基于硅集成双段InP量子阱激光器的尖峰光子神经元
Spiking Photonic Neurons Based on Two-Section InP Quantum-Well Lasers Integrated on Silicon
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中文总结 AI 辅助
该研究通过实验探究硅集成双段InP量子阱激光器的尖峰动力学，实现多种类神经元工作模式，明确设计参数对尖峰特性的影响，为可扩展神经形态光子集成电路奠定基础。
中文摘要 AI 辅助
本工作实验研究了单片集成在硅上的双段InP量子阱激光器的尖峰动力学特性。通过适当调节电偏置条件，我们实现了多种类神经元工作模式，如积分-发放和共振-发放，凸显了该器件作为高速光子神经元的通用性。对激光器设计参数（包括腔长及增益/可饱和吸收体比例）的系统研究，阐明了它们对尖峰相关特性（如脉冲重复频率）的影响，并梳理出能实现稳定尖峰的工作参数空间。最后，这些发现为可扩展的神经形态光子集成电路铺平了道路，其中低损耗硅突触可与多功能激光神经元共存。
英文摘要
In this work we experimentally investigate the spiking dynamics of two-section InP quantum-well lasers monolithically integrated on silicon. By appropriately tuning the electrical bias conditions, we realize multiple neuronal-like operating regimes, such as integrate-and-fire and resonate-and-fire, highlighting the device's versatility as a high-speed photonic neuron. A systematic investigation of laser design parameters, including cavity length and gain/saturable absorber ratio, elucidates their impact on spiking-related properties (such as pulse repetition frequency) and traces the operational parameter space that unlocks stable spiking. Finally, these findings pave the way toward scalable neuromorphic photonic integrated circuits, where low-loss silicon synapses coexist with versatile laser neurons.
