Resonant tunneling diode nano-optoelectronic excitable nodes for neuromorphic spike-based information processing

Matej Hejda, Juan Arturo Alanis, Ignacio Ortega-Piwonka, João Lourenco, José Figueiredo, Julien Javaloyes, Bruno Romeira, Antonio Hurtado

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
11 Downloads (Pure)

Abstract

In this work, we introduce an interconnected nano-optoelectronic spiking artificial neuron emitter-receiver system capable of operating at ultrafast rates (about 100ps/optical spike) and with low-energy consumption (< pJ/spike). The proposed system combines an excitable resonant tunneling diode (RTD) element exhibiting negative differential conductance, coupled to a nanoscale light source (forming a master node) or a photodetector (forming a receiver node). We study numerically the spiking dynamical responses and information propagation functionality of an interconnected master-receiver RTD node system. Using the key functionality of pulse thresholding and integration, we utilize a single node to classify sequential pulse patterns and perform convolutional functionality for image feature (edge) recognition. We also demonstrate an optically interconnected spiking neural network model for processing of spatiotemporal data at over 10 Gbit/s with high inference accuracy. Finally, we demonstrate an off-chip supervised learning approach utilizing spike-timing-dependent plasticity for the RTD-enabled photonic spiking neural network. These results demonstrate the potential and viability of RTD spiking nodes for low footprint, low-energy, high-speed optoelectronic realization of spike-based neuromorphic hardware.

Original languageEnglish
Article number024072
Number of pages13
JournalPhysical Review Applied
Volume17
Issue number2
DOIs
Publication statusPublished - 25 Feb 2022

Keywords

  • information processing
  • optoelectronic
  • nodes

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