Photonic-electronic spiking neuron with multi-modal and multi-wavelength excitatory and inhibitory operation for high-speed neuromorphic sensing and computing - Weikang Zhang; Matěj Hejda; Qusay Raghib Ali Al-Taai; Dafydd Owen-Newns; Bruno Romeira; José M L Figueiredo; Joshua Robertson; Edward Wasige; Antonio Hurtado (Neuromorphic Computing and Engineering)
We report a multi-modal spiking neuron that allows optical and electronic input and control, and wavelength-multiplexing operation, for use in novel high-speed neuromorphic sensing and computing functionalities. The photonic-electronic neuron is built with a micro-scale, nanostructure resonant tunnelling diode (RTD) with photodetection (PD) capability. Leveraging the advantageous intrinsic properties of this RTD-PD system, namely highly nonlinear characteristics, photo-sensitivity, light-induced I – V curve shift, and the ability to deliver excitable responses under electrical and optical inputs, we successfully achieve flexible neuromorphic spike activation and inhibition regimes through photonic-electrical control. We also demonstrate the ability of this RTD-PD spiking sensing-processing neuron to operate under the simultaneous arrival of multiple wavelength-multiplexed optical signals, due to its large PD spectral window (covering the 1310 and 1550 nm telecom wavelength bands). Our results highlight the potential of RTD photonic-electronic neurons to reproduce multiple key excitatory and inhibitory spiking regimes, at high speed (10 s of ns-rate spiking responses, with faster sub-ns regimes theoretically predicted) and low energy (requiring only ∼10 mV and ∼150 µ W, electrical and optical input amplitudes, respectively), similar in nature to those commonly found in the biological neurons of the visual system and the brain. This work offers a highly promising approach for the realisation of high-speed, energy-efficient photonic-electronic spiking neurons and spiking neural networks, enabling multi-modal and multi-wavelength operation for sensing and information processing tasks, whilst also yielding enhanced system capacity, performance and parallelism. This work therefore paves the way for innovative high-speed, photonic-electronic, and spike-based neuromorphic sensing and computing systems and artificial intelligence hardware.