Literature DB >> 34096562

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

Yarong Wang1, Weihong Huang1, Ziwei Zhang1, Lingchong Fan1, Qiuyue Huang1, Jiaxin Wang1, Yiming Zhang1, Min Zhang1.   

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.

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Year:  2021        PMID: 34096562     DOI: 10.1039/d1nr02099d

Source DB:  PubMed          Journal:  Nanoscale        ISSN: 2040-3364            Impact factor:   7.790


  1 in total

1.  A flexible dual-gate hetero-synaptic transistor for spatiotemporal information processing.

Authors:  Xuerong Liu; Cui Sun; Zhecheng Guo; Yuejun Zhang; Zheng Zhang; Jie Shang; Zhicheng Zhong; Xiaojian Zhu; Xue Yu; Run-Wei Li
Journal:  Nanoscale Adv       Date:  2022-04-20
  1 in total

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