robustness testing

**Robustness Testing** is the **systematic evaluation of whether a model maintains accurate predictions when inputs are perturbed, corrupted, or shifted** — measuring the model's stability and reliability under realistic variations that it will encounter in production. **Robustness Test Categories** - **Input Perturbation**: Small changes to inputs (noise, rounding, sensor drift) should not change predictions significantly. - **Corruption**: Missing values, outliers, and sensor failures should be handled gracefully. - **Distribution Shift**: Performance on data from different tools, time periods, or process conditions. - **Adversarial**: Worst-case perturbations that maximally degrade model performance. **Why It Matters** - **Reliability**: A model that fails with minor input perturbations is unreliable for production use. - **Sensor Noise**: Real-world fab data always contains noise — robustness to noise is essential. - **Confidence**: Robustness testing builds confidence that the model will perform well under real operating conditions. **Robustness Testing** is **testing for the real world** — verifying that models maintain performance amid the noise, drift, and variations of production.