predictive metrology

**Predictive Metrology** is a **forward-looking approach that uses historical data, process models, and machine learning to predict future metrology outcomes** — forecasting equipment drift, process trends, and potential excursions before they occur, enabling proactive (not reactive) process control. **Approaches to Predictive Metrology** - **Time-Series Forecasting**: Predict parameter drift from historical trends (ARIMA, LSTM models). - **Physics-Informed ML**: Combine process physics models with data-driven predictions. - **Digital Twin**: Maintain a simulation model of the process that is continuously updated with real data. - **Anomaly Prediction**: Detect early warning signatures that precede excursions. **Why It Matters** - **Proactive Control**: Adjust before the process goes out of spec, not after the wafers are scrapped. - **Maintenance Scheduling**: Predict when equipment needs maintenance based on measurement trends. - **Yield Improvement**: Earlier detection of drift trends improves yield by preventing out-of-spec production. **Predictive Metrology** is **the crystal ball for semiconductor manufacturing** — forecasting process trends to enable proactive rather than reactive quality control.