Estimation of the optical constants and the thickness of thin films using unconstrained optimization
Journal of Computational Physics
Advanced Algorithms and Operators
Advanced Algorithms and Operators
Journal of Global Optimization
Journal of Computational and Applied Mathematics - Proceedings of the international conference on recent advances in computational mathematics
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
On the two-stage stochastic graph partitioning problem
COCOA'11 Proceedings of the 5th international conference on Combinatorial optimization and applications
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In this work, we proposed the new method for estimation of the thickness and the optical properties of the thin metal oxide film deposited on a transparent substrate. The developed method uses only transmittance spectra measured. Our method is based on the two stage optimization where the thickness is determined in the outer stage and the optical properties are determined in the inner stage. The differential evolutionary algorithm is used in solving the formulated problem. The proposed method was illustrated in the case study of Titanium dioxide film deposited on a glass substrate. The results indicate that the thickness and the optical properties estimated agree well with the experiment. Moreover, we investigated robustness of the proposed method in the case of transmittance spectra containing noises. The data were modelled by adding random noises ranging between 0 and 30% to the transmittance spectra measured. It is seen that the proposed method has better robustness and performance than the existing method based on pointwise unconstrained minimization approach. In solving the estimation problem, the performance of the proposed method was also compared with the well-known Levenberg---Marquardt method and single stage differential evolutionary method. The results indicate that the proposed method has better performance than Levenberg---Marquardt method and single stage differential evolutionary method. Moreover, the proposed method is more robust to random noise than Levenberg---Marquardt method and single stage differential evolutionary method.