An Artificial Intelligence for the Analysis of a DC Magnetron Sputtering System Combined with a Particle-in-Cell Simulation

Abstract

A numerical method to estimate the plasma characteristics with the variation in control parameters is suggested with an artificial intelligence model using limited finite datasets. A transformer-based regression method was applied to estimate the spatial profiles of plasma characteristics in a DC magnetron sputtering system from limited data obtained by a two-dimensional particle-in-cell simulation under varying pressure. Based on the obtained simulation data, an artificial intelligence method successfully predicts the energy and angular distribution of ions incident on the target. This approach enables the quantitative estimation of the impact of various system parameter changes on plasma characteristics using only a limited number of simulation results. It is beneficial for practical applications, such as process optimization, because the ion energy and angle distributions can be estimated very fast without simulating all the cases.