Harnessing a silicon carbide nanowire photoelectric synaptic device for novel visual adaptation spiking neural networks

Abstract

Visual adaptation is essential for optimizing the image quality and sensitivity of artificial vision systems in real-world lighting conditions. However, additional modules, leading to time delays and potentially increasing power consumption, are needed for traditional artificial vision systems to implement visual adaptation. Here, an ITO/PMMA/SiC-NWs/ITO photoelectric synaptic device is developed for compact artificial vision systems with the visual adaption function. The theoretical calculation and experimental results demonstrated that the heating effect, induced by the increment light intensity, leads to the photoelectric synaptic device enabling the visual adaption function. Additionally, a visual adaptation artificial neuron (VAAN) circuit was implemented by incorporating the photoelectric synaptic device into a LIF neuron circuit. The output frequency of this VAAN circuit initially increases and then decreases with gradual light intensification, reflecting the dynamic process of visual adaptation. Furthermore, a visual adaptation spiking neural network (VASNN) was constructed to evaluate the photoelectric synaptic device based visual system for perception tasks. The results indicate that, in the task of traffic sign detection under extreme weather conditions, an accuracy of 97% was achieved (which is approximately 12% higher than that without a visual adaptation function). Our research provides a biologically plausible hardware solution for visual adaptation in neuromorphic computing.