Improvement of prediction models for dry etching characteristics by introducing plasma parameters as explanatory variables

In the increasingly challenging dry etching process, methods utilizing machine learning for the efficient exploration of process conditions are gaining attention. Unlike the conventional process condition exploration methods that utilize digital twins to predict characteristics based on tool parameters as explanatory variables. We have developed a new method that improves etching characteristic prediction accuracy by introducing plasma parameter as explanatory variables in the digital twin.