Optical synaptic devices with ultra-low power consumption for neuromorphic computing

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 x 10(8) A W-1 at V-D = 0.5 V and light power intensity of 0.16 mu W cm(-2) (1.78 x 10(8) A W-1 on average), which further enables artificial synaptic applications with average power consumption as low as 4.78 fi 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.

Automated summary

In this study, a low-power BP/CdS heterostructure-based artificial photonic synapse is proposed, and a fully-connected optoelectronic neural network with high image recognition accuracy is simulated. This research provides a new concept for designing energy-efficient artificial photonic synapses and demonstrates their great potential in high-performance neuromorphic vision systems.