Abstract
Special gases with trace moisture cause the formation of dynamic acidic microdroplets (DMD), which results in corrosion of semiconductor manufacturing devices. In this study, a predictive model for corrosion damage in 316L stainless steel (SS) was developed by combining the DMD process and the pitting initiation model. The DMD process was modeled using the BET model to describe the moisture-to-solution conversion. The pitting initiation model was reconstructed by incorporating the Sridhar model, temporal corrosion model, and Macdonald model. Finally, the predicted results were validated by various experimental data, indicating that the prediction model was accurate and highly reliable.
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