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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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.
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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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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.
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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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DOI: https://doi.org/10.1038/s41928-023-00951-x
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