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:
Mar. 11, 2025

Filed:

May. 23, 2022
Applicant:

Synopsys, Inc., Mountain View, CA (US);

Inventors:

Dereje Shewaseged Woldeamanual, Munich, DE;

Thomas Heribert Mülders, Erding, DE;

Jiuzhou Tang, Munich, DE;

Rainer Zimmermann, Sauerlach, DE;

Robert Marshall Lugg, Portland, OR (US);

Hans-Jürgen Stock, Dachau, DE;

Georg Albert Viehöver, Höhenkirchen-Siegertsbrunn, DE;

Assignee:

Synopsys, Inc., Sunnyvale, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G03F 7/00 (2006.01); G06N 5/04 (2023.01); G06V 10/46 (2022.01); G06V 10/75 (2022.01);
U.S. Cl.
CPC ...
G06V 10/755 (2022.01); G03F 7/705 (2013.01); G06N 5/04 (2013.01); G06V 10/469 (2022.01);
Abstract

A computational lithography process uses machine learning models. An aerial image produced by a lithographic mask is first calculated using a two-dimensional model of the lithographic mask. This first aerial image is applied to a first machine learning model, which infers a second aerial image. The first machine learning model was trained using a training set that includes aerial images calculated using a more accurate three-dimensional model of lithographic masks. The two-dimensional model is faster to compute than the three-dimensional model but it is less accurate. The first machine learning model mitigates this inaccuracy.


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