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. 06, 2017

Filed:

Apr. 07, 2014
Applicant:

Asml Netherlands B.v., Veldhoven, NL;

Inventors:

Yu Cao, Saratoga, CA (US);

Wenjin Shao, Sunnyvale, CA (US);

Jun Ye, Palo Alto, CA (US);

Ronaldus Johannes Gijsbertus Goossens, Los Altos, CA (US);

Assignee:

ASML NETHERLANDS B.V., Veldhoven, NL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/50 (2006.01); G03F 1/00 (2012.01); G03F 1/44 (2012.01); G03F 7/20 (2006.01); G06F 17/10 (2006.01); G03F 1/68 (2012.01);
U.S. Cl.
CPC ...
G06F 17/50 (2013.01); G03F 1/14 (2013.01); G03F 1/44 (2013.01); G03F 1/68 (2013.01); G03F 7/705 (2013.01); G03F 7/70433 (2013.01); G06F 17/10 (2013.01); G06F 17/5009 (2013.01);
Abstract

The present invention relates generally to methods and apparatuses for test pattern selection for computational lithography model calibration. According to some aspects, the pattern selection algorithms of the present invention can be applied to any existing pool of candidate test patterns. According to some aspects, the present invention automatically selects those test patterns that are most effective in determining the optimal model parameter values from an existing pool of candidate test patterns, as opposed to designing optimal patterns. According to additional aspects, the selected set of test patterns according to the invention is able to excite all the known physics and chemistry in the model formulation, making sure that the wafer data for the test patterns can drive the model calibration to the optimal parameter values that realize the upper bound of prediction accuracy imposed by the model formulation.


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