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dc.contributor.authorHyejin Lee-
dc.contributor.authorcho, seong won-
dc.contributor.authorKim, Seon Jeong-
dc.contributor.authorJaesang Lee-
dc.contributor.authorKim, Keun Su-
dc.contributor.authorKim, In ho-
dc.contributor.authorPARK, JONG KEUK-
dc.contributor.authorKwak, Joon Young-
dc.contributor.authorKim, Jae wook-
dc.contributor.authorPark, Jongkil-
dc.contributor.authorJeong, Yeon Joo-
dc.contributor.authorHwang, Gyu Weon-
dc.contributor.authorLEE, KYEONG SEOK-
dc.contributor.authorIelmini, Daniele-
dc.contributor.authorLee, Suyoun-
dc.date.accessioned2024-01-12T03:32:52Z-
dc.date.available2024-01-12T03:32:52Z-
dc.date.created2022-02-25-
dc.date.issued2022-01-
dc.identifier.issn1530-6984-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/76811-
dc.description.abstractInspired by information processing in biological systems, sensor-combined edge-computing systems attract attention requesting artificial sensory neurons as essential ingredients. Here, we introduce a simple and versatile structure of artificial sensory neurons based on a novel three-terminal Ovonic threshold switch (3T-OTS), which features an electrically controllable threshold voltage (Vth). Combined with a sensor driving an output voltage, this 3T-OTS generates spikes with a frequency depending on an external stimulus. As a proof of concept, we have built an artificial retinal ganglion cell (RGC) by combining a 3T-OTS and a photodiode. Furthermore, this artificial RGC is combined with the reservoir-computing technique to perform a classification of chest X-ray images for normal, viral pneumonia, and COVID-19 infections, releasing the recognition accuracy of about 86.5%. These results indicate that the 3T-OTS is highly promising for applications in neuromorphic sensory systems, providing a building block for energy-efficient in-sensor computing devices.-
dc.languageEnglish-
dc.publisherAmerican Chemical Society-
dc.titleThree-Terminal Ovonic Threshold Switch (3T-OTS) with Tunable Threshold Voltage for Versatile Artificial Sensory Neurons-
dc.typeArticle-
dc.identifier.doi10.1021/acs.nanolett.1c04125-
dc.description.journalClass1-
dc.identifier.bibliographicCitationNano Letters, v.22, no.2, pp.733 - 739-
dc.citation.titleNano Letters-
dc.citation.volume22-
dc.citation.number2-
dc.citation.startPage733-
dc.citation.endPage739-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.identifier.wosid000748834300001-
dc.identifier.scopusid2-s2.0-85123878778-
dc.relation.journalWebOfScienceCategoryChemistry, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryChemistry, Physical-
dc.relation.journalWebOfScienceCategoryNanoscience & Nanotechnology-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.relation.journalWebOfScienceCategoryPhysics, Condensed Matter-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.type.docTypeArticle-
dc.subject.keywordPlusPARALLEL-
dc.subject.keywordPlusSTATES-
dc.subject.keywordPlusPHASE-
dc.subject.keywordPlusMODEL-
dc.subject.keywordAuthorin-sensor computing-
dc.subject.keywordAuthorartificial retinal ganglion cell-
dc.subject.keywordAuthorgate-tunable Ovonic threshold switch-
dc.subject.keywordAuthorneuromorphic-
dc.subject.keywordAuthorspiking neural network-
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KIST Article > 2022
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