公开(公告)号：US20230206417A1

公开(公告)日：2023-06-29

申请号：US17565273

申请日：2021-12-29

Applicant: Applied Materials Israel Ltd.

Inventor： Elad COHEN ,  Victor EGOROV ,  Ilan BEN-HARUSH ,  Rafael BISTRITZER

IPC: G06T7/00 ,  G06T7/10

CPC classification number: G06T7/0004 ,  G06T7/10

Abstract: There is provided a system of examination of a semiconductor specimen, comprising a processor and memory circuitry configured to obtain, for each given candidate defect of a plurality of candidate defects in an image of the specimen, a given area of the given candidate defect in the image, obtain a reference image, perform a segmentation of at least part of the reference image, to determine, for each given candidate defect, first reference areas in the reference image matching a given reference area corresponding to the given area, select among the first reference areas, a plurality of second reference areas, obtain a plurality of corresponding second areas in the image, and use data informative of a pixel intensity of the second areas and data informative of a pixel intensity of the given area to determine whether the given candidate defect corresponds to a defect.

公开(公告)号：US20250166154A1

公开(公告)日：2025-05-22

申请号：US18518025

申请日：2023-11-22

Applicant: Applied Materials Israel Ltd.

Inventor： Sergey KHRISTO ,  Shih-Wei YANG ,  Denis SIMAKOV ,  Elad COHEN ,  Victor EGOROV

IPC: G06T7/00

Abstract: There are provided systems and methods comprising obtaining a first inspection image informative of a first area of a specimen acquired by an examination tool, feeding at least the first inspection image to a machine learning algorithm configured to determine, for each given pixel of a plurality of pixels of the first inspection image, or for each given group of pixels of a plurality of groups of pixels of the first inspection image, one or more given parameters of a given model informative of pixel intensity distribution, for said each given pixel or given group of pixels, using at least some of the one or more given parameters, or the given model associated with the one or more given parameters, and measured pixel intensity of the given pixel or group of pixels, to determine whether a defect is present in the given pixel or in the given group of pixels.