Optoelectronic Properties of Printed Photogating Carbon Nanotube Thin Film Transistors and Their Application for Light-Stimulated Neuromorphic Devices
Optoelectronic Properties of Printed Photogating Carbon Nanotube Thin Film Transistors and Their Application for Light-Stimulated Neuromorphic Devices
复制标题
印刷光电门控碳纳米管薄膜晶体管的光电特性及其在光刺激神经形态器件中的应用
DOI：
10.1021/acsami.9b02086
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发表时间：
2019-03-27
影响因子：
9.5
通讯作者：
Cui, Zheng
中科院分区：
文献类型：
--
作者：
Shao, Lin;Wang, Hailu;Cui, Zheng
基于电子/离子混合器件的人工突触/神经元已经引起了广泛的关注，因为它有可能克服神经形态计算范式的冯诺依曼瓶颈。在这里，我们报告了一种新的光神经元器件的基础上印刷photogating单壁碳纳米管（SWCNT）薄膜晶体管（TFT）使用轻n掺杂的硅作为栅电极。印刷的单壁碳纳米管薄膜晶体管的漏极电流可以逐渐增加到3000倍以上的初始值后，脉冲与光刺激，和电信号可以保持超过10分钟。这些特性类似于大脑启发的神经形态系统的学习和记忆功能。本文详细介绍了光刺激神经形态器件的工作机理。重要的突触特性，如低通滤波特性和非易失性存储能力，成功地模拟在印刷光刺激人工突触。这表明，印刷的单壁碳纳米管TFT光神经形态器件可以作为非易失性存储单元，并执行光神经形态计算，这表现出潜在的未来神经形态系统的应用。
Artificial synapses/neurons based on electronic/ionic hybrid devices have attracted wide attention for brain-inspired neuromorphic systems since it is possible to overcome the von Neumann bottleneck of the neuromorphic computing paradigm. Here, we report a novel photo-neuromorphic device based on printed photogating single-walled carbon nanotube (SWCNT) thin film transistors (TFTs) using lightly n-doped Si as the gate electrode. The drain currents of the printed SWCNT TFTs can gradually increase to over 3000 times of their starting value after being pulsed with light stimulation, and the electrical signals can maintain for over 10 min. These characteristics are similar to the learning and memory functions of brain-inspired neuromorphic systems. The working mechanism of the light-stimulated neuromorphic devices is investigated and described here in detail. Important synaptic characteristics, such as low-pass filtering characteristics and nonvolatile memory ability, are successfully emulated in the printed light-stimulated artificial synapses. It demonstrates that the printed SWCNT TFT photoneuromorphic devices can act as the nonvolatile memory units and perform photoneuromorphic computing, which exhibits potential for future neuromorphic system applications.