Abstract: There is provided a system and method of semiconductor specimen examination. The method includes obtaining a plurality of images of a semiconductor specimen acquired by an examination tool; processing the plurality of images using a first machine learning (ML) model for defect detection, thereby obtaining, from the plurality of images, a set of images labeled with detected defects, wherein the first ML model is previously trained using a first training set comprising a subset of synthetic defective images each containing one or more synthetic defects, and a subset of nominal images; and training a second ML model using a second training set comprising at least part of the set of images labeled with detected defects, wherein the second ML model, upon being trained, is usable for defect detection with improved detection performance with respect to the first ML model.

Abstract: There are provided systems and methods comprising, for each given overlay target of a plurality of different overlay targets to be manufactured on a semiconductor specimen, said given overlay target comprising a plurality of stacked semiconductor layers, obtaining a design image of the given overlay target, feeding the design image to a trained machine learning model, to simulate at least one image of the given overlay target that would have been acquired by an electron beam examination system, using the at least one image to determine, before actual manufacturing of the given overlay target, data informative of at least one simulated overlay in the image, and using the data informative of the at least one simulated overlay of each given overlay target to select at least one optimal overlay target among the plurality of different overlay targets, the optimal overlay target being usable to be manufactured on the semiconductor specimen.

Abstract: There is provided a system and method for examining a semiconductor specimen. The method includes obtaining a runtime image of the specimen, and providing the runtime image as an input to an end-to-end (E2E) learning model to process, thereby obtaining, as an output of the E2E learning model, runtime measurement data specific for a metrology application. The E2E learning model is previously trained for the metrology application using a training set comprising a plurality of training images of the specimen and respective ground truth measurement data associated therewith, and one or more cost functions specifically configured to evaluate, for the plurality of training images and corresponding training measurement data outputted by the E2E learning model, one or more metrology benchmarks from a group comprising precision, correlation, and matching.