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A ferroelectric memristor with a capacitor-like structure for neuromorphic computing

This paper presents a ferroelectric memristor-based synaptic device with a capacitor-like structure and nonvolatile multilevel conductance. Conductance modulation can be achieved by manipulating the barrier height through polarization inversion and the mimicking of the important synaptic functions,...

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Bibliographic Details
Published in:Journal of physics. D, Applied physics Applied physics, 2023-03, Vol.56 (12), p.125302
Main Authors: Xie, Donggang, Sun, Qinchao, Geng, Qianyi, Su, Jie, Li, Qiang, Ni, Hao
Format: Article
Language:English
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Summary:This paper presents a ferroelectric memristor-based synaptic device with a capacitor-like structure and nonvolatile multilevel conductance. Conductance modulation can be achieved by manipulating the barrier height through polarization inversion and the mimicking of the important synaptic functions, such as long-term plasticity and spike-timing-dependent plasticity, have been implemented. By extracting device parameters, a simulated artificial neural network is constructed for image recognition, and it can achieve 91.2% recognition accuracy for Modified National Institute of Standards and Technology images. These results show the potential of this device in building brain-like computing systems and further enrich the research of ferroelectric materials in realizing artificial synapses.
ISSN:0022-3727
1361-6463
DOI:10.1088/1361-6463/acbb12