Abstract: A scanning electron microscope (SEM) is provided, configured to evaluate one or more properties of a sample comprising a plurality of repeating features. The SEM comprises a scanning arrangement to scan a site on the sample by irradiating it with source particles and detecting resulting signals, and a controller to operate the SEM and evaluate properties at the site based on the signals. The scanning arrangement scans equivalent sites of interest on the features for a scan-duration which is less than an acquisition-duration necessary for evaluation of a property of interest. The sum of the scan-durations is no less than the acquisition-duration. The controller aggregates measurements of the detected signals produced by each of the equivalent sites of interest, and evaluates one or more predetermined properties of the sites of interest on the plurality of repeating features based at least partially on the aggregated measurements.

Abstract: There is provided a system and method of runtime defect examination of a semiconductor specimen, comprising obtaining a first image representative of at least part of the semiconductor specimen, the first image acquired by an examination tool configured with a first focus plane; estimating whether the first image is in focus using a machine learning (ML) model, wherein the ML model is previously trained for classifying images into focused images and defocused images; upon an estimation that the first image is out of focus, performing focus calibration on the examination tool to select a second focus plane associated with an optimal focus score; and obtaining a second image acquired by the examination tool configured with the second focus plane, and estimating whether the second image is in focus using the ML model. The second image, upon being estimated as being in focus, is usable for defect examination on the specimen.

Abstract: There is provided a system and method of runtime defect examination of a semiconductor specimen, comprising obtaining a first image representative of at least part of the semiconductor specimen, the first image acquired by an examination tool configured with a first focus plane; estimating whether the first image is in focus using a machine learning (ML) model, wherein the ML model is previously trained for classifying images into focused images and defocused images; upon an estimation that the first image is out of focus, performing focus calibration on the examination tool to select a second focus plane associated with an optimal focus score; and obtaining a second image acquired by the examination tool configured with the second focus plane, and estimating whether the second image is in focus using the ML model. The second image, upon being estimated as being in focus, is usable for defect examination on the specimen.