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Highly Reliable Performance of Flexible Synaptic Devices Based on PVP-GO QD Nanocomposites Due to the Formation of Directional FilamentsHighly Reliable Performance of Flexible Synaptic Devices Based on PVP–GO QD Nanocomposites Due to the Formation of Directional Filaments

Other Titles
Highly Reliable Performance of Flexible Synaptic Devices Based on PVP–GO QD Nanocomposites Due to the Formation of Directional Filaments
Authors
Li, MingLi, MingjunAn, HaoqunAn, Jun SeopGu, PengyuKim, Dae HunPark, Kwan KyuKim, Tae Whan
Issue Date
Jan-2024
Publisher
American Chemical Society
Keywords
artificial synaptic device; flexible devices; graphene oxide quantum dots; neuromorphic computing; silver cluster-type filament
Citation
ACS Applied Materials & Interfaces, v.16, no.3, pp 3621 - 3630
Pages
10
Indexed
SCIE
SCOPUS
Journal Title
ACS Applied Materials & Interfaces
Volume
16
Number
3
Start Page
3621
End Page
3630
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/197375
DOI
10.1021/acsami.3c12615
ISSN
1944-8244
1944-8252
Abstract
The metallic conductive filament (CF) model, which serves as an important conduction mechanism for realizing synaptic functions in electronic devices, has gained recognition and is the subject of extensive research. However, the formation of CFs within the active layer is plagued by issues such as uncontrolled and random growth, which severely impacts the stability of the devices. Therefore, controlling the growth of CFs and improving the performance of the devices have become the focus of that research. Herein, a synaptic device based on polyvinylpyrrolidone (PVP)/graphene oxide quantum dot (GO QD) nanocomposites is proposed. Doping GO QDs in the PVP provides a large number of active centers for the reduction of silver ions, which allows, to a certain extent, the growth of CFs to be controlled. Because of this, the proposed device can simulate a variety of synaptic functions, including the transition from long-term potentiation to long-term depression, paired-pulse facilitation, post-tetanic potentiation, transition from short-term memory to long-term memory, and the behavior of the “learning experience”. Furthermore, after being bent repeatedly, the devices were still able to simulate multiple synaptic functions accurately. Finally, the devices achieved a high recognition accuracy rate of 89.39% in the learning and inference tests, producing clear digit classification results.
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서울 공과대학 > 서울 기계공학부 > 1. Journal Articles
서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles

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