Abstract
The identification of appropriate reaction models is very helpful for developing chemical vapor deposition (CVD) processes. In this study, we developed an automatic modeling system that analyzes experimental data on the cross- sectional shapes of films deposited on substrates with nanometer- or micrometer-sized trenches. The system then identifies a suitable reaction model to describe the film deposition. The inference engine used by the system to model the reaction mechanism was designed using real-coded genetic algorithms (RCGAs): a generation alternation model named “just generation gap” (JGG) and a real-coded crossover named “real-coded ensemble crossover” (REX). We studied the effect of REX+JGG on the system's performance, and found that the system with REX+JGG was the most accurate and reliable at model identification among the algorithms that we studied.
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