Synaptic learning functionalities of inverse biomemristive device based on trypsin for artificial intelligence application
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Desai, T.R. | - |
dc.contributor.author | Dongale, T.D. | - |
dc.contributor.author | Patil, S.R. | - |
dc.contributor.author | Tiwari, A.P. | - |
dc.contributor.author | Pawar, P.K. | - |
dc.contributor.author | Kamat, R.K. | - |
dc.contributor.author | Kim, T.G. | - |
dc.date.accessioned | 2021-12-03T11:41:42Z | - |
dc.date.available | 2021-12-03T11:41:42Z | - |
dc.date.created | 2021-08-31 | - |
dc.date.issued | 2021-03 | - |
dc.identifier.issn | 2238-7854 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/129111 | - |
dc.description.abstract | This paper presents an organic memristive device based on the soft and quasi-liquid trypsin biomaterial. Accordingly, trypsin is isolated from the bovine pancreas and deposited on a conducting fluorine-doped tin oxide (FTO) substrate. The current-voltage (I-V) measurements of the trypsin/FTO device show a hysteresis loop at multiple frequencies and voltages. The results demonstrate that the current and resistance of the device can be modulated by altering the frequency and magnitude of the applied signal, suggesting the feasibility of this material for brain-inspired computing devices. The hysteresis area of the device increases in proportion to the frequency of the signal, signifying the inverse-memristive phenomenon. The time-domain flux, time-domain charge, and charge-flux properties are calculated from the experimental I-V data to demonstrate the memristive nature of the trypsin hydrogel. Interestingly, the trypsin hydrogel memristive device mimics bio-synaptic properties such as potentiation-depression and four complex spike-time-dependent plasticity learning rules. Electrochemical studies are performed to understand the electrochemical kinetics of the device. A possible switching mechanism is illustrated to show the memristive effect of the trypsin hydrogel. The results of the present investigation suggest that trypsin can be a possible biomaterial to develop an electronic synaptic device for neuromorphic computing applications. © 2021 The Author(s). | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Editora Ltda | - |
dc.title | Synaptic learning functionalities of inverse biomemristive device based on trypsin for artificial intelligence application | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, T.G. | - |
dc.identifier.doi | 10.1016/j.jmrt.2021.01.108 | - |
dc.identifier.scopusid | 2-s2.0-85102968071 | - |
dc.identifier.wosid | 000640317100006 | - |
dc.identifier.bibliographicCitation | Journal of Materials Research and Technology, v.11, pp.1100 - 1110 | - |
dc.relation.isPartOf | Journal of Materials Research and Technology | - |
dc.citation.title | Journal of Materials Research and Technology | - |
dc.citation.volume | 11 | - |
dc.citation.startPage | 1100 | - |
dc.citation.endPage | 1110 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Metallurgy & Metallurgical Engineering | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Metallurgy & Metallurgical Engineering | - |
dc.subject.keywordPlus | MEMRISTIVE DEVICES | - |
dc.subject.keywordPlus | MEMORY | - |
dc.subject.keywordPlus | NANOPARTICLES | - |
dc.subject.keywordPlus | CIRCUIT | - |
dc.subject.keywordAuthor | Biomaterial | - |
dc.subject.keywordAuthor | Inverse-memristive device | - |
dc.subject.keywordAuthor | Resistive switching | - |
dc.subject.keywordAuthor | Synaptic learning | - |
dc.subject.keywordAuthor | Trypsin | - |
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