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, 2019

Filed:

Oct. 15, 2018
Applicant:

Asml Netherlands B.v., Veldhoven, NL;

Inventors:

Scott Anderson Middlebrooks, Veldhoven, NL;

Niels Geypen, Veldhoven, NL;

Hendrik Jan Hidde Smilde, Veldhoven, NL;

Alexander Straaijer, Veldhoven, NL;

Maurits Van Der Schaar, Veldhoven, NL;

Markus Gerardus Martinus Maria Van Kraaij, Veldhoven, NL;

Assignee:

ASML Netherlands B.V., Veldhoven, NL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G03F 7/20 (2006.01);
U.S. Cl.
CPC ...
G03F 7/70633 (2013.01);
Abstract

Disclosed is a method of measuring a parameter of a lithographic process, and associated inspection apparatus. The method comprises measuring at least two target structures on a substrate using a plurality of different illumination conditions, the target structures having deliberate overlay biases; to obtain for each target structure an asymmetry measurement representing an overall asymmetry that includes contributions due to (i) the deliberate overlay biases, (ii) an overlay error during forming of the target structure and (iii) any feature asymmetry. A regression analysis is performed on the asymmetry measurement data by fitting a linear regression model to a planar representation of asymmetry measurements for one target structure against asymmetry measurements for another target structure, the linear regression model not necessarily being fitted through an origin of the planar representation. The overlay error can then be determined from a gradient described by the linear regression model.


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