Photonic Spiking Neural Networks (PSNN) composed of the co-integrated CMOS
and photonic elements can offer low loss, low power, highly-parallel, and
high-throughput computing for brain-inspired neuromorphic systems. In addition,
heterogeneity of neuron dynamics can also bring greater diversity and
expressivity to brain-inspired networks, potentially allowing for the
implementation of complex functions with fewer neurons. In this paper, we
design, fabricate, and experimentally demonstrate an optoelectronic spiking
neuron that can simultaneously achieve high programmability for heterogeneous
biological neural networks and maintain high-speed computing. We demonstrate
that our neuron can be programmed to tune four essential parameters of neuron
dynamics under 1GSpike/s input spiking pattern signals. A single neuron circuit
can be tuned to output three spiking patterns, including chattering behaviors.
The PSNN consisting of the optoelectronic spiking neuron and a Mach-Zehnder
interferometer (MZI) mesh synaptic network achieves 89.3% accuracy on the Iris
dataset. Our neuron power consumption is 1.18 pJ/spike output, mainly limited
by the power efficiency of the vertical-cavity-lasers, optical coupling
efficiency, and the 45 nm CMOS platform used in this experiment, and is
predicted to achieve 36.84 fJ/spike output with a 7 nm CMOS platform (e.g.
ASAP7) integrated with silicon photonics containing on-chip micron-scale
lasers