记忆电阻器
材料科学
铁电性
神经形态工程学
光电子学
电介质
纳米技术
电子工程
计算机科学
人工神经网络
人工智能
工程类
作者
Xiaobing Yan,Hongwei Yan,Gongjie Liu,Jianhui Zhao,Zhen Zhao,Hong Wang,Haidong He,Mengmeng Hao,Zhaohua Li,Lei Wang,Wei Wang,Zixuan Jian,Jiaxin Li,Jingsheng Chen
出处
期刊:Nano Research
[Springer Nature]
日期:2022-07-28
卷期号:15 (10): 9654-9662
被引量:33
标识
DOI:10.1007/s12274-022-4604-z
摘要
Ferroelectric memristors, as one of the most potential non-volatile memory to meet the rapid development of the artificial intelligence era, have the comprehensive function of simulating brain storage and calculation. However, due to the high dielectric loss of traditional ferroelectric materials, the durability of ferroelectric memristors and Si based integration have a great challenge. Here, we report a silicon-based epitaxial ferroelectric memristor based on self-assembled vertically aligned nanocomposites BaTiO3(BTO)-CeO2 films. The BTO-CeO2 memristors exhibit a stable resistance switching behavior at a high temperature of 100 °C due to higher Curie temperatures of BTO-CeO2 films with in-plane compressive strain. And the endurance of the device can reach the order of magnitude of 1 × 106 times. More importantly, the device has excellent functions for simulating artificial synaptic behavior, including excitatory post-synaptic current, paired-pulse facilitation, paired-pulse depression, spike-time-dependent plasticity, and short and long-term plasticity. Digits recognition ability of the memristor devices is evaluated though a single-layer perceptron model, in which recognition accuracy of digital can reach 86.78% after 20 training iterations. These results provide new way for epitaxial composite ferroelectric films as memristor medium with high temperature intolerance and better durability integrated on silicon.
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