Deep-UV-photo-excited synaptic Ga_(2)O_(3) nano-device with low-energy consumption for neuromorphic computing  

作　　者：Liubin Yang Xiushuo Gu Min Zhou Jianya Zhang Yonglin Huang Yukun Zhao 

机构地区：[1]College of Electronic and Optical Engineering&College of Flexible Electronics(Future Technology),Nanjing University of Posts and Telecommunications,Nanjing 210023,China [2]Key Lab of Nanodevices and Applications,Suzhou Institute of Nano-Tech and Nano-Bionics(SINANO),Chinese Academy of Sciences(CAS),Suzhou 215123,China [3]School of Nano-Tech and Nano-Bionics,University of Science and Technology of China,Hefei 230026,China [4]Key Laboratory of Efficient Low-carbon Energy Conversion and Utilization of Jiangsu Provincial Higher Education Institutions,School of Physical Science and Technology,Suzhou University of Science and Technology,Suzhou 215009,China

出　　处：《Journal of Semiconductors》2025年第2期88-96,共9页半导体学报(英文版)

基　　金：financially supported by the Key Research Program of Frontier Sciences,CAS (No. ZDBS-LYJSC034);National Natural Science Foundation of China (No.62174172);China Postdoctoral Science Foundation (Nos.2023TQ0238 and 2023M742560);Basic Research Pilot Project of Suzhou (No. SSD2024003);Jiangsu Key Disciplines of the Fourteenth Five-Year Plan (No. 2021135)

摘　　要：Synaptic nano-devices have powerful capabilities in logic,memory and learning,making them essential compo-nents for constructing brain-like neuromorphic computing systems.Here,we have successfully developed and demonstrated a synaptic nano-device based on Ga_(2)O_(3) nanowires with low energy consumption.Under 255 nm light stimulation,the biomimetic synaptic nano-device can stimulate various functionalities of biological synapses,including pulse facilitation,peak time-dependent plasticity and memory learning ability.It is found that the artificial synaptic device based on Ga_(2)O_(3) nanowires can achieve an excellent"learning-forgetting-relearning"functionality.The transition from short-term memory to long-term memory and retention of the memory level after the stepwise learning can attribute to the great relearning functionality of Ga_(2)O_(3) nanowires.Furthermore,the energy consumption of the synaptic nano-device can be lower than 2.39×10^(-11) J for a synaptic event.Moreover,our device demonstrates exceptional stability in long-term stimulation and storage.In the applica-tion of neural morphological computation,the accuracy of digit recognition exceeds 90%after 12 training sessions,indicating the strong learning capability of the cognitive system composed of this synaptic nano-device.Therefore,our work paves an effective way for advancing hardware-based neural morphological computation and artificial intelligence systems requiring low power consumption.

关 键 词：Ga_(2)O_(3)nanowires synaptic nano-device low energy consumption neural network 

分 类 号：TB383.1[一般工业技术—材料科学与工程] TN60[电子电信—电路与系统] TP183[自动化与计算机技术—控制理论与控制工程]