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Programmable surface plasmonic neural networks for microwave detection and processing

Nature Electronics volume 6, pages 319–328 (2023)Cite this article

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Abstract

A range of alternative approaches to traditional digital hardware have been explored for the implementation of artificial neural networks, including optical neural networks and diffractive deep neural networks. Spoof surface plasmon polariton waveguides, which operate at microwave and terahertz frequencies, can offer low crosstalk, low radiation loss and easy integration, and are of potential use in the development of an alternative technology for artificial neural networks. Here, we report a programmable surface plasmonic neural network that is based on a spoof surface plasmon polariton platform and can detect and process microwaves. The approach uses a parallel coupled spoof surface plasmon polariton cell integrated with varactors. The weight coefficients of the cell can be adjusted by tuning the voltages of the varactors, and the activation function of the neural network can be programmed by detecting the input intensity and feeding back the threshold to an amplifier. We show that a two-layer fully connected surface plasmonic neural network consisting of four input cells and four output cells can perform a vector classification task. The surface plasmonic neural network can also be used to create a wireless communication system to decode and recover images. In addition, we show that partially connected surface plasmonic neural networks can classify handwritten digits with high accuracy.

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Fig. 1: Programmable SSPP cell, supercell and partially connected SPNN.
Fig. 2: Simulated SSPP cell dispersion and transmission parameters.
Fig. 3: Experimental results for the SSPP cell.
Fig. 4: Training performance of the SSPP supercell.
Fig. 5: The decoding mechanism in a wireless communication system using the SSPP supercell.
Fig. 6: Training performance for a partially connected SPNN with the ReLU activation function.

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Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

The codes that support the theoretical modelling of the spoof plasmonic neural network are available from the corresponding authors upon reasonable request.

References

  1. Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441–446 (2017).

    Article  Google Scholar 

  2. Krogh, A. What are artificial neural networks? Nat. Biotechnol. 26, 195–197 (2008).

    Article  Google Scholar 

  3. Huang, H. M., Wang, Z., Wang, T., Xiao, Y. & Guo, X. Artificial neural networks based on memristive devices: from device to system. Adv. Intell. Syst. 2, 2000149 (2020).

    Article  Google Scholar 

  4. Strukov, D. B. Smart connections. Nature 476, 403–405 (2011).

    Article  Google Scholar 

  5. Tompson, J., Jain, A., LeCun, Y. & Bregler, C. Joint training of a convolutional network and a graphical model for human pose estimation. Adv. Neural Inf. Process. Syst. 2, 1799–1807 (2014).

  6. Graves, A., Mohamed, A. & Hinton, G. Speech recognition with deep recurrent neural networks. In Proc. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing 6646–6649 (IEEE, 2013).

  7. Young, T., Hazarika, D., Poria, S. & Cambria, E. Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13, 55–75 (2018).

    Article  Google Scholar 

  8. Xie, J., Xu, L. & Chen, E. Image denoising and inpainting with deep neural networks. Adv. Neural Inf. Process. Syst. 1, 341–349 (2012).

    Google Scholar 

  9. Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proc. 34th International Conference on Machine Learning, 70 (eds Precup, D. & Teh, Y. W.), 1263–1272 (PMLR, 2017).

  10. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A. & Mougiakakou, S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35, 1207–1216 (2016).

    Article  Google Scholar 

  11. Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2008).

    Article  MathSciNet  MATH  Google Scholar 

  12. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. Assoc. Comput. Mach. 60, 84–90 (2017).

    Google Scholar 

  13. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016).

  14. Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–647 (2020).

    Article  Google Scholar 

  15. Harris, N. C. et al. Quantum transport simulations in a programmable nanophotonic processor. Nat. Photon. 11, 447–453 (2017).

    Article  Google Scholar 

  16. Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39–47 (2020).

    Article  Google Scholar 

  17. Zhuang, L., Roeloffzen, C. G. H., Hoekman, M., Boller, K.-J. & Lowery, A. J. Programmable photonic signal processor chip for radiofrequency applications. Optica 2, 854–859 (2015).

    Article  Google Scholar 

  18. Zhang, Q., Yu, H., Barbiero, M., Wang, B. & Gu, M. Artificial neural networks enabled by nanophotonics. Light Sci. Appl. 8, 42–56 (2019).

    Article  Google Scholar 

  19. Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019).

    Article  Google Scholar 

  20. Bogaerts, W. et al. Programmable photonic circuits. Nature 586, 207–216 (2020).

    Article  Google Scholar 

  21. Wang, J., Sciarrino, F., Laing, A. & Thompson, M. G. Integrated photonic quantum technologies. Nat. Photon. 14, 273–284 (2020).

    Article  Google Scholar 

  22. Wu, H. & Dai, Q. Artificial intelligence accelerated by light. Nature 589, 25–26 (2021).

    Article  Google Scholar 

  23. Feldman, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589, 52–58 (2021).

    Article  Google Scholar 

  24. Zhou, T. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photon. 15, 367–373 (2021).

    Article  MathSciNet  Google Scholar 

  25. Liu, C. et al. A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat. Electron. 2, 113–122 (2022).

    Article  Google Scholar 

  26. Williamson, I. A. D. et al. Reprogrammable electro-optic nonlinear activation functions for optical neural networks. IEEE J. Quantum Electron. 26, 7700412 (2020).

    Google Scholar 

  27. Joy, S. R., Erementchouk, M., Yu, H. & Mazumder, P. Spoof plasmon interconnects—communications beyond RC limit. IEEE Trans. Commun. 67, 599–610 (2019).

    Article  Google Scholar 

  28. Pendry, J. B., Martin-Moreno, L. & Garcia-Vidal, F. J. Mimicking surface plasmons with structured surfaces. Science 305, 847–848 (2004).

    Article  Google Scholar 

  29. Maier, S. A. Plasmonics: Fundamentals and Applications (Springer, 2007).

  30. Shen, X., Cui, T. J., Martin-Cano, D. & Garcia-Vidal, F. J. Conformal surface plasmons propagating on ultrathin and flexible films. Proc. Natl Acad. Sci. USA 110, 40–45 (2013).

    Article  Google Scholar 

  31. Zhang, H. C. et al. A plasmonic route for the integrated wireless communication of subdiffraction-limited signals. Light Sci. Appl. 9, 113 (2020).

    Article  Google Scholar 

  32. Gao, X. et al. Crosstalk suppression based on mode mismatch between spoof SPP transmission line and microstrip. IEEE Trans. Compon. Package Manuf. Technol. 9, 2267–2275 (2019).

    Article  Google Scholar 

  33. Zhang, H. C., Cui, T. J., Zhang, Q., Fan, Y. & Fu, X. Breaking the challenge of signal integrity using time-domain spoof surface plasmon polaritons. ACS Photon. 2, 1333–1340 (2015).

    Article  Google Scholar 

  34. Cui, T. J. Microwave metamaterials. Nat. Sci. Rev. 5, 134–136 (2018).

    Article  Google Scholar 

  35. Gao, X. et al. Programmable multifunctional device based on spoof surface plasmon polaritons. IEEE Trans. Antennas Propag. 68, 1558 (2020).

    Article  Google Scholar 

  36. Gao, X. et al. Programmable hybrid circuit based on reconfigurable SPP and spatial waveguide modes. Adv. Mater. Technol. 5, 1900828 (2019).

    Article  Google Scholar 

  37. Zhang, J. et al. Integrated spoof plasmonic circuits. Sci. Bull. 64, 843–855 (2019).

    Article  Google Scholar 

  38. Ma, Q. et al. Controllable and programmable nonreciprocity based on detachable digital coding metasurface. Adv. Opt. Mater. 7, 1901285 (2019).

    Article  Google Scholar 

  39. Ma, Q. et al. Smart sensing metasurface with self-defined functions in dual polarization. Nanophotonics 9, 3271–3278 (2020).

    Article  Google Scholar 

  40. IEEE Standard for Information Technology—Telecommunications and Information Exchange Between Systems—Local and Metropolitan Area Networks—Specific Requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications—Amendment 4: Enhancements for Very High Throughput for Operation in Bands Below 6 GHz IEEE Std 802.11ac-2013 (amendment to IEEE Std 802.11-2012, as amended by IEEE Std 802.11ae-2012, IEEE Std 802.11aa-2012, and IEEE Std 802.11ad-2012) (IEEE, 2013).

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Acknowledgements

This work was supported by the Basic Scientific Center of Information Metamaterials of the National Natural Science Foundation of China under grants 62288101 (T.J.C.) and 92167202 (Q.M.), the National Key Research and Development Program of China under grants 2017YFA0700201, 2017YFA0700202 and 2017YFA0700203 (T.J.C.), the Major Project of Natural Science Foundation of Jiangsu Province under grant BK20212002 (T.J.C.), the 111 Project under grant 111-2-05 (T.J.C.) and the China Postdoctoral Science Foundation under grants 2021M700761 and 2022T150112 (Q.M.).

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Author notes

  1. These authors contributed equally: Xinxin Gao, Qian Ma, Ze Gu.

Authors and Affiliations

  1. Institute of Electromagnetic Space, Southeast University, Nanjing, China

    Xinxin Gao, Qian Ma, Ze Gu, Wen Yi Cui, Che Liu, Jingjing Zhang & Tie Jun Cui

  2. State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China

    Xinxin Gao, Qian Ma, Ze Gu, Wen Yi Cui, Che Liu, Jingjing Zhang & Tie Jun Cui

  3. State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong, China

    Xinxin Gao

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Contributions

T.J.C. and Q.M. initiated the plan and supervised the entire study. X.G., Q.M. and Z.G. conceived the idea of this work, and designed the simulations and experiments. X.G., Q.M., Z.G., W.Y.C., J.Z. and C.L. carried out the measurements and data analyses. X.G., Q.M., Z.G. and T.J.C. prepared the manuscript with input from all authors. All authors discussed the research.

Corresponding authors

Correspondence to Qian Ma or Tie Jun Cui.

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Nature Electronics thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary Sections 1–18, Figs. 1–16 and Tables 1–10.

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Gao, X., Ma, Q., Gu, Z. et al. Programmable surface plasmonic neural networks for microwave detection and processing. Nat Electron 6, 319–328 (2023). https://doi.org/10.1038/s41928-023-00951-x

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