ML-OCD (Machine Learning-Based Optical Critical Dimension) is a scatterometry approach that uses machine learning models trained on simulated or measured spectra — replacing traditional library matching or regression with neural networks, Gaussian processes, or other ML models for faster, more robust CD extraction.

How Does ML-OCD Work?

• Training Data: Generate a large synthetic dataset using RCWA simulations (parameter → spectrum pairs).
• Model Training: Train a neural network (or other ML model) to predict parameters from spectra.
• Inference: The trained model predicts CD, height, SWA from a measured spectrum in microseconds.
• Uncertainty: Bayesian ML methods provide prediction confidence intervals.

Why It Matters

• Speed: Inference in microseconds — faster than both library matching and regression.
• Robustness: ML models handle noise, systematic errors, and model imperfections better than exact matching.
• Complex Structures: Can handle structures too complex for traditional library/regression approaches (GAA, CFET).

ML-OCD is AI-powered dimensional metrology — using machine learning to extract nanoscale dimensions from optical spectra faster and more robustly.