Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-28T05:57:37.284Z Has data issue: false hasContentIssue false
  • English
  • Français

Graphene-based photonic synapse for multi wavelength neural networks

Published online by Cambridge University Press:  06 August 2020

Bicky A. Marquez ,
Hugh Morison ,
Zhimu Guo ,
Matthew Filipovich ,
Paul R. Prucnal  and
Bhavin J. Shastri Open the ORCID record for Bhavin J. Shastri [Opens in a new window]
Bicky A. Marquez
Affiliation:
Department of Physics, Engineering Physics and Astronomy, Queen's University, Kingston, ONK7L 3N6, Canada
Hugh Morison
Affiliation:
Department of Physics, Engineering Physics and Astronomy, Queen's University, Kingston, ONK7L 3N6, Canada
Zhimu Guo
Affiliation:
Department of Physics, Engineering Physics and Astronomy, Queen's University, Kingston, ONK7L 3N6, Canada
Matthew Filipovich
Affiliation:
Department of Physics, Engineering Physics and Astronomy, Queen's University, Kingston, ONK7L 3N6, Canada
Paul R. Prucnal
Affiliation:
Department of Electrical Engineering, Princeton University, Princeton, NJ08540, USA
Bhavin J. Shastri
Affiliation:
Department of Physics, Engineering Physics and Astronomy, Queen's University, Kingston, ONK7L 3N6, Canada Department of Electrical Engineering, Princeton University, Princeton, NJ08540, USA
Get access

Abstract

A synapse is a junction between two biological neurons, and the strength, or weight of the synapse, determines the communication strength between the neurons. Building a neuromorphic (i.e. neuron isomorphic) computing architecture, inspired by a biological network or brain, requires many engineered synapses. Furthermore, recent investigation in neuromorphic photonics, i.e. neuromorphic architectures on photonics platforms, have garnered much interest to enable high-bandwidth, low-latency, low-energy applications of neural networks in machine learning and neuromorphic computing. We propose a graphene-based synapse model as a core element to enable large-scale photonic neural networks based on on-chip multiwavelength techniques. This device consists of an electro-absorption modulator embedded in a microring resonator. We also introduce an encoding protocol that allows for the representation of synaptic weights on our photonic device with 15.7 bits of resolution using current control hardware. Recent work has suggested that graphene-based modulators could operate in excess of 100 GHz. Combined with our work, such a graphene-based synapse could enable applications for ultrafast and online learning.

Type
Articles
Information
MRS Advances , Volume 5 , Issue 37-38: Electronics and Photonics , 2020 , pp. 1909 - 1917
Check for updates
Copyright
Copyright © Materials Research Society 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Strubell, E., Ganesh, A., and McCallum, A., Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, no. 1, 3645 (2019).Google Scholar
Prucnal, P. R. and Shastri, B. J.. Neuromorphic Photonics (CRC Press, 2017).CrossRefGoogle Scholar
Ferreira de Lima, T., Peng, H.-T., Tait, A.N., Nahmias, M.A., Miller, H.B., Shastri, B.J., and Prucnal, P.R., J. Light. Technol. 37, 1515–1534 (2019).CrossRefGoogle Scholar
Tait, A.N., Ferreira de Lima, T., Zhou, E., Wu, A.X., Nahmias, M.A., Shastri, B.J., and Prucnal, P.R., Sci. Rep. 7, 7430 (2017).CrossRefGoogle Scholar
Feldmann, J., Youngblood, N., Wright, C.D., Bhaskaran, H., and Pernice, W.H.P., Nature 569, 208–214 (2019).CrossRefGoogle Scholar
Shen, Y., Harris, N.C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., Sun, X., Zhao, S., Larochelle, H., Englund, D., and Soljačić, M., Nat. Photon. 11, 441–446 (2017).CrossRefGoogle Scholar
Shainline, J.M., Buckley, S.M., Mirin, R.P., and Nam, S.W., Phys. Rev. Appl. 7, 034013 (2017).CrossRefGoogle Scholar
Tait, A.N., Nahmias, M.A., Shastri, B.J., and Prucnal, P.R., J. Light. Technol. 32, 4029–4041 (2014).CrossRefGoogle Scholar
Tait, A.N., Ferreira de Lima, T., Nahmias, M.A., Miller, H.B., Peng, H.-T., Shastri, B.J., and Prucnal, P.R., Phys. Rev. Appl. 11, 064043 (2019).CrossRefGoogle Scholar
Huang, C., Bilodeau, S., Ferreira de Lima, T., Tait, A.N., Ma, P.Y., Blow, E.C., Jha, A., Peng, H.-T., Shastri, B.J., and Prucnal, P.R., APL Photonics 5, 040803 (2020).CrossRefGoogle Scholar
Nahmias, M. A., Ferreira de Lima, T., Tait, A. N., Peng, H., Shastri, B. J. and Prucnal, P. R., IEEE J. Sel. Top. Quantum Electron. 26,1, (2020).CrossRefGoogle Scholar
Liu, M., Yin, X., Ulin-Avila, E., Geng, B., Zentgraf, T., Ju, L., Wang, F., and Zhang, X., Nature 474, 64 (2011).CrossRefGoogle Scholar
Phare, C.T., Lee, Y.-H.D., Cardenas, J., and Lipson, M., Nat. Photon. 9, 511–515 (2015).CrossRefGoogle Scholar
Jayatilleka, H., Shoman, H., Chrostowski, L., and Shekhar, S., Optica 6, 84–91 (2019).CrossRefGoogle Scholar
Tait, A.N., Jayatilleka, H., Ferreira De Lima, T., Ma, P.Y., Nahmias, M.A., Shastri, B.J., Shekhar, S., Chrostowski, L., and Prucnal, P.R., Opt. Express 26, 26422 (2018).CrossRefGoogle Scholar
Amin, R., Ma, Z., Maiti, R., Khan, S., Khurgin, J.B., Dalir, H., and Sorger, V.J., Appl. Opt. 57, D130 (2018).CrossRefGoogle Scholar
Lumerical Inc. https://www.lumerical.com/products/ (Accessed 28 July 2020)Google Scholar