Charge trap-based carbon nanotube transistor for synaptic function mimicking  

作　　者：Jie Zhao Fang Liu Qi Huang Tongkang Lu Meiqi Xi Lianmao Peng Xuelei Liang 

机构地区：[1]Center for Carbon-Based Electronics,Peking University,Beijing,100871,China [2]Key Laboratory for the Physics and Chemistry of Nanodevices,Department of Electronics,Peking University,Beijing,100871,China [3]Shanxi Institute for Carbon-Based Thin Film Electronics,Peking University(SICTFE-PKU),Taiyuan,030012,China [4]Taiyuan Laboratory for Carbon-Based Thin Film Electronics,Taiyuan,030012,China

出　　处：《Nano Research》2021年第11期4258-4263,共6页纳米研究（英文版）

基　　金：This work was supported by the National Key Research and Development Program (No. 2016YFA0201902);the National Natural Science Foundation of China (No. 51991341);the Open Research Fund of Key Laboratory of Space Utilization, and Chinese Academy of Sciences (No. LSU-KFJJ-2020-06).

摘　　要：Brain-inspired neuromorphic computing is expected for breaking through the bottleneck of the computer of conventional von Neumann architecture. To this end, the first step is to mimic functions of biological neurons and synapses by electronic devices. In this paper, synaptic transistors were fabricated by using carbon nanotube (CNT) thin films and interface charge trapping effects were confirmed to dominate the weight update of the synaptic transistors. Large synaptic weight update was realized due to the high sensitivity of the CNTs to the trapped charges in vicinity. Basic synaptic functions including inhibitory post-synaptic current (IPSC), excitatory post-synaptic current (EPSC), spike-timing-dependent plasticity (STDP), and paired-pulse facilitation (PPF) were mimicked. Large dynamic range of STDP (> 2,180) and low power consumption per spike (∼ 0.7 pJ) were achieved. By taking advantage of the long retention time of the trapped charges and uniform device-to-device performance, long-term image memory behavior of neural network was successfully imitated in a CNT synaptic transistor array.

关 键 词：carbon nanotube charge trap synaptic transistor long-term memory 

分 类 号：TB383[一般工业技术—材料科学与工程]