Optical inspection-based automatic defect classification

Abstract

Implementations disclosed describe, among other things, a systems and techniques for perform efficient inspection of a semiconductor manufacturing sample. The techniques include collecting optical inspection data for training sample(s) that have a plurality of defects. The techniques further include generating, using the optical inspection data, a training data set that includes descriptions, images, and ground truth classifications for the defects. The techniques further include using the training data set to train a plurality of machine learning (ML) classifiers to generate predicted classifications for the defects in the training sample(s). The techniques further include selecting, using the predicted classifications and the ground truth classifications, one or more ML classifiers that meet one or more accuracy criteria, and using the selected ML classifier(s) to classify defects in the semiconductor manufacturing sample.