Fang Junlin, Tang Zhenhua, Lai Xi-Cai, Qiu Fan, Jiang Yan-Ping, Liu Qiu-Xiang, Tang Xin-Gui, Sun Qi-Jun, Zhou Yi-Chun, Fan Jing-Min, Gao Ju
School of Physics and Optoelectric Engineering, Guangzhou Higher Education Mega Center, Guangdong University of Technology, Guangzhou 510006, P. R. China.
School of Advanced Materials and Nanotechnology, Xidian University, Xian 710126, China.
ACS Appl Mater Interfaces. 2024 Jun 19;16(24):31348-31362. doi: 10.1021/acsami.4c05316. Epub 2024 Jun 4.
Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>10). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.
当今的计算系统为满足信息处理的巨大需求，推动了受大脑启发的神经形态系统的发展。然而，在大多数受大脑启发的神经形态系统中，能够同时通过光信号和电信号调节电导率的光电器件相对较少。在这项工作中，作为光电器件的金/ MXene / Y:HfO / FTO铁电忆阻器在不同电压下表现出数字和模拟电阻切换（RS）行为，具有良好的切换比（> 10）。在光电条件下，优化的权重更新参数和增强算法在卷积神经网络中实现了97.1%的识别准确率。建立了专门为光电输入设计的新型逻辑门电路。此外，该器件整合了相对湿度的影响，开发了一种具有否决权的创新三人投票机制。这些结果为将光电器件人工突触与基于逻辑的计算设备集成提供了一种可行的方法。
