期刊
ADVANCED MATERIALS
卷 32, 期 7, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.201905764
关键词
electronic synapses; ferroelectric domain switching; ferroelectric tunnel junctions; neuromorphic computing; ultrathin films
类别
资金
- National Key R&D Program of China [2017YFA0303604, 2019YFA0308500]
- Youth Innovation Promotion Association of CAS [2018008]
- National Natural Science Foundation of China [11674385, 11404380, 11721404, 11874412]
- Key Research Program of Frontier Sciences CAS [QYZDJSSW-SLH020]
- Open Research Fund of Key Laboratory of Polar Materials and Devices Ministry of Education [CLPMKFKT201902]
Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (approximate to 200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据