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Reproducible Ultrathin Ferroelectric Domain Switching for High‐Performance Neuromorphic Computing
Author(s) -
Li Jiankun,
Ge Chen,
Du Jianyu,
Wang Can,
Yang Guozhen,
Jin Kuijuan
Publication year - 2020
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201905764
Subject(s) - neuromorphic engineering , memristor , mnist database , materials science , artificial neural network , computer science , ferroelectricity , synapse , domain (mathematical analysis) , artificial intelligence , electronic engineering , nanotechnology , optoelectronics , neuroscience , engineering , mathematical analysis , mathematics , dielectric , biology
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 (≈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.