Photonic Spiking Neural Network based on DML and DFB-SA Laser Chip for Pattern Classification

Neuromorphic photonic computing based on spiking dynamics holds significant promise for next-generation AI accelerators, enabling high-speed, low-latency, and low-energy computing. However, the architecture of neuromorphic photonic systems is severely constrained by large-scale discrete devices. In this work, we propose a photonic spiking neural network (PSNN) architecture utilizing a directly modulated laser and a distributed feedback laser with a saturable absorber (DML-DFB-SA). The distributed feedback laser with a saturable absorber (DFB-SA) functions as a photonic spiking neuron, exhibiting nonlinear neuron-like dynamics. Specifically, we replace the conventional optical source and external modulator with a single directly modulated laser (DML), which simultaneously serves as the optical carrier and performs electro-optic conversion. This integration results in enhanced system compactness and reduced power consumption. Experimental results show that the energy efficiency of the DML-DFB-SA system reaches 0.625 pJ/MAC, representing a significant improvement in energy efficiency. Besides, since both DML and DFB-SA laser chips can be fabricated on an Indium Phosphide (InP) substrate, large-scale integration of photonic spiking neural networks (PSNNs) becomes practical. Moreover, the DML-DFB-SA system exhibits consistent robustness against the chirp effect of DML in short-distance transmissions, which makes it a promising candidate for PSNN applications. To validate the DML-DFB-SA's operational principle, we utilize a time-multiplexed spike coding scheme, enabling a single neuron to emulate the functionality of ten neurons. Experimental evaluations demonstrate a recognition accuracy of 94% on the MNIST dataset. The proposed system and approach provide a promising framework for developing low-energy, large-scale integrated PSNN chips.