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dc.contributor.authorMaier, P.
dc.contributor.authorHartmann, F.
dc.contributor.authorEmmerling, M.
dc.contributor.authorSchneider, C.
dc.contributor.authorKamp, M.
dc.contributor.authorHöfling, S.
dc.contributor.authorWorschech, L.
dc.date.accessioned2016-09-26T13:30:09Z
dc.date.available2016-09-26T13:30:09Z
dc.date.issued2016-05-17
dc.identifier.citationMaier , P , Hartmann , F , Emmerling , M , Schneider , C , Kamp , M , Höfling , S & Worschech , L 2016 , ' Electro-photo-sensitive memristor for neuromorphic and arithmetic computing ' Physical Review Applied , vol 5 , no. 5 , 054011 , pp. 1-9 . DOI: 10.1103/PhysRevApplied.5.054011en
dc.identifier.issn2331-7019
dc.identifier.otherPURE: 241962971
dc.identifier.otherPURE UUID: 3214abab-b423-40f1-8ed4-921a29d9ac79
dc.identifier.otherScopus: 84973659480
dc.identifier.otherScopus: 84973659480
dc.identifier.urihttp://hdl.handle.net/10023/9557
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=84973659480&partnerID=8YFLogxKen
dc.descriptionThe authors gratefully acknowledge financial support from the European Union [FPVII (2007-2013) under Grant Agreement No. 318287 Landauer], as well as the state of Bavaria.en
dc.description.abstractWe present optically and electrically tunable conductance modifications of a site-controlled quantum-dot memristor. The conductance of the device is tuned by electron localization on a quantum dot. The control of the conductance with voltage and low-power light pulses enables applications in neuromorphic and arithmetic computing. As in neural networks, applying pre- and postsynaptic voltage pulses to the memristor allows us to increase (potentiation) or decrease (depression) the conductance by tuning the time difference between the electrical pulses. Exploiting state-dependent thresholds for potentiation and depression, we are able to demonstrate a memory-dependent induction of learning. The discharging of the quantum dot can further be induced by low-power light pulses in the nanowatt range. In combination with the state-dependent threshold voltage for discharging, this enables applications as generic building blocks to perform arithmetic operations in bases ranging from binary to decimal with low-power optical excitation. Our findings allow the realization of optoelectronic memristor-based synapses in artificial neural networks with a memory-dependent induction of learning and enhanced functionality by performing arithmetic operations.en
dc.format.extent9en
dc.language.isoeng
dc.relation.ispartofPhysical Review Applieden
dc.rightsCopyright © 2016, American Physical Society 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 https://dx.doi.org/10.1103/PhysRevApplied.5.054011en
dc.subjectMemristoren
dc.subjectArtificial neural networken
dc.subjectMetaplasticityen
dc.subjectSynaptic plasticityen
dc.subjectQuantum doten
dc.subjectFloating gate transistoren
dc.subjectQC Physicsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectPhysics and Astronomy(all)en
dc.subjectNDASen
dc.subject.lccQCen
dc.subject.lccQA75en
dc.titleElectro-photo-sensitive memristor for neuromorphic and arithmetic computingen
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.doihttp://dx.doi.org/10.1103/PhysRevApplied.5.054011
dc.description.statusPeer revieweden


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