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The use of artificial neural networks in electrostatic force microscopy.

Authors
  • Castellano-Hernández, Elena
  • Rodríguez, Francisco B
  • Serrano, Eduardo
  • Varona, Pablo
  • Sacha, Gomez Monivas
Type
Published Article
Journal
Nanoscale Research Letters
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Jan 01, 2012
Volume
7
Issue
1
Pages
250–250
Identifiers
DOI: 10.1186/1556-276X-7-250
PMID: 22587580
Source
Medline
License
Unknown

Abstract

The use of electrostatic force microscopy (EFM) to characterize and manipulate surfaces at the nanoscale usually faces the problem of dealing with systems where several parameters are not known. Artificial neural networks (ANNs) have demonstrated to be a very useful tool to tackle this type of problems. Here, we show that the use of ANNs allows us to quantitatively estimate magnitudes such as the dielectric constant of thin films. To improve thin film dielectric constant estimations in EFM, we first increase the accuracy of numerical simulations by replacing the standard minimization technique by a method based on ANN learning algorithms. Second, we use the improved numerical results to build a complete training set for a new ANN. The results obtained by the ANN suggest that accurate values for the thin film dielectric constant can only be estimated if the thin film thickness and sample dielectric constant are known.PACS: 07.79.Lh; 07.05.Mh; 61.46.Fg.

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