Journal Physics D: Applied Physics, volume 56, issue 8, pages 84001

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

Weikang Zhang 1
Matěj Hejda 1
Ekaterina Malysheva 2
Qusay Raghib Ali Al-Taai 3
Julien Javaloyes 4
Edward Wasige 3
Jose Alaor Figueiredo 5
Victor Dolores Calzadilla 2
B. Romeira 6
ANTONIO HURTADO 1
Show full list: 10 authors
Publication typeJournal Article
Publication date2023-02-07
scimago Q1
SJR0.681
CiteScore6.8
Impact factor3.1
ISSN00223727, 13616463
Surfaces, Coatings and Films
Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Acoustics and Ultrasonics
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.

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