machine learning ocd

**ML-OCD** (Machine Learning Optical Critical Dimension) is the **application of machine learning to scatterometry data analysis** — using neural networks, random forests, or other ML models to replace or augment traditional RCWA-based library matching for faster, more robust extraction of structural parameters from optical spectra. **ML-OCD Approaches** - **Direct Regression**: Train a neural network to directly map spectra → geometric parameters — bypass library search. - **Hybrid**: Use ML for initial parameter estimation, then refine with physics-based regression. - **Virtual Metrology**: Train ML models to predict reference measurements (CD-SEM, TEM) from OCD spectra. - **Transfer Learning**: Pre-train on simulation data, fine-tune on real measurement data for domain adaptation. **Why It Matters** - **Speed**: ML inference is orders of magnitude faster than RCWA library computation — real-time parameter extraction. - **Complex Structures**: ML can handle structures too complex for tractable RCWA libraries — high-dimensional parameter spaces. - **Robustness**: ML can learn to ignore systematic errors that confuse physics-based models — data-driven robustness. **ML-OCD** is **AI-powered scatterometry** — using machine learning for faster, more robust extraction of critical dimensions from optical measurements.