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Deep-learning-enabled self-adaptive microwave cloak without human intervention

Abstract

Becoming invisible at will has fascinated humanity for centuries and in the past decade it has attracted a great deal of attention owing to the advent of metamaterials. However, state-of-the-art invisibility cloaks typically work in a deterministic system or in conjunction with outside help to achieve active cloaking. Here, we propose the concept of an intelligent (that is, self-adaptive) cloak driven by deep learning and present a metasurface cloak as an example implementation. In the experiment, the metasurface cloak exhibits a millisecond response time to an ever-changing incident wave and the surrounding environment, without any human intervention. Our work brings the available cloaking strategies closer to a wide range of real-time, in situ applications, such as moving stealth vehicles. The approach opens the way to facilitating other intelligent metadevices in the microwave regime and across the wider electromagnetic spectrum and, more generally, enables automatic solutions of electromagnetic inverse design problems.

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Fig. 1: Schematic of a deep-learning-enabled self-adaptive metasurface cloak.
Fig. 2: Transient response of the self-adaptive cloak in FDTD simulations.
Fig. 3: Experimental set-up and ANN training results.
Fig. 4: Demonstration of the self-adaptive cloak response to random backgrounds for normal wave incidence at 8.4 GHz.
Fig. 5: Demonstration of the self-adaptive cloak response to random and simultaneous changes in the incident wave and background.
Fig. 6: Reflection spectrum of the metasurface obtained by different methods.

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Data availability

The data that support the plots within this paper and other findings of the study are available from the corresponding author upon reasonable request.

Code availability

The custom codes used in this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank P. Rebusco and I. Kaminer for critical reading and editing of the manuscript, L.W. Tian for assistance with experimental construction, and J. T. Huangfu, D. S. Liao and Y. Z. Ding for discussions. This work was sponsored by the National Natural Science Foundation of China under grants 61625502, 11961141010, 61574127 and 61975176, the Top-Notch Young Talents Program of China and the Innovation Joint Research Center for Cyber-Physical-Society System. B.Z. was supported by the National Natural Science Foundation of China under grant 61601408. L.S. was supported by the National Natural Science Foundation of China under grant 61905216. C.Q. was supported by the Chinese Scholarship Council (CSC number 201906320294) and a Zhejiang University Academic Award for Outstanding Doctoral Candidates.

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Contributions

C.Q. and H.C. conceived the idea. C.Q. performed the numerical simulations and the experiment. C.Q. and H.C. wrote the manuscript. B.Z., Y.S., L.J., E.L. and L.S. discussed the results and commented on the manuscript. H.C. supervised the project.

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Correspondence to Hongsheng Chen.

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Supplementary information

Supplementary Information

Supplementary methods, discussions and figures.

Supplementary Video

The performance when a cloaked vehicle passes through a random environment.

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Qian, C., Zheng, B., Shen, Y. et al. Deep-learning-enabled self-adaptive microwave cloak without human intervention. Nat. Photonics 14, 383–390 (2020). https://doi.org/10.1038/s41566-020-0604-2

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