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:

A silicon photonic–electronic neural network for fibre nonlinearity compensation

Nature Electronics volume 4, pages 837–844 (2021)Cite this article

Subjects

Abstract

In optical communication systems, fibre nonlinearity is the major obstacle in increasing the transmission capacity. Typically, digital signal processing techniques and hardware are used to deal with optical communication signals, but increasing speed and computational complexity create challenges for such approaches. Highly parallel, ultrafast neural networks using photonic devices have the potential to ease the requirements placed on digital signal processing circuits by processing the optical signals in the analogue domain. Here we report a silicon photonic–electronic neural network for solving fibre nonlinearity compensation in submarine optical-fibre transmission systems. Our approach uses a photonic neural network based on wavelength-division multiplexing built on a silicon photonic platform compatible with complementary metal–oxide–semiconductor technology. We show that the platform can be used to compensate for optical fibre nonlinearities and improve the quality factor of the signal in a 10,080 km submarine fibre communication system. The Q-factor improvement is comparable to that of a software-based neural network implemented on a workstation assisted with a 32-bit graphic processing unit.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: PNN schematics and devices.
Fig. 2: Schematic of the experimental test setup.
Fig. 3: Training with a PNN.
Fig. 4: Programmable and real-time response of photonic neurons and signal-quality performance after being processed by the PNN.
Fig. 5: Signal-quality performance with the activation functions provided by photonic neurons and by Leaky ReLU.

Similar content being viewed by others

Data availability

All data used in this study are available from the corresponding authors upon reasonable request.

Code availability

All codes used in this study are available from the corresponding authors upon reasonable request.

References

  1. Najafabadi, M. M. et al. Deep learning applications and challenges in big data analytics. J. Big Data 2, 1 (2015).

    Article  Google Scholar 

  2. Deng, L. et al. Deep learning: methods and applications. Found. Trends Signal Process. 7, 197–387 (2014).

    Article  MathSciNet  Google Scholar 

  3. De Lima, T. F. et al. Machine learning with neuromorphic photonics. J. Lightwave Technol. 37, 1515–1534 (2019).

    Article  Google Scholar 

  4. Wang, Y. & Boyd, S. Fast model predictive control using online optimization. IEEE Trans. Control Syst. Technol. 18, 267–278 (2009).

    Article  Google Scholar 

  5. Shi, Y. et al. Deep learning for RF signal classification in unknown and dynamic spectrum environments. In 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) 1–10 (IEEE, 2019).

  6. Zibar, D., Piels, M., Jones, R. & Scha¨effer, C. G. Machine learning techniques in optical communication. J. Lightwave Technol. 34, 1442–1452 (2015).

    Article  Google Scholar 

  7. Khan, F. N., Fan, Q., Lu, C. & Lau, A. P. T. An optical communication’s perspective on machine learning and its applications. J. Lightwave Technol. 37, 493–516 (2019).

    Article  Google Scholar 

  8. Zhang, S. et al. Field and lab experimental demonstration of nonlinear impairment compensation using neural networks. Nat. Commun. 10, 3033 (2019).

    Article  Google Scholar 

  9. Ip, E. & Kahn, J. M. Compensation of dispersion and nonlinear impairments using digital backpropagation. J. Lightwave Technol. 26, 3416–3425 (2008).

    Article  Google Scholar 

  10. Giacoumidis, E., Lin, Y., Blott, M. & Barry, L. P. Real-time machine learning based fiber- induced nonlinearity compensation in energy-efficient coherent optical networks. APL Photon. 5, 041301 (2020).

    Article  Google Scholar 

  11. Morero, D. A., Castrillon, M. A., Aguirre, A., Hueda, M. R. & Agazzi, O. E. Design tradeoffs and challenges in practical coherent optical transceiver implementations. J. Lightwave Technol. 34, 121–136 (2016).

    Article  Google Scholar 

  12. Du, L. B. et al. Digital fiber nonlinearity compensation: toward 1-Tb/s transport. IEEE Signal Process. Mag. 31, 46–56 (2014).

    Article  Google Scholar 

  13. Prucnal, P. R. & Shastri, B. J. Neuromorphic Photonics (CRC Press, 2017).

  14. Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photon. 15, 102–114 (2021).

    Article  Google Scholar 

  15. Brunner, D. & Fischer, I. Reconfigurable semiconductor laser networks based on diffractive coupling. Opt. Lett. 40, 3854–3857 (2015).

    Article  Google Scholar 

  16. Zuo, Y. et al. All-optical neural network with nonlinear activation functions. Optica 6, 1132–1137 (2019).

    Article  Google Scholar 

  17. Hill, M. T., Frietman, E. E., de Waardt, H., Khoe, G.-D. & Dorren, H. J. All fiber-optic neural network using coupled SOA based ring lasers. IEEE Trans. Neural Netw. 13, 1504–1513 (2002).

    Article  Google Scholar 

  18. Fok, M. P. et al. Signal feature recognition based on lightwave neuromorphic signal processing. Opt. Lett. 36, 19–21 (2011).

    Article  Google Scholar 

  19. Brunner, D., Soriano, M. C., Mirasso, C. R. & Fischer, I. Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun. 4, 1364 (2013).

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  21. Bueno, J. et al. Reinforcement learning in a large-scale photonic recurrent neural network. Optica 5, 756–760 (2018).

    Article  Google Scholar 

  22. Tait, A. N. et al. Neuromorphic photonic networks using silicon photonic weight banks. Sci. Rep. 7, 7430 (2017).

    Article  Google Scholar 

  23. Peng, H.-T., Nahmias, M. A., De Lima, T. F., Tait, A. N. & Shastri, B. J. Neuromorphic photonic integrated circuits. IEEE J. Sel. Topics Quantum Electron. 24, 1–15 (2018).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Vandoorne, K. et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat. Commun. 5, 3541 (2014).

    Article  Google Scholar 

  27. Tait, A. N. et al. Silicon photonic modulator neuron. Phys. Rev. Appl. 11, 064043 (2019).

    Article  Google Scholar 

  28. Huang, C. et al. On-chip programmable nonlinear optical signal processor and its applications. IEEE J. Sel. Topics Quantum Electron. 27, 1–11 (2020).

  29. Essiambre, R.-J., Kramer, G., Winzer, P. J., Foschini, G. J. & Goebel, B. Capacity limits of optical fiber networks. J. Lightwave Technol. 28, 662–701 (2010).

    Article  Google Scholar 

  30. Tao, Z. et al. Multiplier-free intrachannel nonlinearity compensating algorithm operating at symbol rate. J. Lightwave Technol. 29, 2570–2576 (2011).

    Article  Google Scholar 

  31. Jouppi, N. P. et al. In-datacenter performance analysis of a tensor processing unit. In Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA '17). Association for Computing Machinery, New York, NY, USA, 1–12.

  32. Sun, J., Timurdogan, E., Yaacobi, A., Hosseini, E. S. & Watts, M. R. Large-scale nanophotonic phased array. Nature 493, 195–199 (2013).

    Article  Google Scholar 

  33. Poulton, C. V. et al. 8192-element optical phased array with 100° steering range and flip-chip CMOS. In CLEO: Applications and Technology JTh4A.3 (Optical Society of America, 2020).

  34. Tait, A. N. et al. Feedback control for microring weight banks. Opt. Express 26, 26422–26443 (2018).

    Article  Google Scholar 

  35. Huang, C. et al. Demonstration of scalable microring weight bank control for large-scale photonic integrated circuits. APL Photon. 5, 040803 (2020).

    Article  Google Scholar 

  36. Zhang, W. et al. Microring weight banks control beyond 8.5-bits accuracy. Preprint at https://arxiv.org/abs/2104.01164 (2021).

  37. Hai, M. S., Sakib, M. N. & Liboiron-Ladouceur, O. A 16 GHz silicon-based monolithic balanced photodetector with on-chip capacitors for 25 Gbaud front-end receivers. Opt. Express 21, 32680–32689 (2013).

    Article  Google Scholar 

  38. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).

  39. Carroll, L. et al. Photonic packaging: transforming silicon photonic integrated circuits into photonic devices. Appl. Sci. 6, 426 (2016).

    Article  Google Scholar 

  40. Atabaki, A. H., Eftekhar, A. A., Askari, M. & Adibi, A. Accurate post-fabrication trimming of ultra-compact resonators on silicon. Opt. Express 21, 14139–14145 (2013).

    Article  Google Scholar 

  41. Wang, Z. et al. In situ training of feed-forward and recurrent convolutional memristor networks. Nat. Mach. Intell. 1, 434–442 (2019).

    Article  Google Scholar 

  42. Brasch, V. et al. Photonic chip–based optical frequency comb using soliton Cherenkov radiation. Science 351, 357–360 (2016).

    Article  MathSciNet  Google Scholar 

  43. de Lima, T. F. et al. Noise analysis of photonic modulator neurons. IEEE J. Sel. Topics Quantum Electron. 26, 1–9 (2019).

    Article  Google Scholar 

  44. Nozaki, K. et al. Femtofarad optoelectronic integration demonstrating energy-saving signal conversion and nonlinear functions. Nat. Photon. 13, 454–459 (2019).

    Article  Google Scholar 

  45. Nahmias, M. A. et al. Photonic multiply-accumulate operations for neural networks. IEEE J. Sel. Topics Quantum Electron. 26, 1–18 (2019).

    Article  Google Scholar 

  46. Tait, A. N., Nahmias, M. A., Shastri, B. J. & Prucnal, P. R. Broadcast and weight: an integrated network for scalable photonic spike processing. J. Lightwave Technol. 32, 4029–4041 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the Office of Naval Research (ONR) (N00014-18-1-2297), Defense Advanced Research Projects Agency (HR00111990049), National Science Foundation (NSF) (grant nos. ECCS 1642962 and DGE 1148900) and CUHK Research Direct Grant (170257018). The devices were fabricated at the IME A*STAR foundry, Singapore. Fabrication support was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) Silicon Electronic-Photonic Integrated Circuits (SiEPIC) Program and the Canadian Microelectronics Corporation (CMC).

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA

    Chaoran Huang, Thomas Ferreira de Lima, Alexander N. Tait, Eric C. Blow, Simon Bilodeau, Aashu Jha, Hsuan-Tung Peng, Bhavin J. Shastri & Paul R. Prucnal

  2. Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China

    Chaoran Huang

  3. NEC Laboratories America, Princeton, NJ, USA

    Shinsuke Fujisawa, Yue Tian, Fatih Yaman, Hussam G. Batshon & Ting Wang

  4. Submarine Network Division, NEC Corporation, Tokyo, Japan

    Shinsuke Fujisawa & Yoshihisa Inada

  5. Department of Physics, Engineering Physics and Astronomy, Queens University, Kingston, Ontario, Canada

    Bhavin J. Shastri

Authors
  1. Chaoran Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shinsuke Fujisawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Ferreira de Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexander N. Tait
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eric C. Blow
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yue Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Simon Bilodeau
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Aashu Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Fatih Yaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hsuan-Tung Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hussam G. Batshon
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Bhavin J. Shastri
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yoshihisa Inada
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Ting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Paul R. Prucnal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.H., S.F. and T.F.L. conceived the ideas and implemented the experimental setup, designed the experiment, conducted the experimental measurements and analysed the results. C.H., T.F.L. E.C.B., S.B., A.J. and H.-T.P. designed the silicon photonic chip. A.N.T., Y.T., F.Y. and B.J.S. provided the theoretical support. T.F.L., E.C.B. and S.B. performed the chip packaging. C.H., S.F., T.F.L., A.N.T. and B.J.S. wrote the manuscript. T.W. and P.R.P. supervised the research and contributed to the general concept and interpretation of the results. All the authors discussed the data and contributed to the manuscript.

Corresponding authors

Correspondence to Chaoran Huang or Paul R. Prucnal.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Electronics thanks José Capmany and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Tables 1–4 and Discussion.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, C., Fujisawa, S., de Lima, T.F. et al. A silicon photonic–electronic neural network for fibre nonlinearity compensation. Nat Electron 4, 837–844 (2021). https://doi.org/10.1038/s41928-021-00661-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-021-00661-2

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