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Simultaneous estimation of thin film thickness and optical properties using two-stage optimization

机译:使用两阶段优化同时估算薄膜厚度和光学性能

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摘要

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.
机译:在这项工作中,我们提出了一种新的方法来估算沉积在透明基板上的金属氧化物薄膜的厚度和光学性能。开发的方法仅使用测得的透射光谱。我们的方法基于两阶段优化,其中在外部确定厚度,而在内部确定光学特性。差分进化算法用于解决所提出的问题。在玻璃基板上沉积二氧化钛薄膜的案例研究中说明了该方法。结果表明,估计的厚度和光学性质与实验吻合良好。此外,我们研究了在包含噪声的透射光谱情况下该方法的鲁棒性。通过将介于0%到30%之间的随机噪声添加到所测量的透射光谱中来对数据进行建模。可以看出,与现有的基于逐点无约束最小化方法的方法相比,该方法具有更好的鲁棒性和性能。在解决估计问题时,还将所提方法的性能与著名的Levenberg-Marquardt方法和单级微分进化方法进行了比较。结果表明,该方法比Levenberg-Marquardt方法和单级微分进化方法具有更好的性能。此外,与Levenberg-Marquardt方法和单级差分进化方法相比,该方法对随机噪声的鲁棒性更高。

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