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:
May. 28, 2024

Filed:

Feb. 26, 2020
Applicant:

Asml Netherlands B.v., Veldhoven, NL;

Inventors:

Alexandru Onose, Eindhoven, NL;

Remco Dirks, Deurne, NL;

Roger Hubertus Elisabeth Clementine Bosch, Mierlo, NL;

Sander Silvester Adelgondus Marie Jacobs, Eindhoven, NL;

Frank Jaco Buijnsters, Eindhoven, NL;

Siebe Tjerk De Zwart, Valkenswaard, NL;

Artur Palha Da Silva Clerigo, Eindhoven, NL;

Nick Verheul, Den Bosch, NL;

Assignee:

ASML NETHERLANDS B.V., Veldhoven, NL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G03F 7/00 (2006.01);
U.S. Cl.
CPC ...
G03F 7/705 (2013.01); G03F 7/70625 (2013.01); G03F 7/70633 (2013.01); G03F 7/706841 (2023.05); G03F 7/70616 (2013.01);
Abstract

A method, computer program and associated apparatuses for metrology. The method includes determining a reconstruction recipe describing at least nominal values for use in a reconstruction of a parameterization describing a target. The method includes obtaining first measurement data relating to measurements of a plurality of targets on at least one substrate, the measurement data relating to one or more acquisition settings and performing an optimization by minimizing a cost function which minimizes differences between the first measurement data and simulated measurement data based on a reconstructed parameterization for each of the plurality of targets. A constraint on the cost function is imposed based on a hierarchical prior. Also disclosed is a hybrid model method comprising obtaining a coarse model operable to provide simulated coarse data; and training a data driven model to correct the simulated coarse data so as to determine simulated data for use in reconstruction.


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