Photonic integrated spiking neuron chip based on a self-pulsating DFB laser with a saturable absorber

饱和吸收 偏压 激光器 光学 物理 光子学 啁啾声 光电子学 材料科学 电压 光纤激光器 量子力学
作者
Yuechun Shi,Shuiying Xiang,Xingxing Guo,Yahui Zhang,Hongji Wang,Dianzhuang Zheng,Yuna Zhang,Yanan Han,Yong Zhao,Xiaojun Zhu,Xiangfei Chen,Xun Li,Yue Hao
出处
期刊:Photonics Research [Optica Publishing Group]
卷期号:11 (8): 1382-1382 被引量:31
标识
DOI:10.1364/prj.485941
摘要

We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback (DFB) laser chip with an intracavity saturable absorber (SA). The DFB laser with an intracavity SA (DFB-SA) contains a gain region and an SA region. The gain region is designed and fabricated by the asymmetric equivalent π -phase shift based on the reconstruction-equivalent-chirp technique. Under properly injected current in the gain region and reversely biased voltage in the SA region, periodic self-pulsation was experimentally observed due to the Q -switching effect. The self-pulsation frequency increases with the increase of the bias current and is within the range of several gigahertz. When the bias current is below the self-pulsation threshold, neuronlike spiking responses appear when external optical stimulus pulses are injected. Experimental results show that the spike threshold, temporal integration, and refractory period can all be observed in the fabricated DFB-SA chip. To numerically verify the experimental findings, a time-dependent coupled-wave equation model was developed, which described the physics processes inside the gain and SA regions. The numerical results agree well with the experimental measurements. We further experimentally demonstrated that the weighted sum output can readily be encoded into the self-pulsation frequency of the DFB-SA neuron. We also benchmarked the handwritten digit classification task with a simple single-layer fully connected neural network. By using the experimentally measured dependence of the self-pulsation frequency on the bias current in the gain region as an activation function, we can achieve a recognition accuracy of 92.2%, which bridges the gap between the continuous valued artificial neural networks and spike-based neuromorphic networks. To the best of our knowledge, this is the first experimental demonstration of a photonic integrated spiking neuron based on a DFB-SA, which shows great potential to realizing large-scale multiwavelength photonic spiking neural network chips.

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