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dc.contributor.authorAng, Diing Shenpen_US
dc.contributor.authorZhou, Yuen_US
dc.contributor.authorYew, Kwang Singen_US
dc.contributor.authorBerco, Danen_US
dc.identifier.citationAng, D. S., Zhou, Y., Yew, K. S., & Berco, D. (2019). On the area scalability of valence-change memristors for neuromorphic computing. Applied Physics Letters, 115(17), 173501-. doi:10.1063/1.5116270en_US
dc.description.abstractThe ability to vary the conductance of a valence-change memristor in a continuous manner makes it a prime choice as an artificial synapse in neuromorphic systems. Because synapses are the most numerous components in the brain, exceeding the neurons by several orders of magnitude, the scalability of artificial synapses is crucial to the development of large scale neuromorphic systems but is an issue which is seldom investigated. Leveraging on the conductive atomic force microscopy method, we found that the conductance switching of nanoscale memristors (∼25 nm2) is abrupt in a majority of the cases examined. This behavior is contrary to the analoglike conductance modulation or plasticity typically observed in larger area memristors. The result therefore implies that plasticity may be lost when the device dimension is scaled down. The contributing factor behind the plasticity behavior of a large-area memristor was investigated by current mapping, and may be ascribed to the disruption of the plurality of conductive filaments happening at different voltages, thus yielding an apparent continuous change in conductance with voltage. The loss of plasticity in scaled memristors may pose a serious constraint to the development of large scale neuromorphic systems.en_US
dc.relation.ispartofApplied Physics Lettersen_US
dc.rights© 2019 Author(s). All rights reserved. This paper was published by AIP Publishing in Applied Physics Letters and is made available with permission of Author(s).en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleOn the area scalability of valence-change memristors for neuromorphic computingen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsArtificial Neural Networksen_US
dc.subject.keywordsAtomic Force Microscopyen_US
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