https://doi.org/10.1051/epjap/2011100488
Application of multilayer perceptron neural networks for predicting the permeability tensor components of thin ferrite films
1
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
a e-mail: f.djerfaf@mail.lagh-univ.dz
b e-mail: vincentd@univ-st-etienne.fr
Received:
6
December
2010
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
