M
M.F. Tabet
Researcher at Florida International University
Publications - 12
Citations - 156
M.F. Tabet is an academic researcher from Florida International University. The author has contributed to research in topics: Thin film & Surface roughness. The author has an hindex of 6, co-authored 12 publications receiving 147 citations.
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Optical properties of cobalt oxide films deposited by spray pyrolysis
TL;DR: In this paper, the optical constants of Co3O4films were determined by analyzing variable angle of incidence spectroscopic ellipsometry data and normal incidence transmittance data, between 3500 and 17 000 A. The films were deposited by spray pyrolysis of cobalt acetylacetonate onto heated sodalime-silica float glass and fused silica substrates.
Journal ArticleDOI
Use of artificial neural networks to predict thickness and optical constants of thin films from reflectance data
M.F. Tabet,William A. McGahan +1 more
TL;DR: Artificial neural networks and the Levenberg-Marquardt algorithm are combined to calculate the thickness and refractive index of thin films from spectroscopic reflectometry data as mentioned in this paper.
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Development of artificial neural networks for real time, in situ ellipsometry data reduction
TL;DR: This work describes the development of enhanced, high speed data reduction algorithms using artificial neural networks (ANN), which are trained using computed data and subsequently give values of film parameters in the millisecond time regime.
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Determining the optical properties of a mixed-metal oxide film, Co3−x−yCrxFeyO4, with spectroscopic ellipsometry and atomic force microscopy
TL;DR: The optical properties of a mixed-metal oxide thin film from the Co3−x−yCrxFeyO4 family have been determined from combined analysis of ellipsometry, atomic force microscopy, and transmittance measurements as discussed by the authors.
Journal ArticleDOI
Real time, in-situ ellipsometry solutions using artificial neural network pre-processing
Frank K. Urban,M.F. Tabet +1 more
TL;DR: The work here addresses a key question raised in the prior work: how the solution workload should best be shared between the ANN and VDLS for fast, accurate solutions.