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, Ming; Li, Mingjun; An, Haoqun; An, Jun Seop; Gu, Pengyu; Kim, Dae Hun; Park, Kwan Kyu; Kim, 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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