Resonant Tunneling Diode Nano-Optoelectronic Excitable Nodes for Neuromorphic Spike-Based Information Processing

In this work, we introduce an interconnected nano-optoelectronic spiking artificial neuron emitterreceiver system capable of operating at ultrafast rates (about 100 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.

Automated summary

This work introduces an interconnected nano-optoelectronic spiking artificial neuron emitter-receiver system that operates at ultrafast rates and with low-energy consumption. The system utilizes pulse thresholding and integration for image feature recognition and demonstrates a spiking neural network model for processing spatiotemporal data at high speeds. It also showcases a supervised learning approach for the RTD-enabled photonic spiking neural network.