Tuning the synaptic behaviors of biocompatible synaptic transistor through ion-doping

被引:38
作者
Huang, Jinsong [1 ]
Chen, Jinwei [1 ]
Yu, Rengjian [1 ]
Zhou, Yilun [1 ]
Yang, Qian [1 ]
Li, Enlong [1 ]
Chen, Qizhen [1 ]
Chen, Huipeng [1 ,2 ]
Guo, Tailiang [1 ,2 ]
机构
[1] Fuzhou Univ, Natl & Local United Engn Lab Flat Panel Display T, Inst Optoelect Display, Fuzhou 350002, Peoples R China
[2] Fujian Sci & Technol Innovat Lab Optoelect Inform, Fuzhou 350100, Peoples R China
基金
中国国家自然科学基金;
关键词
Ion-doping; Artificial synapses; Pattern recognition; Neuromorphic computing;
D O I
10.1016/j.orgel.2020.106019
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Biodegradable and environmentally friendly artificial synapse devices are essential for the future development of neuromorphic computing. The emergence of synaptic transistors based on biocompatible polymer materials provides an ideal approach to achieve green electronics. However, modulating the synaptic properties in a wide range in a fixed biocompatible synaptic transistor is still challengeable, while it is vitally important for improving the adaptability of the synaptic device to achieve neuro-prosthetics in the future. Here, we reported the regulation of the synaptic behavior of biocompatible synaptic transistor through ion-doping, which allows to adjusting the response of the synaptic device according to a specific function. The ions doped into the insulating layer strengthen the formation of electric double layers (EDLs), which enables a remarkable regulation effect on post-synaptic current. Moreover, basic synaptic properties, including excitatory/inhibitory post-synaptic current (EPCS/IPSC), paired-pulse facilitation/depression (PPF/PPD), short-term/long-term memory (STM/LTM) are successfully demonstrated. In addition, high-pass and low-pass filtering functions are also implemented in a single synaptic device, indicating that the synapse attenuation can be effectively transformed according to the needs of the function. More importantly, this is the first work to demonstrate that the accuracy of pattern recognition of synaptic transistors, an important indicator of neuromorphic calculations, can be significantly improved via ion doping (as high as 75.96% relative to undoped devices of 41.68%). Our research provides a feasible strategy for precisely controlling synaptic behaviors, which has a profound impact on improving the adaptability of artificial synaptic devices in the field of neuromorphic computing.
引用
收藏
页数:8
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