Neuromorphic Photonic Processing and Memory with Spiking Resonant Tunnelling Diode Neurons and Neural Networks

Abstract

Neuromorphic computing-modelled after the functionality and efficiency of biological neural systems-offers promising new directions for advancing artificial intelligence and computational models. Photonic techniques for neuromorphic computing hardware are attracting increasing research interest, thanks to their potentials for ultra high bandwidths, low-crosstalk and high parallelism. Among these, approaches based upon resonant tunnelling diodes (RTDs) have recently gained attention as potential building blocks for next-generation light-enabled neuromorphic hardware, due to their capacity to replicate key neuronal behaviours such as excitable spiking and refractoriness, added to their potentials for high operational speeds, energy efficiency and compact footprints. In particular, their ability to function as opto-electronic spiking neurons makes them strong candidates for integration into novel event based neuromorphic computing systems. This work demonstrates the application of optically-triggered spiking RTD neurons to a multiplicity of applications and architectures, these include systems based upon single elements for multi-modal (photonic-electronic) fast rising edge-detection in time-series data, the construction of a two-layer feedforward artificial photonic spiking neural network (pSNN) using RTD neurons as the nonlinear nodes delivering excellent performance in complex dataset classification tasks, and a pSNN comprised of multiple coupled light-sensitive RTD spiking neurons that supports performance as an adjustable neuromorphic optical spiking memory system with a tunable storage time of spiking patterns.