# Virtual Metrology

Virtual metrology for chemical mechanical planarization (CMP) material removal rate (MRR) has relied on simplified linear assumptions or handcrafted feature engineering, which needs domain knowledge and can discard temporal information in raw sensor signals. GREP first pretrains self-supervised on unlabeled sensor sequences, having the encoder regress global proxy targets projected from complete and half sequences, then fine-tunes on labeled data. On the PHM 2016 CMP dataset it matches a feature-engineered supervised baseline across most labeled-data fractions, lowers error in selected limited-label settings, and cuts RMSE from 11.23 to 9.32 in the final chronological interval.

Virtual metrology for chemical mechanical planarization (CMP) material removal rate (MRR) has relied on simplified linear assumptions or handcrafted feature engineering, which needs domain knowledge and can discard temporal information in raw sensor signals. GREP first pretrains self-supervised on unlabeled sensor sequences, having the encoder regress global proxy targets projected from complete and half sequences, then fine-tunes on labeled data. On the PHM 2016 CMP dataset it matches a feature-engineered supervised baseline across most labeled-data fractions, lowers error in selected limited-label settings, and cuts RMSE from 11.23 to 9.32 in the final chronological interval.