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:
Apr. 20, 2021

Filed:

Aug. 13, 2018
Applicant:

Kla-tencor Corporation, Milpitas, CA (US);

Inventors:

John Hench, Los Gatos, CA (US);

Antonio Arion Gellineau, Santa Clara, CA (US);

Alexander Kuznetsov, Austin, TX (US);

Assignee:

KLA-Tencor Corporation, Milpitas, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G01T 1/24 (2006.01); G01N 21/47 (2006.01); G01N 21/95 (2006.01); G01N 23/201 (2018.01);
U.S. Cl.
CPC ...
G01T 1/24 (2013.01); G01N 21/47 (2013.01); G01N 21/9501 (2013.01); G01N 23/201 (2013.01); G01N 2021/4735 (2013.01);
Abstract

Methods and systems for more efficient X-Ray scatterometry measurements of on-device structures are presented herein. X-Ray scatterometry measurements of one or more structures over a measurement area includes a decomposition of the one or more structures into a plurality of sub-structures, a decomposition of the measurement area into a plurality of sub-areas, or both. The decomposed structures, measurement areas, or both, are independently simulated. The scattering contributions of each of the independently simulated decomposed structures are combined to simulate the actual scattering of the measured structures within the measurement area. In a further aspect, measured intensities and modelled intensities including one or more incidental structures are employed to perform measurement of structures of interest. In other further aspects, measurement decomposition is employed to train a measurement model and to optimize a measurement recipe for a particular measurement application.


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