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:
Jul. 16, 2024

Filed:

Oct. 14, 2020
Applicant:

Pdf Solutions, Inc., Santa Clara, CA (US);

Inventors:

Tomonori Honda, Santa Clara, CA (US);

Richard Burch, McKinney, TX (US);

John Kibarian, Los Altos Hills, CA (US);

Lin Lee Cheong, San Jose, CA (US);

Qing Zhu, Rowlett, TX (US);

Vaishnavi Reddipalli, San Jose, CA (US);

Kenneth Harris, Kaneohe, HI (US);

Said Akar, Miami, FL (US);

Jeffrey D David, San Jose, CA (US);

Michael Keleher, Kirkland, WA (US);

Brian Stine, Palo Alto, CA (US);

Dennis Ciplickas, San Jose, CA (US);

Assignee:

PDF Solutions, Inc., Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 11/07 (2006.01); G06F 18/211 (2023.01); G06F 18/241 (2023.01); G06F 18/40 (2023.01); H01L 21/02 (2006.01); G06F 3/0482 (2013.01); G06N 3/08 (2023.01); G06N 7/01 (2023.01);
U.S. Cl.
CPC ...
G06F 11/079 (2013.01); G06F 11/0736 (2013.01); G06F 11/0751 (2013.01); G06F 11/0778 (2013.01); G06F 18/211 (2023.01); G06F 18/241 (2023.01); G06F 18/40 (2023.01); G06N 20/00 (2019.01); H01L 21/02 (2013.01); G06F 3/0482 (2013.01); G06N 3/08 (2013.01); G06N 7/01 (2023.01);
Abstract

Classifying wafers using Collaborative Learning. An initial wafer classification is determined by a rule-based model. A predicted wafer classification is determined by a machine learning model. Multiple users can manually review the classifications to confirm or modify, or to add user classifications. All of the classifications are input to the machine learning model to continuously update its scheme for detection and classification.


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