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An enhanced lumped element electrical model of a double barrier memristive device
Citation Link: https://doi.org/10.15480/882.2522
Publikationstyp
Journal Article
Date Issued
2017-04-13
Sprache
English
Institut
TORE-DOI
TORE-URI
Journal
Volume
50
Issue
19
Article Number
195102
Citation
Journal of Physics D: Applied Physics 19 (50): 195102 (2017-04-13)
Publisher DOI
Scopus ID
Publisher
IOP Publ.
The massive parallel approach of neuromorphic circuits leads to effective methods for solving complex problems. It has turned out that resistive switching devices with a continuous resistance range are potential candidates for such applications. These devices are memristive systems - nonlinear resistors with memory. They are fabricated in nanotechnology and hence parameter spread during fabrication may aggravate reproducible analyses. This issue makes simulation models of memristive devices worthwhile. Kinetic Monte-Carlo simulations based on a distributed model of the device can be used to understand the underlying physical and chemical phenomena. However, such simulations are very time-consuming and neither convenient for investigations of whole circuits nor for real-time applications, e.g. emulation purposes. Instead, a concentrated model of the device can be used for both fast simulations and real-time applications, respectively. We introduce an enhanced electrical model of a valence change mechanism (VCM) based double barrier memristive device (DBMD) with a continuous resistance range. This device consists of an ultra-thin memristive layer sandwiched between a tunnel barrier and a Schottky-contact. The introduced model leads to very fast simulations by using usual circuit simulation tools while maintaining physically meaningful parameters. Kinetic Monte-Carlo simulations based on a distributed model and experimental data have been utilized as references to verify the concentrated model.
Subjects
electrical modeling
memristive devices
memristor
nanoelectronics
neuromorphic circuits
resistive switching
DDC Class
600: Technik
More Funding Information
The financial support by the German Research Foundation (Deutsche Forschungsgemeinschaft—DFG) through FOR 2093 is gratefully acknowledged.
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Solan_2017_J._Phys._D__Appl._Phys._50_195102.pdf
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