Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 오세용 | - |
dc.date.accessioned | 2024-09-04T05:17:02Z | - |
dc.date.available | 2024-09-04T05:17:02Z | - |
dc.date.issued | 2021-12-21 | - |
dc.identifier.citation | NPJ 2D MATERIALS AND APPLICATIONS, v. 5, no 95, page. 1-8 | en_US |
dc.identifier.issn | 2397-7132 | en_US |
dc.identifier.uri | https://www.nature.com/articles/s41699-021-00274-5 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/192114 | - |
dc.description.abstract | In recent years, optoelectronic artificial synapses have garnered a great deal of research attention owing to their multifunctionality to process optical input signals or to update their weights optically. However, for most optoelectronic synapses, the use of optical stimuli is restricted to an excitatory spike pulse, which majorly limits their application to hardware neural networks. Here, we report a unique weight-update operation in a photoelectroactive synapse; the synaptic weight can be both potentiated and depressed using “optical spikes.” This unique bidirectional operation originates from the ionization and neutralization of inherent defects in hexagonal-boron nitride by co-stimuli consisting of optical and electrical spikes. The proposed synapse device exhibits (i) outstanding analog memory characteristics, such as high accessibility (cycle-to-cycle variation of <1%) and long retention (>21 days), and (ii) excellent synaptic dynamics, such as a high dynamic range (>384) and modest asymmetricity (<3.9). Such remarkable characteristics enable a maximum accuracy of 96.1% to be achieved during the training and inference simulation for human electrocardiogram patterns. | en_US |
dc.description.sponsorship | This research was supported by the Basic Science Research Program, Basic Research Lab Program, and Nano-Material Technology Development Program through National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIP) (2021R1A2C2010026, 2020R1A4A2002806, 2020M3F3A2A02082473, and 2019M3F3A1A01074451). This work was also supported by Samsung Electronics Co., Ltd. (IO201210-07994-01 and IO200304-07131-01). | en_US |
dc.language | en_US | en_US |
dc.publisher | NATURE RESEARCH | en_US |
dc.relation.ispartofseries | v. 5, no 95;1-8 | - |
dc.title | Photoelectroactive artificial synapse and its application to biosignal pattern recognition | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1038/s41699-021-00274-5 | en_US |
dc.relation.journal | NPJ 2D MATERIALS AND APPLICATIONS | - |
dc.contributor.googleauthor | Oh, Seyong | - |
dc.contributor.googleauthor | Lee, Je-Jun | - |
dc.contributor.googleauthor | Seo, Seunghwan | - |
dc.contributor.googleauthor | Yoo, Gwangwe | - |
dc.contributor.googleauthor | Park, Jin-Hong | - |
dc.relation.code | 2021006413 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | SCHOOL OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | seyongoh89 | - |
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