Extracting film thickness and optical constants from spectrophotometric data by evolutionary optimization

Area of Science:

• Materials Science
• Optics
• Computational Physics

Background:

• Accurate characterization of thin film optical properties is crucial for device performance.
• Traditional methods for extracting optical constants can be complex and time-consuming.
• Inverse problems in optics often require robust optimization techniques for unique solutions.

Purpose of the Study:

• To develop a simple and effective method for extracting film thickness and optical constants.
• To utilize reflectance and transmittance spectra for optical property determination.
• To address the inverse problem of optical characterization via optimization.

Main Methods:

• An evolutionary optimization approach is employed to solve the inverse problem.
• Tauc-Lorentz Oscillators (TLOs) and Gaussian Oscillators (GOs) models are used to simulate spectra.
• Genetic Algorithm (GA) and Covariance Matrix Adaptation Evolution Strategy (CMAES) are utilized for optimization.

Main Results:

• The proposed method successfully extracts thickness and optical constants from spectral data.
• The evolutionary optimization ensures convergence to a global optimum for unique solutions.
• Simulated reflectance and transmittance spectra accurately fit experimental data points.

Conclusions:

• The developed method provides an efficient and reliable way to determine optical parameters of films.
• Evolutionary algorithms offer a powerful tool for solving complex inverse problems in optical characterization.
• This approach validates the effectiveness of model-based optimization for thin film analysis.