opc model validation

**OPC Model Validation** is the **process of verifying that a calibrated OPC model accurately predicts patterning results on features NOT used during calibration** — ensuring the model generalizes beyond its training data to reliably predict CD, profile, and defectivity for arbitrary layout patterns. **Validation Methodology** - **Holdout Set**: Test model predictions on a separate set of features excluded from calibration — cross-validation. - **Validation Structures**: Include 1D (lines/spaces), 2D (line ends, contacts), and complex structures (logic, SRAM). - **Error Metrics**: RMS CD error, max CD error, and systematic bias across feature types — all must be within specification. - **Process Window**: Validate model accuracy across the focus-dose process window, not just at nominal conditions. **Why It Matters** - **Generalization**: A model that fits calibration data but fails on new features is worthless — validation ensures generalization. - **Confidence**: Validated models provide confidence that OPC corrections will be accurate on the production layout. - **Standards**: Industry guidelines (e.g., SEMI) define minimum validation requirements for OPC models. **OPC Model Validation** is **proving the model works on unseen data** — testing OPC model accuracy on independent structures to ensure reliable correction of all layout patterns.