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Integration of synaptic phototransistors and quantum dot light-emitting diodes for visualization and recognition of UV patterns

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dc.contributor.authorHyojin Seung-
dc.contributor.authorChangsoon Choi-
dc.contributor.authorDong Chan Kim-
dc.contributor.authorJi Su Kim-
dc.contributor.authorJeong Hyun Kim-
dc.contributor.authorJunhee Kim-
dc.contributor.authorPark, Soo Ik-
dc.contributor.authorLim, Jung Ah-
dc.contributor.authorYang, Jiwoong-
dc.contributor.authorMoon Kee Choi-
dc.contributor.authorTaeghwan Hyeon-
dc.contributor.authorDae-Hyeong Kim-
dc.date.accessioned2023-01-26T02:41:42Z-
dc.date.available2023-01-26T02:41:42Z-
dc.date.created2022-10-29-
dc.date.issued2022-10-
dc.identifier.issn2375-2548-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/12682-
dc.description.abstractSynaptic photodetectors exhibit photon-triggered synaptic plasticity, which thus can improve the image recognition rate by enhancing the image contrast. However, still, the visualization and recognition of invisible ultraviolet (UV) patterns are challenging, owing to intense background noise. Here, inspired by all-or-none potentiation of synapse, we develop an integrated device of synaptic phototransistors (SPTrs) and quantum dot light-emitting diodes (QLEDs), facilitating noise reduction and visualization of UV patterns through on-device preprocessing. The SPTrs convert noisy UV inputs into a weighted photocurrent, which is applied to the QLEDs as a voltage input through an external current-voltage-converting circuit. The threshold switching characteristics of the QLEDs result in amplified current and visible illumination by the suprathreshold input voltage or nearly zero current and no visible illumination by the input voltage below the threshold. The preprocessing of image data with the SPTr-QLED can amplify the image contrast, which is helpful for high-accuracy image recognition.-
dc.language영어-
dc.publisherNLM (Medline)-
dc.titleIntegration of synaptic phototransistors and quantum dot light-emitting diodes for visualization and recognition of UV patterns-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000882137600001-
dc.identifier.scopusid2-s2.0-85139751254-
dc.identifier.rimsid79059-
dc.contributor.affiliatedAuthorHyojin Seung-
dc.contributor.affiliatedAuthorChangsoon Choi-
dc.contributor.affiliatedAuthorDong Chan Kim-
dc.contributor.affiliatedAuthorJi Su Kim-
dc.contributor.affiliatedAuthorJeong Hyun Kim-
dc.contributor.affiliatedAuthorJunhee Kim-
dc.contributor.affiliatedAuthorMoon Kee Choi-
dc.contributor.affiliatedAuthorTaeghwan Hyeon-
dc.contributor.affiliatedAuthorDae-Hyeong Kim-
dc.identifier.doi10.1126/sciadv.abq3101-
dc.identifier.bibliographicCitationScience advances, v.8, no.41-
dc.relation.isPartOfScience advances-
dc.citation.titleScience advances-
dc.citation.volume8-
dc.citation.number41-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.relation.journalWebOfScienceCategoryMultidisciplinary Sciences-
dc.subject.keywordPlusPLASTICITY-
dc.subject.keywordPlusSENSOR-
dc.subject.keywordPlusARRAY-
Appears in Collections:
Center for Nanoparticle Research(나노입자 연구단) > 1. Journal Papers (저널논문)
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