Chuanyu Fu: School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, China. ORCID
Mengjiao Pei: School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, China.
Hangyuan Cui: School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, China.
Shuo Ke: School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, China.
Yixin Zhu: School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, China.
Changjin Wan: National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China. ORCID
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.
