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Leaky 2T Dynamic Random-Access Memory Devices Based on Nanometer-Thick Indium-Gallium-Zinc-Oxide Films for Reservoir Computing
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Jang, Junwon | - |
| dc.contributor.author | Kim, Seongmin | - |
| dc.contributor.author | Park, Suyong | - |
| dc.contributor.author | Kim, Soomin | - |
| dc.contributor.author | Kim, Sungjun | - |
| dc.contributor.author | Cho, Seongjae | - |
| dc.date.accessioned | 2024-10-14T06:30:19Z | - |
| dc.date.available | 2024-10-14T06:30:19Z | - |
| dc.date.issued | 2024-10 | - |
| dc.identifier.issn | 2574-0970 | - |
| dc.identifier.issn | 2574-0970 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/26461 | - |
| dc.description.abstract | This paper explores the integration of indium-gallium-zinc oxide (IGZO)-based 2-transistor 0-capacitor dynamic random-access memory (2T0C DRAM, or shortly, 2T DRAM) into reservoir computing for advanced semiconductor artificial intelligence (AI) applications. The short-term memory characteristics of IGZO 2T DRAM enable rapid read-write speeds essential for processing time-varying input data. Experimental results confirm high on/off ratios and leaky retention behaviors. The study also examines paired-pulse facilitation (PPF) phenomena, offering insights into reinforcement mechanisms for cognitive computing. Finally, the reservoir computing approach achieves notable pattern recognition accuracy with a 4-bit pulse scheme, showcasing its effectiveness in complex data sets. [GRAPHICS] | - |
| dc.format.extent | 6 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | American Chemical Society | - |
| dc.title | Leaky 2T Dynamic Random-Access Memory Devices Based on Nanometer-Thick Indium-Gallium-Zinc-Oxide Films for Reservoir Computing | - |
| dc.type | Article | - |
| dc.publisher.location | 미국 | - |
| dc.identifier.doi | 10.1021/acsanm.4c04501 | - |
| dc.identifier.scopusid | 2-s2.0-85205772904 | - |
| dc.identifier.wosid | 001326679500001 | - |
| dc.identifier.bibliographicCitation | ACS Applied Nano Materials, v.7, no.19, pp 22430 - 22435 | - |
| dc.citation.title | ACS Applied Nano Materials | - |
| dc.citation.volume | 7 | - |
| dc.citation.number | 19 | - |
| dc.citation.startPage | 22430 | - |
| dc.citation.endPage | 22435 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
| dc.relation.journalResearchArea | Materials Science | - |
| dc.relation.journalWebOfScienceCategory | Nanoscience & Nanotechnology | - |
| dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
| dc.subject.keywordAuthor | neuromorphic system | - |
| dc.subject.keywordAuthor | reservoir computing | - |
| dc.subject.keywordAuthor | capacitorlessdynamic random-access memory | - |
| dc.subject.keywordAuthor | two-transistor DRAM | - |
| dc.subject.keywordAuthor | artificial synaptic array | - |
| dc.subject.keywordAuthor | In-Ga-Zn-O | - |
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