Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

Nature Photonics volume 14, pages 383–390 (2020)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. Pendry, J. B., Schurig, D. & Smith, D. R. Controlling electromagnetic fields. Science 312, 1780–1782 (2006).

    Article  ADS  MathSciNet  MATH  Google Scholar 

  2. Engheta, N. & Ziolkowski, R. W. Metamaterials: Physics and Engineering Explorations (Wiley, 2006).

  3. Schurig, D. et al. Metamaterial electromagnetic cloak at microwave frequencies. Science 314, 977–980 (2006).

    Article  ADS  MathSciNet  Google Scholar 

  4. Cai, W., Chettiar, U. K., Kildishev, A. V. & Shalaev, V. M. Optical cloaking with metamaterials. Nat. Photon. 1, 224–227 (2007).

    Article  ADS  Google Scholar 

  5. Landy, N. & Smith, D. R. A full-parameter unidirectional metamaterial cloak for microwaves. Nat. Mater. 12, 25–28 (2013).

    Article  ADS  Google Scholar 

  6. Liu, R. et al. Broadband ground-plane cloak. Science 323, 366–369 (2009).

    Article  ADS  Google Scholar 

  7. Alù, A. & Engheta, N. Achieving transparency with plasmonic and metamaterial coatings. Phys. Rev. E 72, 016623 (2005).

    Article  ADS  Google Scholar 

  8. Edwards, B., Alù, A., Silveirinha, M. G. & Engheta, N. Experimental verification of plasmonic cloaking at microwave frequencies with metamaterials. Phys. Rev. Lett. 103, 153901 (2009).

    Article  ADS  Google Scholar 

  9. Yu, N. et al. Light propagation with phase discontinuities: generalized laws of reflection and refraction. Science 334, 333–337 (2011).

    Article  ADS  Google Scholar 

  10. Ni, X., Wong, Z. J., Mrejen, M., Wang, Y. & Zhang, X. An ultrathin invisibility skin cloak for visible light. Science 349, 1310–1314 (2015).

    Article  ADS  Google Scholar 

  11. Yang, Y. et al. Full-polarization 3D metasurface cloak with preserved amplitude and phase. Adv. Mater. 28, 6866–6871 (2016).

    Article  Google Scholar 

  12. Zigoneanu, L., Popa, B. I. & Cummer, S. A. Three-dimensional broadband omnidirectional acoustic ground cloak. Nat. Mater. 13, 352–355 (2014).

    Article  ADS  Google Scholar 

  13. Farhat, M., Guenneau, S. & Enoch, S. Ultrabroadband elastic cloaking in thin plates. Phys. Rev. Lett. 103, 024301 (2009).

    Article  ADS  Google Scholar 

  14. Han, T. et al. Experimental demonstration of a bilayer thermal cloak. Phys. Rev. Lett. 112, 054302 (2014).

    Article  ADS  Google Scholar 

  15. Ma, W., Cheng, F. & Liu, Y. Deep-learning enabled on-demand design of chiral metamaterials. ACS Nano 12, 6326–6334 (2018).

    Article  Google Scholar 

  16. Malkiel, I. et al. Plasmonic nanostructure design and characterization via deep learning. Light Sci. Appl. 7, 60 (2018).

    Article  ADS  Google Scholar 

  17. Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73–76 (2016).

    Article  ADS  Google Scholar 

  18. Peurifoy, J. et al. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv. 4, eaar4206 (2018).

    Article  ADS  Google Scholar 

  19. Li, H. et al. Dual-band Fresnel zone plate antenna with independently steerable beams. IEEE Trans. Antennas Propag. 66, 2113–2118 (2018).

    Article  ADS  Google Scholar 

  20. Cui, T. J., Qi, M. Q., Wan, X., Zhao, J. & Cheng, Q. Coding metamaterials, digital metamaterials and programmable metamaterials. Light Sci. Appl. 3, e218 (2014).

    Article  ADS  Google Scholar 

  21. Xu, C., Stiubianu, G. T. & Gorodetsky, A. A. Adaptive infrared-reflecting systems inspired by cephalopods. Science 359, 1495–1500 (2018).

    Article  ADS  Google Scholar 

  22. Taflove, A. & Hagness, S. C. Computational Electrodynamics: The Finite-Difference Time-Domain Method (Artech House, 2000).

  23. Qian, C. et al. Transient response of a signal through a dispersive invisibility cloak. Opt. Lett. 41, 4911–4914 (2016).

    Article  ADS  Google Scholar 

  24. Zheludev, N. I. & Kivshar, Y. S. From metamaterials to metadevices. Nat. Mater. 11, 917–924 (2012).

    Article  ADS  Google Scholar 

  25. Shaltout, A. M., Shalaev, V. M. & Brongersma, M. L. Spatiotemporal light control with active metasurfaces. Science 364, eaat3100 (2019).

    Article  ADS  Google Scholar 

  26. Kwon, H., Sounas, D., Cordaro, A., Polman, A. & Alu, A. Nonlocal metasurfaces for optical signal processing. Phys. Rev. Lett. 121, 173004 (2018).

    Article  ADS  Google Scholar 

  27. Schmidhuber, J. Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015).

    Article  Google Scholar 

  28. Hinton, G. et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29, 82–97 (2012).

    Article  ADS  Google Scholar 

  29. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A. & Kim, C. Machine learning in materials informatics: recent applications and prospects. npj Comput. Mater. 3, 54 (2017).

    Article  ADS  Google Scholar 

  30. Carleo, G. & Troyer, M. Solving the quantum many-body problem with artificial neural networks. Science 355, 602–606 (2017).

    Article  ADS  MathSciNet  MATH  Google Scholar 

  31. Ye, D., Chang, K., Ran, L. & Xin, H. Microwave gain medium with negative refractive index. Nat. Commun. 5, 5841 (2014).

    Article  ADS  Google Scholar 

  32. Constantine, A. B. Antenna Theory: Analysis and Design (Wiley, 2005).

  33. Zhang, Y. & Ng, B. P. MUSIC-like DOA estimation without estimating the number of sources. IEEE Trans. Signal Process. 58, 1668–1676 (2010).

    Article  ADS  MathSciNet  MATH  Google Scholar 

  34. Qian, C. et al. Experimental observation of superscattering. Phys. Rev. Lett. 122, 063901 (2019).

    Article  ADS  Google Scholar 

  35. Selvanayagam, M. & Eleftheriades, G. V. Experimental demonstration of active electromagnetic cloaking. Phys. Rev. X 3, 041011 (2013).

    Google Scholar 

  36. Park, J., Kang, J., Kim, S., Liu, X. & Brongersma, M. Dynamic reflection phase and polarization control in metasurfaces. Nano Lett. 17, 407–413 (2017).

    Article  ADS  Google Scholar 

  37. Wang, Q. et al. Optically reconfigurable metasurfaces and photonic devices based on phase change materials. Nat. Photon. 10, 60–65 (2016).

    Article  ADS  Google Scholar 

  38. Tian, Z. et al. Reconfigurable vanadium dioxide nanomembranes and microtubes with controllable phase transition temperatures. Nano Lett. 18, 3017–3023 (2018).

    Article  ADS  Google Scholar 

  39. Ma, X. et al. An active metamaterial for polarization manipulating. Adv. Opt. Mater. 2, 945–949 (2014).

    Article  Google Scholar 

  40. Sarabandi, K. & Behdad, N. A frequency selective surface with miniaturized elements. IEEE Trans. Antennas Propag. 55, 1239–1245 (2007).

    Article  ADS  Google Scholar 

  41. Widrow, B., Mantey, P. E., Griffiths, L. J. & Goode, B. B. Adaptive antenna systems. Proc. IEEE 55, 2143–2159 (1967).

    Article  Google Scholar 

  42. Aeschlimann, M. et al. Adaptive subwavelength control of nano-optical fields. Nature 446, 301–304 (2007).

    Article  ADS  Google Scholar 

  43. Colton, D. & Kress, R. Inverse Acoustic and Electromagnetic Scattering Theory (Springer, 1997).

  44. Rodriguez-Nieva, J. F. & Scheurer, M. S. Identifying topological order through unsupervised machine learning. Nat. Phys. 15, 790–795 (2019).

    Article  Google Scholar 

  45. Hughes, T. W., Minkov, M., Shi, Y. & Fan, S. Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica 5, 864–871 (2018).

    Article  ADS  Google Scholar 

  46. Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018).

    Article  ADS  MathSciNet  MATH  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. State Key Laboratory of Modern Optical Instrumentation, Interdisciplinary Center for Quantum Information, College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

    Chao Qian, Bin Zheng, Erping Li, Lian Shen & Hongsheng Chen

  2. ZJU-Hangzhou Global Science and Technology Innovation Center, Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, ZJU-UIUC Institute, Zhejiang University, Hangzhou, China

    Chao Qian, Bin Zheng, Erping Li, Lian Shen & Hongsheng Chen

  3. Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yichen Shen & Li Jing

  4. Lightelligence Inc., Boston, MA, USA

    Yichen Shen

Authors
  1. Chao Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yichen Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Erping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lian Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hongsheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to Hongsheng Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary methods, discussions and figures.

Supplementary Video

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41566-020-0604-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing