Zhang Fanqing, Li Chunyang, Chen Zhicheng, Tan Haiqiu, Li Zhongyi, Lv Chengzhai, Xiao Shuai, Wu Lining, Zhao Jing
State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of Education, 100081, Beijing, China.
School of Mechatronical Engineering, Beijing Institute of Technology, 100081, Beijing, China.
Microsyst Nanoeng. 2025 Jan 13;11(1):5. doi: 10.1038/s41378-024-00859-2.
Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic devices array for the artificial visual neuromorphic system, within the size of 0.7 × 0.7 cm, which integrates sensing, memory, and processing functions. The highly uniform floating-gate synaptic transistors array were constructed by the wafer-scale grown monolayer molybdenum disulfide with Au nanoparticles (NPs) acting as the electrons capture layers. Various synaptic plasticity behaviors have been achieved owing to the switchable electronic storage performance. The excellent optical/electrical coordination capabilities were implemented by paralleled processing both the optical and electrical signals the synaptic array of 784 devices, enabling to realize the badges and letters writing and erasing process. Finally, the established artificial visual convolutional neural network (CNN) through optical/electrical signal modulation can reach the high digit recognition accuracy of 96.5%. Therefore, our results provide a feasible route for future large-scale integrated artificial visual neuromorphic system.
最近,受生物启发的智能人工视觉神经系统引起了极大的关注。然而,在追求大规模并行和高效的视觉记忆与识别方面仍存在重大障碍。在本研究中,我们展示了一种用于人工视觉神经形态系统的28×28突触器件阵列,其尺寸为0.7×0.7厘米,集成了传感、记忆和处理功能。通过晶圆级生长的单层二硫化钼构建了高度均匀的浮栅突触晶体管阵列,其中金纳米颗粒(NPs)作为电子捕获层。由于可切换的电子存储性能,实现了各种突触可塑性行为。通过对784个器件的突触阵列并行处理光信号和电信号,实现了优异的光/电协同能力,从而能够实现徽章和字母的写入和擦除过程。最后,通过光/电信号调制建立的人工视觉卷积神经网络(CNN)能够达到96.5%的高数字识别准确率。因此,我们的结果为未来大规模集成人工视觉神经形态系统提供了一条可行的途径。