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Graphene oxide based memristor

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Khurana_2018_Graphene_oxide_MMNN_19_CC.pdf (4.842Mb)
Date
04/04/2018
Author
Khurana, Geetika
Kumar, Nitu
Scott, James Floyd
Katiyar, Ram S.
Keywords
Memristor
Graphene oxide
Forming free
Multi-bit storage
Flexible devices
QC Physics
TK Electrical engineering. Electronics Nuclear engineering
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Abstract
A memristor is the memory extension to the concept of resistor. With unique superior properties, memristors have prospective promising applications in non‐volatile memory (NVM). Resistive random access memory (RRAM) is a non‐volatile memory using a material whose resistance changes under electrical stimulus can be seen as the most promising candidate for next generation memory both as embedded memory and a stand‐alone memory due to its high speed, long retention time, low power consumption, scalability and simple structure. Among carbon‐based materials, graphene has emerged as wonder material with remarkable properties. In contrast to metallic nature of graphene, the graphene oxide (GO) is good insulating/semiconducting material and suitable for RRAM devices. The advantage of being atomically thin and the two-dimensional of GO permits scaling beyond the current limits of semiconductor technology, which is a key aspect for high‐density fabrication. Graphene oxide‐based resistive memory devices have several advantages over other oxide materials, such as easy synthesis and cost‐effective device fabrication, scaling down to few nanometre and compatibility for flexible device applications. In this chapter, we discuss the GO‐based RRAM devices, which have shown the properties of forming free, thermally stable, multi‐bit storage, flexible and high on/off ratio at low voltage, which boost up the research and development to accelerate the GO‐based RRAM devices for future memory applications.
Citation
Khurana , G , Kumar , N , Scott , J F & Katiyar , R S 2018 , Graphene oxide based memristor . in A P James (ed.) , Memristor and Memristive Neural Networks . InTech , pp. 19-40 . https://doi.org/10.5772/intechopen.69752
Publication
Memristor and Memristive Neural Networks
DOI
https://doi.org/10.5772/intechopen.69752
Type
Book item
Rights
© 2018 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.
Description
The authors acknowledge the financial support from DOD Grant (AFOSR‐FA9550‐16‐1‐0295) and IFN‐NSF Grant (EPS‐01002410) for travel support.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/13080

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