Issue 26, 2021

Ultralow-power flexible transparent carbon nanotube synaptic transistors for emotional memory

Abstract

Emulating the biological behavior of the human brain with artificial neuromorphic devices is essential for the future development of human–machine interactive systems, bionic sensing systems and intelligent robotic systems. In this paper, artificial flexible transparent carbon nanotube synaptic transistors (F-CNT-STs) with signal transmission and emotional learning functions are realized by adopting the poly(vinyl alcohol) (PVA)/SiO2 proton-conducting electrolyte. Synaptic functions of biological synapses including excitatory and inhibitory behaviors are successfully emulated in the F-CNT-STs. Besides, synaptic plasticity such as spike-duration-dependent plasticity, spike-number-dependent plasticity, spike-amplitude-dependent plasticity, paired-pulse facilitation, short-term plasticity, and long-term plasticity have all been systematically characterized. Moreover, the F-CNT-STs also closely imitate the behavior of human brain learning and emotional memory functions. After 1000 bending cycles at a radius of 3 mm, both the transistor characteristics and the synaptic functions can still be implemented correctly, showing outstanding mechanical capability. The realized F-CNT-STs possess low operating voltage, quick response, and ultra-low power consumption, indicating their high potential to work in low-power biological systems and artificial intelligence systems. The flexible artificial synaptic transistor enables its potential to be generally applicable to various flexible wearable biological and intelligent applications.

Graphical abstract: Ultralow-power flexible transparent carbon nanotube synaptic transistors for emotional memory

Supplementary files

Article information

Article type
Paper
Submitted
03 Apr 2021
Accepted
16 May 2021
First published
17 May 2021

Nanoscale, 2021,13, 11360-11369

Ultralow-power flexible transparent carbon nanotube synaptic transistors for emotional memory

Y. Wang, W. Huang, Z. Zhang, L. Fan, Q. Huang, J. Wang, Y. Zhang and M. Zhang, Nanoscale, 2021, 13, 11360 DOI: 10.1039/D1NR02099D

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