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. 10, 2026

Filed:

Dec. 20, 2022
Applicant:

Stmicroelectronics International N.v., Geneva, CH;

Inventors:

Francesco Rundo, Gravina di Catania, IT;

Salvatore Coffa, Milan, IT;

Riccardo Emanuele Sarpietro, Paternò, IT;

Concetto Spampinato, Catania, IT;

Paola Carmelina Giuffre′, Valverde, IT;

Giuseppe Randazzo, Catania, IT;

Marco Stefano Scroppo, Catania, IT;

Daniele Riccardo Vinciguerra, Catania, IT;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06V 10/74 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01);
U.S. Cl.
CPC ...
G06T 7/001 (2013.01); G06V 10/761 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 20/70 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30148 (2013.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01);
Abstract

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities for generating classification predictions for wafer defect maps. Reduced feature data may be generated, using a non-linear dimensionality reduction machine learning model and based at least in part on vector representations for a set of wafer defect map images. One or more wafer defect pattern clusters may be generated, using a density-based clustering machine learning model and based at least in part on the reduced feature data. Each wafer defect map image may be associated with a particular wafer defect pattern cluster of the one or more wafer defect pattern clusters. A classification prediction may be generated for each wafer defect map image based at least in part on the particular wafer defect pattern cluster associated with the respective wafer defect map image.


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