Experimental demonstration of coherent photonic neural computing based on a Fabry-Perot laser with a saturable absorber

As Moore's law has reached its limits, it is becoming increasingly difficult for traditional computing architectures to meet the demands of continued growth in computing power. Photonic neural computing has become a prom-ising approach to overcome the von Neuman bottleneck. However, while photonic neural networks are good at linear computing, it is difficult to achieve nonlinear computing. Here, we propose and experimentally demon-strate a coherent photonic spiking neural network consisting of Mach-Zehnder modulators (MZMs) as the syn-apse and an integrated quantum-well Fabry-Perot laser with a saturable absorber (FP-SA) as the photonic spiking neuron. Both linear computation and nonlinear computation are realized in the experiment. In such a coherent architecture, two presynaptic signals are modulated and weighted with two intensity modulation MZMs through the same optical carrier. The nonlinear neuron-like dynamics including temporal integration, threshold, and refractory period are successfully demonstrated. Besides, the effects of frequency detuning on the nonlinear neuron-like dynamics are also explored, and the frequency detuning condition is revealed. The proposed hard-ware architecture plays a foundational role in constructing a large-scale coherent photonic spiking neural network. (c) 2022 Chinese Laser Press