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

Author:

Shi Yuechun1ORCID,Xiang Shuiying,Guo Xingxing,Zhang Yahui,Wang Hongji2,Zheng Dianzhuang1,Zhang Yuna,Han Yanan,Zhao Yong3,Zhu Xiaojun4ORCID,Chen Xiangfei2,Li Xun5ORCID,Hao Yue

Affiliation:

1. Yongjiang Laboratory

2. Nanjing University

3. Jiangnan University

4. Nantong University

5. McMaster University

Abstract

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.

Funder

National Key Research and Development Program of China

National Outstanding Youth Science Fund of National Natural Science Foundation of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

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