Eur. Phys. J. Appl. Phys.
Volume 56, Number 3, December 2011
|Number of page(s)||8|
|Section||Spintronics, Magnetism and Superconductivity|
|Published online||14 November 2011|
Application of multilayer perceptron neural networks for predicting the permeability tensor components of thin ferrite films
Laboratoire des matériaux diélectriques, Université Amar Telidji-Laghouat, Algeria
2 DIOM, Université de Lyon, 42023 Saint-Étienne, France
3 Laboratoire d’Instrumentation Scientifique (LIS), Département d’Électronique, Faculté des Sciences de l’Ingénieur, Université Ferhat ABBAS, 1900 Sétif, Algeria
Revised: 6 April 2011
Accepted: 7 July 2011
Published online: 14 November 2011
A novel characterization method using artificial neural networks is presented. This method allows one to determine the intrinsic permeability tensor of ferrite thin-films from S-parameters measurements. Neural networks, efficient to solve inverse problems, are used to compute the permeability tensor components μ and k. This optimization technique is used to find extremely complex functions between inputs and outputs and can be successfully applied on our magnetic thin-film characterization problem. Results of our networks are compared to a theoretical model. A great number of both simulated and measured tests have been performed on many magnetic thin-films. Neural network processing leads to a rapid and robust method for predicting the magnetic characterization of thin-films in microwave range.
© EDP Sciences, 2011
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.