Accelerated Learning in Wide-Band-Gap AlN Artificial Photonic Synaptic Devices: Impact on Suppressed Shallow Trap Level
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Moonsang | - |
dc.contributor.author | Nam, Seunghyun | - |
dc.contributor.author | Cho, Byungjin | - |
dc.contributor.author | Kwon, Ojun | - |
dc.contributor.author | Lee, Hyun Uk | - |
dc.contributor.author | Hahm, Myung Gwan | - |
dc.contributor.author | Kim, Un Jeong | - |
dc.contributor.author | Son, Hyungbin | - |
dc.date.accessioned | 2021-09-16T03:40:17Z | - |
dc.date.available | 2021-09-16T03:40:17Z | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 1530-6984 | - |
dc.identifier.issn | 1530-6992 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/49173 | - |
dc.description.abstract | Artificial synaptic platforms are promising for next-generation semiconductor computing devices; however, state-of-the-art optoelectronic approaches remain challenging, owing to their unstable charge trap states and limited integration. We demonstrate wide-band-gap (WBG) III-V materials for photoelectronic neural networks. Our experimental analysis shows that the enhanced crystallinity of WBG synapses promotes better synaptic characteristics, such as effective multilevel states, a wider dynamic range, and linearity, allowing the better power consumption, training, and recognition accuracy of artificial neural networks. Furthermore, light-frequency-dependent memory characteristics suggest that artificial optoelectronic synapses with improved crystallinity support the transition from short-term potentiation to long-term potentiation, implying a clear emulation of the psychological multistorage model. This is attributed to the charge trapping in deep-level states and suppresses fast decay and nonradiative recombination in shallow traps. We believe that the fingerprints of these WBG synaptic characteristics provide an effective strategy for establishing an artificial optoelectronic synaptic architecture for innovative neuromorphic computing. © 2021 American Chemical Society. All rights reserved. | - |
dc.format.extent | 8 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | American Chemical Society | - |
dc.title | Accelerated Learning in Wide-Band-Gap AlN Artificial Photonic Synaptic Devices: Impact on Suppressed Shallow Trap Level | - |
dc.type | Article | - |
dc.identifier.doi | 10.1021/acs.nanolett.1c01885 | - |
dc.identifier.bibliographicCitation | Nano Letters, v.21, no.18, pp 7879 - 7886 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000700883900060 | - |
dc.identifier.scopusid | 2-s2.0-85113145940 | - |
dc.citation.endPage | 7886 | - |
dc.citation.number | 18 | - |
dc.citation.startPage | 7879 | - |
dc.citation.title | Nano Letters | - |
dc.citation.volume | 21 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | artificial synapse | - |
dc.subject.keywordAuthor | crystallinity | - |
dc.subject.keywordAuthor | neuromorphic | - |
dc.subject.keywordAuthor | shallow trap | - |
dc.subject.keywordAuthor | wide band gap | - |
dc.subject.keywordPlus | Aluminum nitride | - |
dc.subject.keywordPlus | Charge trapping | - |
dc.subject.keywordPlus | Crystallinity | - |
dc.subject.keywordPlus | Energy gap | - |
dc.subject.keywordPlus | III-V semiconductors | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Nitrogen compounds | - |
dc.subject.keywordPlus | Semiconductor devices | - |
dc.subject.keywordPlus | Charge trap state | - |
dc.subject.keywordPlus | Computing devices | - |
dc.subject.keywordPlus | Experimental analysis | - |
dc.subject.keywordPlus | Long-term potentiations | - |
dc.subject.keywordPlus | Neuromorphic computing | - |
dc.subject.keywordPlus | Non-radiative recombinations | - |
dc.subject.keywordPlus | Recognition accuracy | - |
dc.subject.keywordPlus | State of the art | - |
dc.subject.keywordPlus | Wide band gap semiconductors | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Physical | - |
dc.relation.journalWebOfScienceCategory | Nanoscience & Nanotechnology | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.relation.journalWebOfScienceCategory | Physics, Condensed Matter | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.