An enhanced lumped element electrical model of a double barrier memristive device. (13th April 2017)

• Record Type: Journal Article
• Authors: Solan, Enver; Dirkmann, Sven; Hansen, Mirko; Schroeder, Dietmar; Kohlstedt, Hermann; Ziegler, Martin; Mussenbrock, Thomas; Ochs, Karlheinz
• Is Part Of: Journal of physics. Volume 50:Number 19(2017)
• Publication Date: 2017-04-13
• Subjects: memristive devices -- resistive switching -- neuromorphic circuits -- electrical modeling -- nanoelectronics -- memristor; Physics -- Periodicals; 530
• DOI: 10.1088/1361-6463/aa69ae
• Languages: English
• ISSNs: 0022-3727

Abstract

Abstract: 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.