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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
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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.

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MRS Advances , Volume 5 , Issue 37-38: Electronics and Photonics , 2020 , pp. 1909 - 1917
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Copyright © Materials Research Society 2020

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