We propose and numerically demonstrate a photonic computing primitive designed for integrated spiking neural networks (SNNs) based on add-drop ring microresonators (ADRMRs) and electrically reconfigurable phase-change material (PCM) photonic switches. In this neuromorphic system, the passive silicon-based ADRMR, equipped with a power-tunable auxiliary light, effectively demonstrates nonlinearity-induced dual neural dynamics encompassing spiking response and synaptic plasticity that can generate single-wavelength optical neural spikes with synaptic weight. By cascading these ADRMRs with different resonant wavelengths, weighted multiple-wavelength spikes can be feasibly output from the ADRMR-based hardware arrays when external wavelength-addressable optical pulses are injected; subsequently, the cumulative power of these weighted output spikes is utilized to ascertain the activation status of the reconfigurable PCM photonic switches. Moreover, the reconfigurable mechanism driving the interconversion of the PCMs between the resonant-bonded crystalline states and the covalent-bonded amorphous states is achieved through precise thermal modulation. Drawing from the thermal properties, an innovative thermodynamic leaky integrate-and-firing (TLIF) neuron system is proposed. With the TLIF neuron system as the fundamental unit, a fully connected SNN is constructed to complete a classic deep learning task: the recognition of handwritten digit patterns. The simulation results reveal that the exemplary SNN can effectively recognize 10 numbers directly in the optical domain by employing the surrogate gradient algorithm. The theoretical verification of our architecture paves a whole new path for integrated photonic SNNs, with the potential to advance the field of neuromorphic photonic systems and enable more efficient spiking information processing.

中文翻译：

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基于分插环微谐振器和电可重构相变材料光子开关的片上尖峰神经网络

我们提出并数字演示了一种光子计算原语，该原语设计用于基于分插环微谐振器（ADRMR）和电可重构相变材料（PCM）光子开关的集成尖峰神经网络（SNN）。在这个神经形态系统中，基于硅的无源 ADRMR 配备了功率可调的辅助光，有效地演示了非线性引起的双神经动力学，包括尖峰响应和突触可塑性，可以生成具有突触重量的单波长光学神经尖峰。通过级联这些具有不同谐振波长的ADRMR，当注入外部波长可寻址光脉冲时，可以从基于ADRMR的硬件阵列输出加权的多波长尖峰；随后，这些加权输出尖峰的累积功率被用来确定可重构PCM光子开关的激活状态。此外，驱动PCM在谐振键合晶态和共价键合非晶态之间相互转换的可重构机制是通过精确的热调制实现的。根据热特性，提出了一种创新的热力学泄漏积分激发（TLIF）神经元系统。以TLIF神经元系统为基本单元，构建全连接的SNN来完成经典的深度学习任务：手写数字模式的识别。仿真结果表明，示例性 SNN 通过采用代理梯度算法可以有效地直接在光域中识别 10 个数字。我们的架构的理论验证为集成光子 SNN 铺平了一条全新的道路，有可能推动神经形态光子系统领域的发展，并实现更有效的尖峰信息处理。