@article{Zhao2021, 
author = {Jie Zhao and Fang Liu and Qi Huang and Tongkang Lu and Meiqi Xi and Lianmao Peng and Xuelei Liang},
title = {Charge trap-based carbon nanotube transistor for synaptic function mimicking},
year = {2021},
journal = {Nano Research},
volume = {14},
number = {11},
pages = {4258-4263},
keywords = {carbon nanotube, charge trap, synaptic transistor, long-term memory},
url = {https://www.sciopen.com/article/10.1007/s12274-021-3611-9},
doi = {10.1007/s12274-021-3611-9},
abstract = {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.}
}