Artificial synaptic characteristics of PVA:ZnO nanocomposite memristive devices
(2023) In APL Materials 11(10).- Abstract
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... (More)
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.
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- author
- Kumari, Renu ; Gellanki, Jnaneswari ; Kundale, Somnath S. ; Ustad, Ruhan E. ; Dongale, Tukaram D. ; Fu, Ying ; Pettersson, Håkan LU and Kumar, Sandeep LU
- organization
- publishing date
- 2023-10
- type
- Contribution to journal
- publication status
- published
- subject
- in
- APL Materials
- volume
- 11
- issue
- 10
- article number
- 101124
- publisher
- American Institute of Physics (AIP)
- external identifiers
-
- scopus:85175313733
- ISSN
- 2166-532X
- DOI
- 10.1063/5.0165205
- language
- English
- LU publication?
- yes
- id
- 7d1e8dde-417c-4836-8756-b36f48251d7b
- date added to LUP
- 2023-12-12 13:58:54
- date last changed
- 2024-02-09 11:01:27
@article{7d1e8dde-417c-4836-8756-b36f48251d7b, abstract = {{<p>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.</p>}}, author = {{Kumari, Renu and Gellanki, Jnaneswari and Kundale, Somnath S. and Ustad, Ruhan E. and Dongale, Tukaram D. and Fu, Ying and Pettersson, Håkan and Kumar, Sandeep}}, issn = {{2166-532X}}, language = {{eng}}, number = {{10}}, publisher = {{American Institute of Physics (AIP)}}, series = {{APL Materials}}, title = {{Artificial synaptic characteristics of PVA:ZnO nanocomposite memristive devices}}, url = {{http://dx.doi.org/10.1063/5.0165205}}, doi = {{10.1063/5.0165205}}, volume = {{11}}, year = {{2023}}, }