Brain-inspired neuromorphic computing, featured by parallel computing, is considered as one of the most energy-efficient and time-saving architectures for massive data computing. However, photonic synapse, one of the key components, is still suffering high power consumption, potentially limiting its applications in artificial neural system. In this study, we present a BP/CdS heterostructure-based artificial photonic synapse with ultra-low power consumption. The device shows remarkable negative light response with maximum responsivity up to 4.1 × 10⁸ A W⁻¹ at V_D = 0.5 V and light power intensity of 0.16 μW cm⁻² (1.78 × 10⁸ A W⁻¹ on average), which further enables artificial synaptic applications with average power consumption as low as 4.78 fJ for each training process, representing the lowest among the reported results. Finally, a fully-connected optoelectronic neural network (FONN) is simulated with maximum image recognition accuracy up to 94.1%. This study provides new concept towards the designing of energy-efficient artificial photonic synapse and shows great potential in high-performance neuromorphic vision systems.

类脑神经形态计算以并行计算为特征，被认为是海量数据计算最节能、最省时的架构之一。然而，关键部件之一的光子突触仍然面临着高功耗的问题，这可能限制了其在人工神经系统中的应用。在这项研究中，我们提出了一种基于 BP/CdS 异质结构的超低功耗人工光子突触。该器件表现出显着的负光响应，在V _D = 0.5 V 时最大响应度高达 4.1 × 10 ⁸ AW ^-1 ，光功率强度为 0.16 μW cm ^-2 （平均 1.78 × 10 ⁸ AW ^-1 ），这进一步使得人工突触应用的每个训练过程平均功耗低至 4.78 fJ，是报告结果中最低的。最后，仿真了全连接光电神经网络（FONN），图像识别准确率最高可达94.1%。这项研究为节能人工光子突触的设计提供了新的概念，并在高性能神经形态视觉系统中显示出巨大的潜力。