Tuneable presynaptic weighting in optoelectronic spiking neurons built with laser-coupled resonant tunneling diodes

Zhang, W., Hejda, M., Malysheva, E., Al-taai, Q. , Javaloyes, J., Wasige, E. , Figueiredo, J. M. L., Calzadilla, V., Romeira, B. and Hurtado, A. (2023) Tuneable presynaptic weighting in optoelectronic spiking neurons built with laser-coupled resonant tunneling diodes. Journal of Physics D: Applied Physics, 56, 084001. (doi: 10.1088/1361-6463/aca914)

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Abstract

Optoelectronic spiking neurons are regarded as highly promising systems for novel light-powered neuromorphic computing hardware. Here, we investigate an optoelectronic (O/E/O) spiking neuron built with an excitable resonant tunnelling diode (RTD) coupled to a photodetector and a vertical-cavity surface-emitting laser (VCSEL). This work provides the first experimental report on the control of the amplitude (weighting factor) of the fired optical spikes directly in the neuron, introducing a simple way for presynaptic spike amplitude tuning. Notably, a very simple mechanism (the control of VCSEL bias) is used to tune the amplitude of the spikes fired by the optoelectronic neuron, hence enabling an easy and high-speed option for the weighting of optical spiking signals in future interconnected photonic spike-processing nodes. Furthermore, we validate the feasibility of this layout using a simulation of a monolithically-integrated, RTD-powered, nanoscale optoelectronic spiking neuron model, confirming the system's potential for delivering weighted optical spiking signals at very high speeds (GHz firing rates). These results demonstrate the high degree of flexibility of RTD-based artificial optoelectronic spiking neurons and highlight their potential towards compact, high-speed and low-energy photonic spiking neural networks for use in future, light-enabled neuromorphic hardware.

Item Type:Articles
Additional Information:The authors acknowledge support by the European Commission (Grant 828841-ChipAI-H2020-FETOPEN-2018-2020) and by the UK Research and Innovation (UKRI) Turing AI Acceleration Fellowships Programme (EP/V025198/1).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Wasige, Professor Edward and Al-Taai, Dr Qusay and ZHANG, WEIKANG and Javaloyes, Dr Julien
Authors: Zhang, W., Hejda, M., Malysheva, E., Al-taai, Q., Javaloyes, J., Wasige, E., Figueiredo, J. M. L., Calzadilla, V., Romeira, B., and Hurtado, A.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:Journal of Physics D: Applied Physics
Publisher:IOP Publishing
ISSN:0022-3727
ISSN (Online):1361-6463
Published Online:06 December 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Journal of Physics D: Applied Physics 56:084001
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
303977ChipAIEdward WasigeEuropean Commission (EC)828841ENG - Electronics & Nanoscale Engineering