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Organic Optoelectronic Synaptic Device Based on Silver-Cluster Conduction Offers with Visual Learning Performance
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Li, Ming | - |
| dc.contributor.author | Kim, Hyung Soon | - |
| dc.contributor.author | Li, Mingjun | - |
| dc.contributor.author | An, Jun Seop | - |
| dc.contributor.author | Park, Kwan Kyu | - |
| dc.contributor.author | Park, Jinsub | - |
| dc.contributor.author | Kim, Tae Whan | - |
| dc.date.accessioned | 2026-03-20T02:01:18Z | - |
| dc.date.available | 2026-03-20T02:01:18Z | - |
| dc.date.issued | 2026-01 | - |
| dc.identifier.issn | 2637-6113 | - |
| dc.identifier.issn | 2637-6113 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/211413 | - |
| dc.description.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. | - |
| dc.format.extent | 11 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | AMER CHEMICAL SOC | - |
| dc.title | Organic Optoelectronic Synaptic Device Based on Silver-Cluster Conduction Offers with Visual Learning Performance | - |
| dc.type | Article | - |
| dc.publisher.location | 미국 | - |
| dc.identifier.doi | 10.1021/acsaelm.5c02052 | - |
| dc.identifier.scopusid | 2-s2.0-105029533947 | - |
| dc.identifier.wosid | 001656071300001 | - |
| dc.identifier.bibliographicCitation | ACS APPLIED ELECTRONIC MATERIALS, v.8, no.2, pp 802 - 812 | - |
| dc.citation.title | ACS APPLIED ELECTRONIC MATERIALS | - |
| dc.citation.volume | 8 | - |
| dc.citation.number | 2 | - |
| dc.citation.startPage | 802 | - |
| dc.citation.endPage | 812 | - |
| dc.type.docType | Article; Early Access | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Engineering | - |
| dc.relation.journalResearchArea | Materials Science | - |
| dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
| dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
| dc.subject.keywordPlus | OXIDE QUANTUM DOTS | - |
| dc.subject.keywordAuthor | optoelectronic modulation | - |
| dc.subject.keywordAuthor | synaptic devices | - |
| dc.subject.keywordAuthor | reliable operation | - |
| dc.subject.keywordAuthor | nitrogen-doped graphene quantum dot | - |
| dc.subject.keywordAuthor | Ag cluster-type filaments | - |
| dc.subject.keywordAuthor | neuromorphic computing | - |
| dc.identifier.url | https://pubs.acs.org/doi/10.1021/acsaelm.5c02052 | - |
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