Shen Weikang, Wang Pan, Wei Guodong, Yuan Shuai, Chen Mi, Su Ying, Xu Bingshe, Li Guoqiang
Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China.
Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan, Shanxi, 030024, P. R. China.
Small. 2024 Aug;20(34):e2400458. doi: 10.1002/smll.202400458. Epub 2024 Apr 12.
1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200 °C. Based on the advanced functions under light stimulus, the constructed 5 × 3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200 °C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.
一维纳米线网络在结构、信息传递和计算方面与生物神经网络有相似之处，已成为神经形态系统的一个有前途的平台。基于一维纳米线网络的类脑结构，神经形态突触器件可以克服冯·诺依曼瓶颈，以高信息处理速率和低功耗实现智能高效的传感和计算功能。在此，开发了基于SiC@NiO核壳纳米线网络光电忆阻器（NNOM）的高温神经形态突触器件。实验结果表明，NNOM在电刺激和光刺激下均能实现突触的短期/长期可塑性和调制可塑性，并且在室温和200℃的光刺激下均表现出短期/长期记忆和“学习-遗忘-再学习”等先进功能。基于光刺激下的先进功能，构建的5×3光电突触阵列器件在高达200℃时表现出稳定的视觉记忆功能，可用于开发人工视觉系统。此外，当受到多种电子或光刺激时，NNOM有效地复制了巴甫洛夫经典条件反射的原理，实现了视觉异源突触功能并优化了神经网络。总体而言，这些神经形态突触器件具有丰富的突触特性和高温热稳定性，为推进神经形态计算和视觉系统提供了一条有前途的途径。
