Organic Optoelectronic Synaptic Device Based on Silver-Cluster Conduction Offers with Visual Learning Performance
- Authors
- Li, Ming; Kim, Hyung Soon; Li, Mingjun; An, Jun Seop; Park, Kwan Kyu; Park, Jinsub; Kim, Tae Whan
- Issue Date
- Jan-2026
- Publisher
- AMER CHEMICAL SOC
- Keywords
- optoelectronic modulation; synaptic devices; reliable operation; nitrogen-doped graphene quantum dot; Ag cluster-type filaments; neuromorphic computing
- Citation
- ACS APPLIED ELECTRONIC MATERIALS, v.8, no.2, pp 802 - 812
- Pages
- 11
- Indexed
- SCIE
SCOPUS
- Journal Title
- ACS APPLIED ELECTRONIC MATERIALS
- Volume
- 8
- Number
- 2
- Start Page
- 802
- End Page
- 812
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/211413
- DOI
- 10.1021/acsaelm.5c02052
- ISSN
- 2637-6113
2637-6113
- Abstract
- As an emerging and promising type of electronic devices, optoelectronic synaptic devices emulate the synaptic plasticity. Moreover, by the coordinated modulation of electrical and optical signals, this device can efficiently store and process information. Based on poly(vinylpyrrolidone): nitrogen-doped graphene oxide quantum dots (PVP:N-GO QD) nanocomposites, we fabricated an organic optoelectronic synaptic device and deeply explored their synaptic properties during optoelectronic modulation. Introducing nitrogen (N) into GO QDs through the hydrothermal method effectively enhances the n-pi* electronic transition, thereby achieving additional photoinduced conductance and providing an important physical basis for optoelectronic modulation. In addition, exposing the device to light at 365 nm significantly enhanced synaptic characteristics and achieved light-assisted regulation. In the Ag/PVP:N-GO-QD/ITO device structure, the top Ag electrode is used as the source of Ag ions, where Ag atoms are oxidized and migrated to the active layer under positive bias. By promoting the reduction of silver ions and optimizing the growth of conductive filaments, the device can stably simulate various biological synaptic behaviors. Finally, the pattern recognition accuracies of 90.62% (dark) and 91.11% (light) in learning and inference tests further demonstrate its broad prospects for applications in neuromorphic computing and artificial intelligence.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 기계공학부 > 1. Journal Articles
- 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.