Virtual metrology (VM) aims to estimate costly or delayed metrology variables from heterogeneous sensor signals and in-line measurements, and it acts as a critical component of semiconductor manufacturing. While recent data-driven models have improved VM accuracy, most are trained in batch settings and assume stationary data distributions. In practice, however, production environments evolve continuously due to equipment degradation, process adjustments, and maintenance events, making conventional batch-trained VM models difficult to maintain under temporal distribution shifts. In this work, we propose GRAPE (Global Regression with Adaptive Prediction Enhancement), a practical online learning framework for VM in evolving wafer-processing environments. GRAPE integrates a stable global predictor based on interpretable representations with two lightweight streaming adapters that correct feature-level and environment-level shifts through online updates. Experiments on two real-world semiconductor manufacturing datasets, including chronologically held-out blind-test streams, show that GRAPE improves robustness and generalization under real temporal drift compared with both conventional batch-learning methods and existing online learning baselines.

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