ABSTRACT
The simulation of human brain neurons by synaptic devices could be an effective strategy to break through the notorious "von Neumann Bottleneck" and "Memory Wall". Herein, opto-electronic synapses based on layered hafnium disulfide (HfS2) transistors have been investigated. The basic functions of biological synapses are realized and optimized by modifying pulsed light conditions. Furthermore, 2 × 2 pixel imaging chips have also been developed. Two-pixel visual information is illuminated on diagonal pixels of the imaging array by applying light pulses (λ = 405 nm) with different pulse frequencies, mimicking short-term memory and long-term memory characteristics of the human vision system. In addition, an optically/electrically driven neuromorphic computation is demonstrated by machine learning to classify hand-written numbers with an accuracy of about 88.5%. This work will be an important step toward an artificial neural network comprising neuromorphic vision sensing and training functions.
