The Application Of Modified Particle Swarm Optimization On The Film Parameters Inversion And Design

Optical thin film plays an important role in modern optics. Without the optical thin film, there won't be so many complex high performance optical instruments, optoelectronic devices and optical systems. With developments of science and technology, especially those in optical communication and projector display, the method to measure parameters of optical thin film quickly and accurately becomes a bottleneck to confine the development of thin film technology.Ellipsometry is an optical analytical technique, which is used to determine the optical constants and the film thickness from measurements of the change in polarization state of reflecting light. It has such advantages as remarkable sensitivity, nondestructive characteristic, etc. However, we can not get the film parameters directly because the ellipsometric equation is a transcendental equation. We can hardly find the analytical solution for film parameters from the measured ellipsometric parameters⊿ andΨ, Therefore, it becomes a primary but an important problem to find an algorithm to inverse the measured data⊿ andΨ.Particle swarm optimization (PSO) is a new kind of swarm intelligent stochastic optimization algorithm, which is simple and easily implemented, fast convergent, few parameters to set and has strong ability of global optimization, so as to be used to inverse the measured ellipsometric parameters. According to the features of the measured ellipsometric parameters, a new evaluation function with the characterization of p is found, where p is the function of the amplitude ratio and phase difference between the P light and S light which have the same polarization state before reflected by the film, and the change of polarized light polarization state reflected by the film can be completely characterized by the function. Compared to the case of usingΨand⊿ directly as the evaluation function the new evaluation function program is much simpler, less calculation steps needed, so that the parameter inversion efficiency is greatly improved.Although the PSO algorithm has been used for more than one decade, the theory and application of PSO are not fully developed, an optimization is necessary when the PSO is applied to inverse the measured ellipsometric parameters. After a study on the optimization performance of PSO in the inversion with various parameters, it is found that the range of inertia weightωfrom 0.5 to 0.8 is the best, the sum of learning parameters c1, c2 is preferably not greater than 3. Besides, a smaller c1 is required to match a larger c2, and the population parameter c2 has a stronger influence on the algorithm than the individual parameter c1.Concerning the application of the modified PSO to design films, experiments were carried out in accordance with two-film-design requirements. The results show that this method can get better results under the same coating layer number in comparison with the traditional needle method.