SEMICONDUCTOR METROLOGY SYSTEM AND METHOD

A machine learning system and method for optical critical dimension measurement. From a training set of spectra and references, features are extracted and subjected to regression analysis to generate predictor variables. Using feature functions, inverse feature functions, a machine-learning predictor component and masks, a machine-learning optical critical dimension explainer is generated. A wafer is analyzed by metrology tools and the machine-learning predictor component calculates a critical dimension inference from measured spectra. Theoretical spectra are then generated by the predictor component based upon a modification of the critical dimension inference. The measured spectra are compared to the theoretical spectra and the fit of the measured spectra to the theoretical spectra is evaluated for acceptance. The results of the comparison and analysis is output in human readable form.