DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, Joon-Kyu | ko |
dc.contributor.author | Chung, Young-Woo | ko |
dc.contributor.author | Sim, Jaeho | ko |
dc.contributor.author | Yu, Ji-Man | ko |
dc.contributor.author | Lee, Geon-Beom | ko |
dc.contributor.author | Kim, Sang-Hyeon | ko |
dc.contributor.author | Choi, Yang-Kyu | ko |
dc.date.accessioned | 2022-02-22T06:43:07Z | - |
dc.date.available | 2022-02-22T06:43:07Z | - |
dc.date.created | 2022-01-10 | - |
dc.date.created | 2022-01-10 | - |
dc.date.created | 2022-01-10 | - |
dc.date.created | 2022-01-10 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.citation | SCIENTIFIC REPORTS, v.12, no.1 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | http://hdl.handle.net/10203/292347 | - |
dc.description.abstract | A mnemonic-opto-synaptic transistor (MOST) that has triple functions is demonstrated for an in-sensor vision system. It memorizes a photoresponsivity that corresponds to a synaptic weight as a memory cell, senses light as a photodetector, and performs weight updates as a synapse for machine vision with an artificial neural network (ANN). Herein the memory function added to a previous photodetecting device combined with a photodetector and a synapse provides a technical breakthrough for realizing in-sensor processing that is able to perform image sensing and signal processing in a sensor. A charge trap layer (CTL) was intercalated to gate dielectrics of a vertical pillar-shaped transistor for the memory function. Weight memorized in the CTL makes photoresponsivity tunable for real-time multiplication of the image with a memorized photoresponsivity matrix. Therefore, these multi-faceted features can allow in-sensor processing without external memory for the in-sensor vision system. In particular, the in-sensor vision system can enhance speed and energy efficiency compared to a conventional vision system due to the simultaneous preprocessing of massive data at sensor nodes prior to ANN nodes. Recognition of a simple pattern was demonstrated with full sets of the fabricated MOSTs. Furthermore, recognition of complex hand-written digits in the MNIST database was also demonstrated with software simulations. | - |
dc.language | English | - |
dc.publisher | Nature Publishing GroupNATURE RESEARCH | - |
dc.title | Mnemonic-Opto-Synaptic Transistor for In-sensor Vision System | - |
dc.type | Article | - |
dc.identifier.wosid | 000750981100100 | - |
dc.identifier.scopusid | 2-s2.0-85124061341 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 1 | - |
dc.citation.publicationname | SCIENTIFIC REPORTS | - |
dc.identifier.doi | 10.1038/s41598-022-05944-y | - |
dc.contributor.localauthor | Kim, Sang-Hyeon | - |
dc.contributor.localauthor | Choi, Yang-Kyu | - |
dc.contributor.nonIdAuthor | Chung, Young-Woo | - |
dc.contributor.nonIdAuthor | Sim, Jaeho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.