Designed Memristor Circuit for Self-Limited Analog Switching and its Application to a Memristive Neural Network

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Analog memristors enable compact neuromorphic computing with low power consumption. One of the issues with the technology is slow precise analog data programming. In this study, a novel analog data programming method utilizing a self-limited set switching is proposed. The method can transfer any resistance values from reference resistors to the target memristor accurately inside a crossbar array by performing an appropriate voltage clocking. An ideal memristor model based on the method is proposed and a Ti-doped NbOx charge trap memristor is evaluated as a promising candidate for applications. The characteristic error of the Ti-doped NbOx memristor device is about 5% on average, compared to the ideal memristor, and configuring optimum parallel resistors in the circuit further improves this to 2.95%. The method is then applied to program a memristive neural network and this error is confirmed negligible; thus the proposed method is viable.
Publisher
WILEY
Issue Date
2019-06
Language
English
Article Type
Article
Citation

ADVANCED ELECTRONIC MATERIALS, v.5, no.6

ISSN
2199-160X
DOI
10.1002/aelm.201800740
URI
http://hdl.handle.net/10203/263135
Appears in Collection
MS-Journal Papers(저널논문)
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