Active learning-based defect location identification

An apparatus for identifying locations to inspect on a first substrate using a machine learning model and for training the machine learning model to identify locations to inspect on a second substrate based on inspection results of the locations on the first substrate, the apparatus comprising:

• a memory storing a set of instructions;
• and
• at least one processor configured to execute the set of instructions to cause the apparatus to at least:
• input process-related data associated with a substrate to a defect location prediction model;
• generate, using the defect location prediction model, a prediction of defect or non-defect for each of a plurality of locations on the substrate, wherein each prediction is associated with a confidence score that is indicative of a confidence in the prediction for the corresponding location;
• add each of the locations for which the confidence score satisfies a confidence threshold to a set of locations to be inspected by an inspection system;
• obtain inspection results data for the set of locations from the inspection system;
• and
• input the inspection results data and process-related data for the set of locations to the defect location prediction model for training the defect location prediction model.

A method comprising:

• inputting process-related data associated with a substrate to a defect location prediction model;
• generating, using the defect location prediction model, a prediction of a defect or non-defect for each of a plurality of locations on the substrate, wherein each prediction is associated with a confidence score that is indicative of a confidence in the prediction for the corresponding location;
• adding each of the locations for which the confidence score satisfies a confidence threshold to a set of locations to be inspected by an inspection system;
• obtaining inspection results data for the set of locations from the inspection system;
• and
• inputting the inspection results data and process-related data for the set of locations to the defect location prediction model for training the defect location prediction model.