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.
A data driven model of TiO2 printed memristors
Gambuzza LV;GRAZIANI, Salvatore;FORTUNA, Luigi;FRASCA, MATTIA
2013-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
