All-Optically Controlled Artificial Synapses Based on Light-Induced Adsorption and Desorption for Neuromorphic Vision
Jiran Liang, Xuan Yu, Jie Qiu, Ming Wang, Chuantong Cheng, Beiju Huang, Hengjie Zhang, Run Chen, Weihua Pei, Hongda Chen
Abstract
Artificial synapses with the capability of optical sensing and synaptic functions are fundamental components to construct neuromorphic visual systems. However, most reported artificial optical synapses require a combination of optical and electrical stimuli to achieve bidirectional synaptic conductance modulation, leading to an increase in the processing time and system complexity. Here, an all-optically controlled artificial synapse based on the graphene/titanium dioxide (TiO 2 ) quantum dot heterostructure is reported, whose conductance could be reversibly tuned by the effects of light-induced oxygen adsorption and desorption. Synaptic behaviors, such as excitatory and inhibitory, short-term and long-term plasticity, and learning–forgetting processes, are implemented using the device. An artificial neural network simulator based on the artificial synapse was used to train and recognize handwritten digits with a recognition rate of 92.2%. Furthermore, a 5 × 5 optical synaptic array that could simultaneously sense and memorize light stimuli was fabricated, mimicking the sensing and memory functionality of the retina. Such an all-optically controlled artificial synapse shows a promising prospect in the application of perception, learning, and memory tasks for future neuromorphic visual systems.