A data driven model of TiO2 printed memristors

Author

Gambuzza, Lucia Valentina ; Samardzic, Natasa ; Dautovic, S. ; Xibilia, Maria Gabriella ; Graziani, Salvatore ; Fortuna, Luigi ; Stojanovic, Goran ; Frasca, Mattia

Author_Institution

Dept. of Electr., Electron. & Comput. Eng., Univ. of Catania, Catania, Italy

fYear

2013

fDate

28-30 Nov. 2013

Firstpage

1

Lastpage

4

Abstract

After the fabrication of several devices showing memristive switching behavior, recently a growing interest to the realization of dynamical nonlinear circuits based on memristors has been manifested. Currently, many memristor circuits have been mostly conceived on the basis of theoretical memristor models. However, in order to analyze the dynamical behavior of memristor circuits with real components and to implement them, the characteristics of the fabricated devices have to be included in the models used. To this aim, a compact data-driven model is proposed in this paper. The model is based on neural networks and is derived starting from experimental measurements performed on printed TiO₂ memristors.

Keywords

memristors; neural nets; titanium compounds; TiO₂; compact data-driven model; dynamical nonlinear circuits; memristive switching behavior; neural networks; printed memristors; Autoregressive processes; Fabrication; Hysteresis; Integrated circuit modeling; Memristors; Neural networks; Solid modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Electronics Engineering (ELECO), 2013 8th International Conference on

Conference_Location

Bursa

Print_ISBN

978-605-01-0504-9

Type

conf

DOI

10.1109/ELECO.2013.6713923

Filename

6713923