Development of an automatic modeling system for reaction mechanisms in CVD processes using REX+JGG

Abstract

We have developed a system that automatically identifies the reaction models involved in the deposition of films on substrates with nanometer- or micrometer-sized trenches by Chemical Vapor Deposition (CVD) processes using Real-Coded Genetic Algorithms (RCGA). The reaction models, which comprise various deposition species and surface reactions with different values of sticking coefficient, were determined both quantitatively and qualitatively on the basis of chemical kinetics. Automatic modeling is achieved by the system through the analysis of the experimental data of the cross-sectional shapes of the deposited films. We investigated the influence of RCGA methods on the performance of the system. We tried two RCGA methods, that is, REX(φ, n+k) (Real-Coded Ensemble Crossover) + JGG (Just Generation Gap) and REX^star + JGG. Although the system successfully identified the appropriate reaction models both quantitatively and qualitatively using both the methods, REX^star + JGG showed better performance with regard to calculation cost. Therefore, we believe that REX^star + JGG is more promising for the analysis of the complicated reaction models.