The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 25, 2024

Filed:

Jul. 18, 2020
Applicant:

Camtek Ltd., Migdal Ha'emek, IL;

Inventors:

Isaac Daniel Buzaglo, Migdal Ha'emek, IL;

Nir Dromi, Migdal Ha'emek, IL;

Assignee:

CAMTEK LTD., Migdal Ha'emek, IL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G01N 21/95 (2006.01); G06T 7/90 (2017.01); H01L 21/66 (2006.01);
U.S. Cl.
CPC ...
G06T 7/001 (2013.01); G01N 21/9501 (2013.01); G06T 7/90 (2017.01); H01L 22/12 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30148 (2013.01);
Abstract

The present disclosure provides method and systemfor classifying defects in wafer using wafer defect images, based on deep learning network. Embodiments herein uses synergy between several modalities of the wafer defect images for the classification decision. Further, by adding a mixture of modalities, information may be obtained from different sources such as color image, ICI, the black and white image, to classify the defect image. In addition to mixture of modalities, a reference image may be used for each modality. The reference image of each modality image is provided to deep learning models to concentrate on the defect itself and not on the related underlying lithography of the defect image. Further, the reference image may be provided to the training process of the deep learning models that may significantly reduce the number of labelled images and the training epochs required for convergence of the deep learning model.


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