In this work, we introduce an interconnected nano-optoelectronic spiking artificial neuron emitter-receiver system capable of operating at ultrafast rates (about $100\mathrm{ps}/$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.

• Received 22 July 2021
• Revised 14 January 2022
• Accepted 26 January 2022

DOI:https://doi.org/10.1103/PhysRevApplied.17.024072