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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
Publikationsdatum
2017-04-13
Sprache
English
Author
Institut
TORE-URI
Enthalten in
Volume
50
Issue
19
Article Number
195102
Citation
Journal of Physics D: Applied Physics 19 (50): 195102 (2017-04-13)
Publisher DOI
Scopus ID
2-s2.0-85018454236
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.
Schlagworte
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.