Organic Synaptic Transistors Based on a Hybrid Trapping Layer for Neuromorphic Computing
Shuqiong Lan, Xiaoyan Wang, Rengjian Yu, Changjie Zhou, Minshuai Wang, Xiaomei Cai
Abstract
Traditional Von-Neumann computers would not meet the needs of storage and processing a large amount of information in the era of artificial intelligence owing to the separated storage and processing unit. Inspired by the human brain, various electronic devices have been developed for neuromorphic computing to conquer the von Neumann bottleneck. Organic synaptic transistors have attracted increasing interest due to their advantages of low cost, flexibility and ease of solution fabrication. However, most synaptic transistors based on the charge trapping principle use a single material, which limits the adjustment of synaptic plasticity. Here, a novel synaptic device based on a hybrid trapping layer was proposed and investigated. The device with a hybrid trapping layer exhibits a larger memory window than the device with a trapping layer based on single material, indicating that the device with hybrid trapping has a larger trapping capability. Moreover, our synaptic device was utilized to successfully simulate typical synaptic properties: excitatory postsynaptic current, inhibitory postsynaptic current, paired-pulse facilitation, paired-pulse depression and the transition from short-term plasticity to long-term plasticity. Furthermore, an artificial neural network was simulated and exhibited a high recognition accuracy. Therefore, the proposed device could promote the development of highly efficient neuromorphic computing systems.