Programmable Optical Synaptic Linking of Neuromorphic Photonic-Electronic RTD Spiking Circuits

Interconnectivity between functional building blocks (such as neurons and synapses) represents a fundamental functionality for realizing neuromorphic systems. However, in the domain of neuromorphic photonics, synaptic interlinking and cascadability of spiking optical artificial neurons remains challenging and mostly unexplored in experiments. In this work, we report an optical synaptic link between optoelectronic spiking artificial neurons based upon resonant tunneling diodes (RTDs) that allows for cascadable spike propagation. First, deterministic spiking is triggered using multimodal (electrical and optical) inputs in RTD-based spiking artificial neurons, which are optoelectronic (OE) circuits incorporating either micron-scale RTDs or photosensitive nanopillar-based RTDs. Second, feedforward linking with dynamical weighting of optical spiking signals between pre- and postsynaptic RTD artificial neurons is demonstrated, including cascaded spike activation. By dynamically weighting the amplitude of optical spikes, it is shown how the cascaded spike activation probability in the postsynaptic RTD node directly follows the amplitude of the weighted optical spikes. This work therefore provides the first experimental demonstration of programmable synaptic optical link and spike cascading between multiple fast and efficient RTD OE spiking artificial neurons, therefore providing a key functionality for photonic-electronic spiking neural networks and light-enabled neuromorphic hardware.

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Publication Details

Journal
ACS Photonics
Published
2024-09-30
DOI
https://doi.org/10.1021/acsphotonics.4c01199
Citations
2
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.69

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article

Programmable Optical Synaptic Linking of Neuromorphic Photonic-Electronic RTD Spiking Circuits

J. M. L. Figueiredo, Bruno Romeira, Joshua Robertson, Ekaterina Malysheva et al.
2 citations
ACS Photonics
Neural Networks and Reservoir Computing
0.69
article

Programmable Optical Synaptic Linking of Neuromorphic Photonic-Electronic RTD Spiking Circuits

J. M. L. Figueiredo, Bruno Romeira, Joshua Robertson, Ekaterina Malysheva, Antonio Hurtado, Edward Wasige, Qusay Raghib Ali Al-Taai, Matéj Hejda, Victor Dolores-Calzadilla, Dafydd Owen-Newns, Weikang Zhang
article en
2 citations

Abstract

Interconnectivity between functional building blocks (such as neurons and synapses) represents a fundamental functionality for realizing neuromorphic systems. However, in the domain of neuromorphic photonics, synaptic interlinking and cascadability of spiking optical artificial neurons remains challenging and mostly unexplored in experiments. In this work, we report an optical synaptic link between optoelectronic spiking artificial neurons based upon resonant tunneling diodes (RTDs) that allows for cascadable spike propagation. First, deterministic spiking is triggered using multimodal (electrical and optical) inputs in RTD-based spiking artificial neurons, which are optoelectronic (OE) circuits incorporating either micron-scale RTDs or photosensitive nanopillar-based RTDs. Second, feedforward linking with dynamical weighting of optical spiking signals between pre- and postsynaptic RTD artificial neurons is demonstrated, including cascaded spike activation. By dynamically weighting the amplitude of optical spikes, it is shown how the cascaded spike activation probability in the postsynaptic RTD node directly follows the amplitude of the weighted optical spikes. This work therefore provides the first experimental demonstration of programmable synaptic optical link and spike cascading between multiple fast and efficient RTD OE spiking artificial neurons, therefore providing a key functionality for photonic-electronic spiking neural networks and light-enabled neuromorphic hardware.

ACS PhotonicsVol. 11(10)
University of Lisbon (PT), University of Strathclyde (GB), International Iberian Nanotechnology Laboratory (PT), University of Glasgow (GB), Eindhoven University of Technology (NL)
Engineering and Physical Sciences Research Council, HORIZON EUROPE European Innovation Council
Affordable and clean energy
Openalex Percentile: Top 24%
Neural Networks and Reservoir Computing
0.69
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