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Artificial synaptic characteristics of PVA:ZnO nanocomposite memristive devices
Central University of Rajasthan, Ajmer, India.
Hansraj College, New Delhi, India.
Shivaji University, Kolhapur, India.ORCID iD: 0000-0002-5162-8143
Shivaji University, Kolhapur, India.
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2023 (English)In: APL Materials, E-ISSN 2166-532X, Vol. 11, no 10, article id 101124Article in journal (Refereed) Published
Abstract [en]

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. © 2023 Author(s).

Place, publisher, year, edition, pages
Melville: American Institute of Physics (AIP), 2023. Vol. 11, no 10, article id 101124
Keywords [en]
Activation energy, Computation theory, Computational efficiency, II-VI semiconductors, Memristors, Metal nanoparticles, Neural networks, Polymer films, Semiconducting films
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-51997DOI: 10.1063/5.0165205Scopus ID: 2-s2.0-85175313733OAI: oai:DiVA.org:hh-51997DiVA, id: diva2:1811780
Available from: 2023-11-14 Created: 2023-11-14 Last updated: 2024-03-11Bibliographically approved

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Fu, YingPettersson, Håkan

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Kundale, Somnath S.Dongale, Tukaram D.Fu, YingPettersson, HåkanKumar, Sandeep
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