  We designed, prototyped, and experimentally demonstrated, for the first time
to our knowledge, an optoelectronic spiking neuron inspired by the Izhikevich
model incorporating both excitatory and inhibitory optical spiking inputs and
producing optical spiking outputs accordingly. The optoelectronic neurons
consist of three transistors acting as electrical spiking circuits, a
vertical-cavity surface-emitting laser (VCSEL) for optical spiking outputs, and
two photodetectors for excitatory and inhibitory optical spiking inputs.
Additional inclusion of capacitors and resistors complete the
Izhikevich-inspired optoelectronic neurons, which receive excitatory and
inhibitory optical spikes as inputs from other optoelectronic neurons. We
developed a detailed optoelectronic neuron model in Verilog-A and simulated the
circuit-level operation of various cases with excitatory input and inhibitory
input signals. The experimental results closely resemble the simulated results
and demonstrate how the excitatory inputs trigger the optical spiking outputs
while the inhibitory inputs suppress the outputs. Utilizing the simulated
neuron model, we conducted simulations using fully connected (FC) and
convolutional neural networks (CNN). The simulation results using MNIST
handwritten digits recognition show 90% accuracy on unsupervised learning and
97% accuracy on a supervised modified FC neural network. We further designed a
nanoscale optoelectronic neuron utilizing quantum impedance conversion where a
200 aJ/spike input can trigger the output from on-chip nanolasers with 10
fJ/spike. The nanoscale neuron can support a fanout of ~80 or overcome 19 dB
excess optical loss while running at 10 GSpikes/second in the neural network,
which corresponds to 100x throughput and 1000x energy-efficiency improvement
compared to state-of-art electrical neuromorphic hardware such as Loihi and
NeuroGrid.
