Characterization of nanofilm parameters based on hybrid optimization algorithm

In order to obtain accurate nano-film parameters in the ellipsometry measurement process, a hybrid optimization algorithm for nano-film parameter data processing was proposed. An Improved Particle Swarm Optimization-Neural Network(IPSO-NN) hybrid optimization algorithm has been proposed, based on the features of artificial neural network algorithm back propagation and particle swarm algorithm for fast optimization. This algorithm has the ability to jump out of the local optimal solution quickly with fewer iterations, so as to quickly find the optimal solution of ellipsometric equation. The algorithm was used to calculate the film parameters of silicon dioxide nano-film thickness standard template with a strandard value of 26.7±0.4 nm in this paper. The results show that the relative error of the film thickness calculation by IPSO-NN hybrid optimization algorithm is less than 2%, and the refractive index error is less than 0.1. At the same time, this paper compares the traditional particle swarm algorithm with the IPSO-NN algorithm through experiments, and verifies that the IPSO-NN algorithm can optimize the number of iterations effectively and the process of finding the optimal solution. This algorithm can achieve rapid convergence and improve the calculation efficiency when calculating the thin film parameters.