Cascade artificial neural networks technique for solving ellipsometry problems

Authors

Nabil I. Boudani, Florida International University

Document Type

Thesis

Degree

Master of Science (MS)

Major/Program

Electrical Engineering

First Advisor's Name

Frank K. Urban

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Jean Andrian

Third Advisor's Name

David Barton

Fourth Advisor's Name

Malcolm Heimer

Date of Defense

4-3-1998

Abstract

Ellipsometry is a well known optical technique used for the characterization of reflective surfaces in study and films between two media. It is based on measuring the change in the state of polarization that occurs as a beam of polarized light is reflected from or transmitted through the film. Measuring this change can be used to calculate parameters of a single layer film such as the thickness and the refractive index. However, extracting these parameters of interest requires significant numerical processing due to the noninvertible equations. Typically, this is done using least squares solving methods which are slow and adversely affected by local minima in the solvable surface. This thesis describes the development and implementation of a new technique using only Artificial Neural Networks (ANN) to calculate thin film parameters. The new method offers a speed in the orders of magnitude faster than preceding methods and convergence to local minima is completely eliminated.

Identifier

FI14051806

Recommended Citation

Boudani, Nabil I., "Cascade artificial neural networks technique for solving ellipsometry problems" (1998). FIU Electronic Theses and Dissertations. 1781.
https://digitalcommons.fiu.edu/etd/1781