- Physical Review Journals
All JournalsPhysics Magazine

Physical Review Applied

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Spiking rate and latency encoding with resonant tunneling diode neuron circuits and design influences

Giovanni Donati1,*, Dafydd Owen-Newns1, Joshua Robertson1, Xavier Porte1, Ekaterina Malysheva2, Jose Figueiredo3, Bruno Romeira4, Victor Dolores-Calzadilla2, and Antonio Hurtado1

  • *Contact author: giovanni.donati@strath.ac.uk
PDF

Phys. Rev. Applied 24, 024041 – Published 18 August, 2025

DOI: https://doi.org/10.1103/n41r-3t9v

Abstract

This paper is a contribution to the Physical Review Applied collection titled Physics-Inspired Computing.

Neuromorphic computing, inspired by the functionality and efficiency of biological neural systems, holds promise for advancing artificial intelligence and computational paradigms. Resonant tunneling diodes (RTDs), thanks to their ability to generate neuronal dynamical responses, such as excitable spiking and refractoriness, have recently emerged as candidates for use as optoelectronic spiking neurons in novel neuromorphic computing hardware. This work explores the ability of RTD spiking neurons to deliver information encoding mechanisms analogous to those observed in biological neurons, specifically spike firing rate and spike latency encoding. We also report detailed experimental and numerical studies on the impacts that the RTD mesa radius and the circuit inductance and capacitance have on its spiking properties, providing useful information for future design of RTD-based neural networks optimized for ultrafast (greater than gigahertz rate) processing capabilities. Finally, we showcase the application of spike rate encoding in RTD neurons for the ultrafast reconstruction of a eight-level amplitude signal, effectively filtering out the noise.

View figure in article

Physics Subject Headings (PhySH)

Collections

This article appears in the following collection:

Collection on Physics-Inspired Computing

Physical Review Applied is pleased to present a Collection on Physics-Inspired Computing, highlighting the rapidly evolving field of energy-efficient computing techniques, from hardware technologies to algorithms, where physics inspiration serves as the crucial link. Contributions to this collection will be published throughout 2025. This Collection is being curated by Guest Editors Kerem Camsari and Supriyo Datta.

Article Text

References (33)

  1. C. D. Schuman, S. R. Kulkarni, M. Parsa, J. P. Mitchell, P. Date, and B. Kay, Opportunities for neuromorphic computing algorithms and applications, Nat. Comput. Sci. 2, 10 (2022).
  2. J. K. Eshraghian, M. Ward, E. O. Neftci, X. Wang, G. Lenz, G. Dwivedi, M. Bennamoun, D. S. Jeong, and W. D. Lu, Training spiking neural networks using lessons from deep learning, Proc. IEEE 111, 1016 (2023).
  3. M. M. Waldrop, The chips are down for Moore’s law, Nat. News 530, 144 (2016).
  4. S. B. Furber, F. Galluppi, S. Temple, and L. A. Plana, The SpiNNaker project, Proc. IEEE 102, 652 (2014).
  5. E. Painkras, L. A. Plana, J. Garside, S. Temple, F. Galluppi, C. Patterson, D. R. Lester, A. D. Brown, and S. B. Furber, SpiNNaker: A 1-W 18-core system-on-chip for massively-parallel neural network simulation, IEEE J. Solid-State Circuits 48, 1943 (2013).
  6. M. V. DeBole, B. Taba, A. Amir, F. Akopyan, A. Andreopoulos, W. P. Risk, J. Kusnitz, C. O. Otero, T. K. Nayak, R. Appuswamy et al., TrueNorth: Accelerating from zero to 64 million neurons in 10 years, Computer 52, 20 (2019).
  7. M. Davies, N. Srinivasa, T.-H. Lin, G. Chinya, Y. Cao, S. H. Choday, G. Dimou, P. Joshi, N. Imam, S. Jain et al., Loihi: A neuromorphic manycore processor with on-chip learning, IEEE Micro 38, 82 (2018).
  8. J. Schemmel, L. Kriener, P. Müller, and K. Meier, in 2017 International Joint Conference on Neural Networks (IJCNN) (IEEE, Anchorage, AK, USA, 2017), pp. 2217–2226.
  9. F. Ashtiani, A. J. Geers, and F. Aflatouni, An on-chip photonic deep neural network for image classification, Nature 606, 501 (2022).
  10. S. Biasi, R. Franchi, L. Cerini, and L. Pavesi, An array of microresonators as a photonic extreme learning machine, APL Photonics 8, 096105 (2023).
  11. J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran, and W. H. Pernice, All-optical spiking neurosynaptic networks with self-learning capabilities, Nature 569, 208 (2019).
  12. X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, All-optical machine learning using diffractive deep neural networks, Science 361, 1004 (2018).
  13. D. K. Ferry, Transport in Semiconductor Mesoscopic Devices (IOP Publishing, Bristol, UK, 2020).
  14. C. Ironside, B. Romeira, and J. Figueiredo, Resonant Tunnelling Diode Photonics Devices and Applications (IOP Publishing, Bristol, UK, 2023).
  15. D. Cimbri, J. Wang, A. Al-Khalidi, and E. Wasige, Resonant tunneling diodes high-speed terahertz wireless communications - A review, IEEE Trans. Terahertz Sci. Technol. 12, 226 (2022).
  16. Y. Nishida, N. Nishigami, S. Diebold, J. Kim, M. Fujita, and T. Nagatsuma, Terahertz coherent receiver using a single resonant tunnelling diode, Sci. Rep. 9, 18125 (2019).
  17. B. Romeira, J. Javaloyes, C. N. Ironside, J. M. Figueiredo, S. Balle, and O. Piro, Excitability and optical pulse generation in semiconductor lasers driven by resonant tunneling diode photo-detectors, Opt. Express 21, 20931 (2013).
  18. M. Hejda, J. A. Alanis, I. Ortega-Piwonka, J. Lourenço, J. Figueiredo, J. Javaloyes, B. Romeira, and A. Hurtado, Resonant tunneling diode nano-optoelectronic excitable nodes for neuromorphic spike-based information processing, Phys. Rev. Appl. 17, 024072 (2022).
  19. M. Hejda, E. Malysheva, D. Owen-Newns, Q. R. Ali Al-Taai, W. Zhang, I. Ortega-Piwonka, J. Javaloyes, E. Wasige, V. Dolores-Calzadilla, J. M. Figueiredo et al., Artificial optoelectronic spiking neuron based on a resonant tunnelling diode coupled to a vertical cavity surface emitting laser, Nanophotonics 12, 857 (2023).
  20. I. Ortega-Piwonka, O. Piro, J. Figueiredo, B. Romeira, and J. Javaloyes, Bursting and excitability in neuromorphic resonant tunneling diodes, Phys. Rev. Appl. 15, 034017 (2021).
  21. B. Romeira, R. Avó, J. Javaloyes, S. Balle, C. N. Ironside, and J. M. Figueiredo, Stochastic induced dynamics in neuromorphic optoelectronic oscillators, Opt. Quantum Electron. 46, 1391 (2014).
  22. G. Donati, D. Owen-Newns, J. Robertson, E. Malysheva, A. Adair, J. Figueiredo, B. Romeira, V. Dolores-Calzadilla, and A. Hurtado, Spiking flip-flop memory in resonant tunnelling diode neurons, Phys. Rev. Lett. 133, 267301 (2024).
  23. K. Sano, K. Murata, T. Otsuji, T. Akeyoshi, N. Shimizu, and E. Sano, An 80-Gb/s optoelectronic delayed flip-flop IC using resonant tunneling diodes and uni-traveling-carrier photodiode, IEEE J. Solid-State Circuits 36, 281 (2001).
  24. B. Romeira, J. M. Figueiredo, and J. Javaloyes, Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications, Chaos 27, 114323 (2017).
  25. T. J. Slight and C. N. Ironside, Investigation into the integration of a resonant tunnelling diode and an optical communications laser: Model and experiment, IEEE J. Quantum Electron. 43, 580 (2007).
  26. B. J. Richmond, L. M. Optican, M. Podell, and H. Spitzer, Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. I. Response characteristics, J. Neurophysiol. 57, 132 (1987).
  27. J. C. Middlebrooks, A. E. Clock, L. Xu, and D. M. Green, A panoramic code for sound location by cortical neurons, Science 264, 842 (1994).
  28. R. Talukder, A. Skalli, X. Porte, S. Thorpe, and D. Brunner, A spiking photonic neural network of 40.000 neurons, trained with rank-order coding for leveraging sparsity, arXiv:2411.19209.
  29. J. L. Figueiredo, C. N. Ironside, and C. R. Stanley, Electric field switching in a resonant tunneling diode electroabsorption modulator, IEEE J. Quantum Electron. 37, 1547 (2001).
  30. B. Romeira, R. R. Adão, J. B. Nieder, Q. Al-Taai, W. Zhang, R. H. Hadfield, E. Wasige, M. Hejda, A. Hurtado, E. Malysheva et al., Brain-inspired nanophotonic spike computing: Challenges and prospects, Neuromorphic Comput. Eng. 3, 033001 (2023).
  31. J. Schulman, H. De Los Santos, and D. Chow, Physics-based RTD current-voltage equation, IEEE Electron Device Lett. 17, 220 (1996).
  32. J. Robertson, D. Black, G. Donati, Q. R. A. Al-Taai, E. Malysheva, B. Romeira, J. Figueiredo, V. D. Calzadilla, E. Wasige, and A. Hurtado, Ultrafast and compact photonic-electronic leaky integrate-and-fire circuits based upon resonant tunnelling diodes, arXiv:2501.17133.
  33. G. Donati, Data for: “Spiking rate and latency encoding with resonant tunnelling diode neuron circuits and design influences” (2025), https://doi.org/10.15129/fa675685-5b03-4866-a532-c291411f6ffc.

Outline

Information

Phys. Rev. Applied 24, 024041– Published 18 August, 2025
  • Received 28 March 2025
  • Revised 1 July 2025
  • Accepted 15 July 2025
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation