiffer and Tomasic [372] 2017 GA optimization framework 164 Pelluri and Appasani [373] 2017 GA optimization framework 165 Feng, Chen, and Huang [374] 2017 GA optimization framework 166 Allen, Dykes, Reid, et al [375] 2017 GA optimization framework 167 Ding, Zhang, Zhang, et al [376] 2017 GA optimization framework 168 Mahdi and Taha [377] 2017 GA Topology optimization 169 Su, Lu, and Li [378] 2017 PSO optimization framework 170 Orlandi [379] 2018 Differential evolution optimization framework (DE) algorithm 171 Bagmancı, Karaaslan, Altıntaş, et al [380] 2018 GA optimization framework 172 Lim, Song, Kim, et al [381] 2018 GA optimization framework 173 Corrêa, Resende, Bicalho, et al [382] 2018 GA optimization framework 174 Kumar, Behera, and Suraj [383] 2018 GA optimization framework 175 Clemens, Iskander, Yun, et al [189] 2018 Hybrid genetic programming optimization framework 176 Soltani, Soltani, and Aguili [384] 2019 GA Inverse design 177 Ibili, Karaosmanoglu, and Ergul [385] 2019 GA optimization framework 178 Seshadri and Gupta [386] 2019 GA optimization framework 179 Nanda, De, Sahu, et al [387] 2019 GA optimization framework 180 Assal, Benzerga, Sharaiha, et al [388] 2019 GA optimization framework 181 Karatzidis, Kantartzis, Pyrialakos, et al [389] 2019 GA optimization framework 182 Tian and Li [390] 2019 GA optimization framework 183 Yuan, Ma, Sui, et al [391] 2019 GA Topology optimization 184 Yanzhang and Jinghao [392] 2019 GA Topology optimization 185 Sui, Ma, Chang, et al [393] 2019 IAGA optimization framework 186 Steckiewicz and Choroszucho [394] 2019 PSO optimization framework 187 Hao, Du, and Zhang …”

Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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Machine intelligence continues to rise in popularity as an aid to the design and discovery of novel metamaterials. The properties of metamaterials are essentially controllable via their architectures and until recently, the design process has relied on a combination of trial-and-error and physics-based methods for optimization. These processes can be time-consuming and challenging, especially if the design space for metamaterial optimization is explored thoroughly. Artificial intelligence (AI) and machine learning (ML) can be used to overcome challenges like these as pre-processed massive metamaterial datasets can be used to very accurately train appropriate models. The models can be broad, describing properties, structure, and function at numerous levels of hierarchy, using relevant inputted knowledge. Here, we present a comprehensive review of the literature where state-of-the-art machine intelligence is used for the design, discovery and development of metamaterials. In this review, individual approaches are categorized based on methodology and application. We further present machine intelligence trends over a wide range of metamaterial design problems including: acoustics, photonics, plasmonics, mechanics, and more. Finally, we identify and discuss recent research directions and highlight current gaps in knowledge. KeywordsMetamaterials • Artificial intelligence • Machine learning • Neural Networks • Evolutionary algorithms • Surrogate modelling • Optimization 42 solely related to the properties of material constituents. 43 Properties can therefore be controlled and manipulated by 44 changing metamaterial 'unit cell' topology and while the 45 bulk properties of the constituent materials have influence, 46 these properties are often not the sole dominating variable 47 used in designing metamaterials. When designing with 48 respect to topology, the properties of metamaterials can 49 be customized within a very large design space, and this 50 is one of the reasons for the exponential growth in meta-51 materials R&D across the breadth of modern engineering.

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Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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“…Next, new AMCs structures to improve the bandwidth have been widely studied by the considerable researchers. Inspired by the concept of phased-array antenna design, gradient metasurfaces [12][13][14][15][16] or coding metasurfaces [17][18][19][20][21][22] can reflect the incident wave in arbitrary desirable direction, which provide a new way to control the backscattering for RCS reduction. However, the method of RCS reduction using the phase gradient metasurfaces or coding metasurfaces is limited in part by complex fabrication process, limited controlability for piecewise phase gradient, difficult for implementation of small patterning elements.…”

Section: Introductionmentioning

confidence: 99%

Design of phase matching chessboard‐like electromagnetic metasurfaces for wideband radar cross section reduction

Chen

Shen

et al. 2019

Micro & Optical Tech Letters

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In this article, a chessboard‐like electromagnetic metasurfaces which can realize the polarization compensation and cancellation of the reflection beam in microwave region is designed and realized for wideband radar cross section (RCS) reduction. The chessboard‐like electromagnetic metasurfaces are composed of a square annular artificial magnetic conductor (AMC) combination with the Jerusalem cross AMC. Simulation and experimental results demonstrate that the chessboard‐like electromagnetic metasurfaces exhibit significantly RCS reduction over −10 dB in the frequency bandwidths 6.16‐18 GHz, and two RCS reduction peaks at 7.0 and 12.0 GHz in the considering frequency ranges for the normal incidence of an electromagnetic wave. The physical mechanisms of the RCS reduction peaks can be elucidated by the phase matching technology similar to the impedance matching technology applied to radar absorbing materials (RAMs). The basic physical property implies that RCS reduction based on the composite AMC metasurfaces can be improved greatly by introducing as many phase matching points as possible in the functional frequency bands. Considered that the phase matching is a necessary condition rather than a sufficient condition for RCS reduction of the chessboard‐like AMCs structures, other physical mechanisms of RCS reduction are also discussed by far field distribution, near field distribution and the properties of mono‐static RCS reduction.

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3-bit Anisotropic Metasurfaces with a Rotation of Meta-particles for Wideband RCS Reduction

Semenikhina

2021

2021 IEEE 19th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM)

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An ultra-wideband metasurface for RCS reduction

Cited by 6 publications

References 4 publications

Machine Intelligence in Metamaterials Design

Machine Intelligence in Metamaterials Design

Design of phase matching chessboard‐like electromagnetic metasurfaces for wideband radar cross section reduction

3-bit Anisotropic Metasurfaces with a Rotation of Meta-particles for Wideband RCS Reduction

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