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:
Oct. 08, 2024

Filed:

Sep. 01, 2023
Applicant:

Carl Zeiss Smt Gmbh, Oberkochen, DE;

Inventors:

Alexander Freytag, Erfurt, DE;

Christoph Husemann, Jena, DE;

Dirk Seidel, Jena-Leutra, DE;

Carsten Schmidt, Jena, DE;

Assignee:

Carl Zeiss SMT GmbH, Oberkochen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01J 3/00 (2006.01); G03F 7/00 (2006.01); G06F 30/20 (2020.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01); G06V 10/75 (2022.01); G06V 10/774 (2022.01); G06V 20/13 (2022.01);
U.S. Cl.
CPC ...
G03F 7/705 (2013.01); G06F 30/20 (2020.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 7/0006 (2013.01); G06T 7/001 (2013.01); G06V 10/751 (2022.01); G06V 10/774 (2022.01); G06V 20/13 (2022.01); G06T 2207/10032 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30148 (2013.01);
Abstract

The present invention relates to a method and an apparatus for determining at least one unknown effect of defects of an element of a photolithography process. The method comprises the steps of: (a) providing a model of machine learning for a relationship between an image, design data associated with the image and at least one effect of the defects of the element of the photolithography process arising from the image; (b) training the model of machine learning using a multiplicity of images used for training purposes, design data associated with the images used for training purposes and corresponding effects of the defects; and (c) determining the at least one unknown effect of the defects by applying the trained model to a measured image and the design data associated with the measured image.


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