Application Of Particle Swarm Optimization To Modulated Photothermal Reflectance Technique

Modulated photothermal reflectance (MPR) technique, as a highly sensitive and nondestructive measurement method, has been widely applied to the fields of science and technology. The MPR technique has attracted much more attention. As a detection method, however, fitting experimental data and getting sample's property parameters quantitatively haven't been solved satisfactorily. The particle swarm optimization algorithm, which is a new kind of intelligent and evolutionary computation, is hopefully applied to multiparameter fitting in measurement technologies. In order to ensure the intelligence and the optimality, the algorithm should be improved according to characteristics of fitting material's physical parameters. The particle swarm optimization algorithm is introduced in MPR technique based on the investigation of particle swarm optimization. The main contributions of this thesis are listed as follows:1. Particle swarm optimization algorithm is studied. In order to make up the deficits in fitting sample's parameters, it is proposed an improved particle swarm optimization in the thesis on the basis of summarization and analysis of particle swarm optimization. Modified strategies are presented as following: Firstly, when there is strong correlation among the parameters which need to be fitted, a dynamic adjustment of searching regions based on information about optimal particle can reduce the searching region as well as the probalility of local convergence. Secondly, on the condition that the parameters' searching region is very large, a new strategy of mutation is proposed, which enhances the searching intelligence and quickens the searching velocity.2. The theoretical study for characterizing thermal properties of thin films and substrates by MPR technique is presented. It is difficult to fit thermal parameters of film-substrate sample in which the film's thermal diffusivity is lower than the substrate's. On the basis of discussion about parameters' sensitivity and correlation, three thermal parameters, i.e. the film's thermal diffusivity, the substrate's thermal diffusivity and the thermal resistance on the film-substrate boundary, are simultaneously fitted with the improved particle swarm optimization. The simulative results showed that the improved particle swarm optimization algorithm fit the strong correlation parameters better than other methods.3. The theoretical and experimental studies of measuring physical parameters of semiconductor by MPR technique are performed. According to the theoretical model and the improved particle swarm optimization algorithm, the silicon parameters including thermal diffusivity, charge carrier life time and surface recombination velocity are fitted with measurable phase signals. Using the improved particle swarm optimization algorithm, it not only obtains satisfying fitting results but also solves the fitting difficulty that is brought from large parameter values' range.