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dc.contributor.authorMaier, P.
dc.contributor.authorHartmann, Fabian
dc.contributor.authorRebello Sousa Dias, M.
dc.contributor.authorEmmerling, M.
dc.contributor.authorSchneider, C.
dc.contributor.authorCastelano, L. K.
dc.contributor.authorKamp, M.
dc.contributor.authorMarques, G. E.
dc.contributor.authorLopez-Richard, V.
dc.contributor.authorWorschech, L.
dc.contributor.authorHöfling, Sven
dc.date.accessioned2016-09-26T11:30:15Z
dc.date.available2016-09-26T11:30:15Z
dc.date.issued2016-10-07
dc.identifier.citationMaier , P , Hartmann , F , Rebello Sousa Dias , M , Emmerling , M , Schneider , C , Castelano , L K , Kamp , M , Marques , G E , Lopez-Richard , V , Worschech , L & Höfling , S 2016 , ' Mimicking of pulse shape-dependent learning rules with a quantum dot memristor ' , Journal of Applied Physics , vol. 120 , no. 13 , 134503 . https://doi.org/10.1063/1.4963830en
dc.identifier.issn0021-8979
dc.identifier.otherPURE: 246059779
dc.identifier.otherPURE UUID: a1f9d482-48cc-4b25-b95e-765a39b8a679
dc.identifier.otherScopus: 84989834075
dc.identifier.otherWOS: 000386155100022
dc.identifier.urihttps://hdl.handle.net/10023/9554
dc.descriptionThe authors gratefully acknowledge financial support from the European Union (FPVII (2007-2013) under grant agreement n° 318287 Landauer) as well as the state of Bavaria. The Brazilian authors acknowledge the support of CNPq. V. L.-R. acknowledges the support of FAPESP (grants 2014/02112-3 and 2015/10765-0).en
dc.description.abstractWe present the realization of four different learning rules with a quantum dot memristor by tuning the shape, the magnitude, the polarity and the timing of voltage pulses. The memristor displays a large maximum to minimum conductance ratio of about 57000 at zero bias voltage. The high and low conductances correspond to different amounts of electrons localized in quantum dots, which can be successively raised or lowered by the timing and shapes of incoming voltage pulses. Modifications of the pulse shapes allow altering the conductance change in dependence on the time difference. Hence, we are able to mimic different learning processes in neural networks with a single device. In addition, the device performance under pulsed excitation is emulated combining the Landauer-Büttiker formalism with a dynamic model for the quantum dot charging, which allows explaining the whole spectrum of learning responses in terms of structural parameters that can be adjusted during fabrication such as gating efficiencies and tunneling rates. The presented memristor may pave the way for future artificial synapses with a stimulus-dependent capability of learning.
dc.language.isoeng
dc.relation.ispartofJournal of Applied Physicsen
dc.rights© 2016, the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at scitation.aip.org / http://dx.doi.org/10.1063/1.4963830en
dc.subjectQC Physicsen
dc.subjectTK Electrical engineering. Electronics Nuclear engineeringen
dc.subjectNDASen
dc.subject.lccQCen
dc.subject.lccTKen
dc.titleMimicking of pulse shape-dependent learning rules with a quantum dot memristoren
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Physics and Astronomyen
dc.contributor.institutionUniversity of St Andrews. Condensed Matter Physicsen
dc.identifier.doihttps://doi.org/10.1063/1.4963830
dc.description.statusPeer revieweden


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