Fu Chuanyu, Pei Mengjiao, Cui Hangyuan, Ke Shuo, Zhu Yixin, Wan Changjin, Wan Qing
School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, China.
Yong jiang Laboratory (Y-LAB), Ningbo, Zhejiang 315202, China.
J Phys Chem Lett. 2024 Sep 26;15(38):9585-9592. doi: 10.1021/acs.jpclett.4c02234. Epub 2024 Sep 13.
Nanofiber neuromorphic transistors are regarded as promising candidates for mimicking brain-like learning and advancing high-performance computing. Composite nanofibers (CNFs) typically exhibit enhanced optoelectronic and mechanical properties. In this study, indium-gallium-zinc oxide (IGZO)/polyvinylpyrrolidone (PVP) CNFs were synthesized, and the neuromorphic transistor was integrated on both rigid and flexible substrates. The learning behavior, characterized by the transition from short-term plasticity (STP) to long-term plasticity, was achieved through photoelectric stimulation of the rigid neuromorphic transistor. The nonlinear STP was simulated by the flexible neuromorphic transistor through electrical pulses, matching effectively with a reservoir computing (RC) system. Hand gesture recognition with little energy consumption (49 pJ per reservoir state) and a maximum accuracy of 92.86% has been achieved by the RC system, proving the substantial potential of the IGZO/PVP CNF neuromorphic transistor for wearable intelligent processing tasks.
纳米纤维神经形态晶体管被视为模仿类脑学习和推动高性能计算的有前途的候选者。复合纳米纤维（CNFs）通常表现出增强的光电和机械性能。在本研究中，合成了铟镓锌氧化物（IGZO）/聚乙烯吡咯烷酮（PVP）复合纳米纤维，并将神经形态晶体管集成在刚性和柔性基板上。通过对刚性神经形态晶体管进行光电刺激，实现了以从短期可塑性（STP）到长期可塑性的转变为特征的学习行为。柔性神经形态晶体管通过电脉冲模拟了非线性STP，有效地与储层计算（RC）系统相匹配。RC系统实现了低能耗（每个储层状态49皮焦耳）和最高92.86%的准确率的手势识别，证明了IGZO/PVP复合纳米纤维神经形态晶体管在可穿戴智能处理任务中的巨大潜力。
