The use of artificial neural networks in electrostatic force microscopy
Title (trans.)On the use of artificial neural networks in electrostatic force microscopy
EntityUAM. Departamento de Ingeniería Informática
PublisherSpringer Pub. Co.
10.1186/1556-276X-7-250Nanoscale Research Letters 7 (2012): 250
ISSN1931-7573 (print); 1556-276X (online)
Funded byThis work was supported by TIN2010-19607 and BFU2009-08473. GMS acknowledges support from the Spanish Ramón y Cajal Program.
SubjectsArtificial neural networks; Electrostatic force microscopy; Thin films; Informática
NoteThe electronic version of this article is the complete one and can be found online at: http://link.springer.com/article/10.1186/1556-276X-7-250
Includes the electronic version of the poster presented at the 12th Trends in Nanotechnology International Conference (TNT2011), held in Tenerife (Spain)
Rights© 2012 Castellano-Hernandez et al.; licensee Springer
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
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.
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Póster presentado en el "Trends in Nanotechnology International Conference (TNT2011)", que fue publicado de manera extensa como artículo
Google Scholar:Castellano-Hernández, Elena - Serrano Jerez, Eduardo - Varona Martínez, Pablo - Gómez Moñivas, Sacha - Rodríguez Ortiz, Francisco Borja
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