Plastic Photonic Synapse Based on VCSOA for Self-Learning in Photonic Spiking Neural Network

Abstract

We propose and demonstrate a plastic photonic synapse to achieve all-optical synaptic plasticity based on the potentiated and depressed dynamics in commercially available vertical-cavity semiconductor optical amplifier (VCSOA). The effects of presynaptic and postsynaptic spike powers, bias current, initial wavelength detuning between presynaptic spike and VCSOA on the potentiated and depressed time windows are investigated. The numerical results show that all-optical synaptic plasticity can be achieved successfully based on VCSOA with delayed self-feedback loop. The proof-of-concept experimental results show that plastic photonic synapse can be achieved based on the VCSOA with subsequent iterations. Besides, the curve of VCSOA gain in the potentiated and depressed time windows is similar to that of spike timing dependent plasticity. Furthermore, all-optical unsupervised self-learning and recognition of first spike timing of an input pattern are realized numerically in the photonic neural network including two proposed plastic photonic synapses. The plastic photonic synapse based on a VCSOA is interesting and valuable for the self-learning in all-optical neural networks. Moreover, the proposed plastic photonic synapse offers great potential for dense, high-performance neuromorphic photonic systems.