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:
Feb. 28, 2023

Filed:

Feb. 14, 2020
Applicant:

Applied Materials, Inc., Santa Clara, CA (US);

Inventors:

Sidharth Bhatia, Santa Cruz, CA (US);

Garrett H. Sin, Sunnyvale, CA (US);

Heng-Cheng Pai, Cupertino, CA (US);

Pramod Nambiar, Santa Clara, CA (US);

Ganesh Balasubramanian, Fremont, CA (US);

Irfan Jamil, Santa Clara, CA (US);

Assignee:

Applied Materials, Inc., Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 23/02 (2006.01); G05B 19/418 (2006.01); G06F 11/34 (2006.01); G06F 11/30 (2006.01); G05B 19/404 (2006.01); G06N 20/00 (2019.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G05B 23/0286 (2013.01); G05B 19/404 (2013.01); G05B 19/4183 (2013.01); G05B 19/4184 (2013.01); G05B 19/41875 (2013.01); G05B 19/41885 (2013.01); G05B 23/024 (2013.01); G05B 23/0294 (2013.01); G06F 11/3089 (2013.01); G06F 11/3447 (2013.01); G06F 11/3466 (2013.01); G06F 11/3495 (2013.01); G05B 2219/31357 (2013.01); G05B 2219/32194 (2013.01); G05B 2219/32201 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01);
Abstract

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.


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