AVS 45th International Symposium
    Thin Films Division Monday Sessions
       Session TF-MoP

Paper TF-MoP22
Thickness and Index Measurement of Transparent Thin Films using Neural Network processed Reflectance Data

Monday, November 2, 1998, 5:30 pm, Room Hall A

Session: Thin Films Poster Session
Presenter: M.F. Tabet, Nanometrics Inc.
Authors: M.F. Tabet, Nanometrics Inc.
W.A. McGahan, Nanometrics Inc.
Correspondent: Click to Email
Artificial neural networks and the Levenberg-Marquardt algorithm are combined to calculate the thickness and refractive index of transparent thin films from spectroscopic reflectometry data. A neural network is a set of simple, highly interconnected processing elements imitating the activity of the brain which are capable of learning information presented to them. Reflectometry has been used by the semiconductor industry to measure thin film thickness for decades. Modeling the optical constants of a film in the visible region with a Cauchy dispersion model allows the determination of both thickness and refractive index of most transparent thin films from reflectance data. In this work Artificial neural networks are used to obtain good initial estimates for thickness and two Cauchy parameters An and Bn, these estimates are then used as the starting point for the Levenberg-Marquardt which does a few iterations to find the final solution. This measurement program was implemented on a Nanometrics NanoSpec 8000XSE and will measure thickness and index of transparent films in the range of 1000 to 16000 Å in an average of four seconds.