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.