Automatic optimization of an examination recipe

A computerized method of examining a semiconductor specimen, the method comprising:

• obtaining inspection data of a given layer of a semiconductor specimen acquired by an inspection tool during runtime examination, the inspection data comprising a plurality of inspection images representative of a plurality of defect candidates from a defect map of the given layer;
• extracting a first set of inspection features characterizing the plurality of inspection images;
• and
• using a classifier to classify the plurality of defect candidates based on the first set of inspection features, giving rise to a list of defect candidates having a higher probability of being defects of interest (DOIs), wherein the semiconductor specimen comprises multiple layers, and the classifier is a general-purpose classifier (GPC) usable for runtime classification of inspection data from any layer of the multiple layers of the semiconductor specimen, the GPC being previously trained using training data comprising a second set of inspection features characterizing training inspection images of various types of DOIs and nuisances collected from the multiple layers and label data associated therewith.

A non-transitory computer readable storage medium tangibly embodying a program of instructions that, when executed by a computer, cause the computer to perform a method of examining a semiconductor specimen, the method comprising:

• obtaining inspection data of a given layer of a semiconductor specimen acquired by an inspection tool during runtime examination, the inspection data comprising a plurality of inspection images representative of a plurality of defect candidates from a defect map of the given layer;
• extracting a first set of inspection features characterizing the plurality of inspection images;
• and
• using a classifier to classify the plurality of defect candidates based on the first set of inspection features, giving rise to a list of defect candidates having a higher probability of being defects of interest (DOIs), wherein the semiconductor specimen comprises multiple layers, and the classifier is a general-purpose classifier (GPC) usable for runtime classification of inspection data from any layer of the multiple layers of the semiconductor specimen, the GPC being previously trained using training data comprising a second set of inspection features characterizing training inspection images of various types of DOIs and nuisances collected from the multiple layers and label data associated therewith.