神经形态工程学
材料科学
纳米复合材料
记忆电阻器
纳米技术
聚乙烯醇
纳米颗粒
突触
神经促进
制作
电阻式触摸屏
聚合物
突触可塑性
人工神经网络
光电子学
电子工程
计算机科学
人工智能
复合材料
神经科学
受体
病理
医学
生物化学
化学
替代医学
工程类
计算机视觉
生物
作者
Renu Kumari,J. Gellanki,Somnath S. Kundale,Ruhan E. Ustad,Tukaram D. Dongale,Ying Fu,Håkan Pettersson,Sandeep Kumar
出处
期刊:APL Materials
[American Institute of Physics]
日期:2023-10-01
卷期号:11 (10)
被引量:5
摘要
Computational efficiency is significantly enhanced using artificial neural network-based computing. A two-terminal memristive device is a powerful electronic device that can mimic the behavior of a biological synapse in addition to storing information and performing logic operations. This work focuses on the fabrication of a memristive device that utilizes a resistive switching layer composed of polyvinyl alcohol infused with ZnO nanoparticles. By incorporating ZnO nanoparticles into the polymer film, the fabricated memristive devices exhibit functionalities that closely resemble those of biological synapses, including short-term and long-term plasticity, paired-pulse facilitation, and spike time-dependent plasticity. These findings establish the ZnO nanoparticle-polymer nanocomposite as a highly promising material for future neuromorphic systems.
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