Zhang Jianya, Li Jiamin, Xu Rui, Wang Yudie, Wang Jiawen, Wang Tianxiang, Zhao Yukun
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.
Division of Nano-Devices Research, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), Suzhou, 215123, China.
Small Methods. 2025 Mar;9(3):e2400989. doi: 10.1002/smtd.202400989. Epub 2024 Sep 30.
In recent years, the rapid development of brain-inspired neuromorphic systems has created an imperative demand for artificial photonic synapses that operate with low power consumption. In this study, a self-driven memristor synapse based on gallium oxide (GaO) nanowires is proposed and demonstrated successfully. This memristor synapse is capable of emulating a range of functionalities of biological synapses when exposed to 255 nm light stimulation. These functionalities encompass peak time-dependent plasticity, pulse facilitation, and memory learning capabilities. It exhibits an ultrahigh paired-pulse facilitation index of 158, indicating exceptional learning performance. The transition from short-term memory to long-term memory can be attributed to the remarkable relearning capabilities. Furthermore, the potential applications of the memristor synapse is showcased through the successful manipulation of a humanoid intelligent robot. Upon establishing artificial intelligence (AI) systems, the control commands originating from the synaptic device can drive the humanoid robot to perform various actions. Based on the memristor synapses, the autonomous feedback system of the humanoid robot facilitates a good collaboration between robotic actions and bio-inspired light perception. Therefore, this research opens up an effective way to advance the development of neuromorphic computing technologies, AI systems, and intelligent robots that demand ultra-low energy consumption.
近年来，受大脑启发的神经形态系统的快速发展，对低功耗运行的人工光子突触产生了迫切需求。在本研究中，成功提出并展示了一种基于氧化镓（GaO）纳米线的自驱动忆阻器突触。当暴露于255纳米光刺激时，这种忆阻器突触能够模拟生物突触的一系列功能。这些功能包括峰值时间依赖性可塑性、脉冲易化和记忆学习能力。它表现出158的超高双脉冲易化指数，表明具有卓越的学习性能。从短期记忆到长期记忆的转变可归因于显著的再学习能力。此外，通过成功操纵人形智能机器人展示了忆阻器突触的潜在应用。在建立人工智能（AI）系统后，源自突触装置的控制命令可驱动人形机器人执行各种动作。基于忆阻器突触，人形机器人的自主反馈系统促进了机器人动作与生物启发式光感知之间的良好协作。因此，本研究为推进神经形态计算技术、人工智能系统和对超低能耗有需求的智能机器人的发展开辟了一条有效途径。
