This talk introduces a hybrid modeling framework for the Bosch etching process that combines multiscale plasma-surface simulations with machine-learning surrogates. Physics-based models generate datasets linking process parameters to key CFₓ species and passivation behavior, while ML models enable fast prediction and analysis of etch outcomes. The proposed approach represents a step toward physics-aware process optimization and data-driven control without relying on purely empirical tuning.