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:
Mar. 25, 2025

Filed:

Jun. 15, 2022
Applicant:

Hitachi High-tech Corporation, Tokyo, JP;

Inventors:

Yuki Doi, Tokyo, JP;

Naoaki Kondo, Tokyo, JP;

Minoru Harada, Tokyo, JP;

Hideki Nakayama, Tokyo, JP;

Yohei Minekawa, Tokyo, JP;

Yuji Takagi, Tokyo, JP;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G01N 21/95 (2006.01); G06T 7/73 (2017.01);
U.S. Cl.
CPC ...
G06T 7/001 (2013.01); G01N 21/9505 (2013.01); G06T 7/74 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/20132 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30148 (2013.01); G06T 2207/30168 (2013.01);
Abstract

In learning processing performed before sample observation processing (steps Sto S), the sample observation device acquires a low-picture quality learning image under a first imaging condition for each defect position indicated by defect position information, determines an imaging count of a plurality of high-picture quality learning images associated with the low-picture quality learning image for each defect position and a plurality of imaging points based on a set value of the imaging count, acquires the plurality of high-picture quality learning images under a second imaging condition (step S), learns a high-picture quality image estimation model using the low-picture quality learning image and the plurality of high-picture quality learning images (step S), and adjusts a parameter related to the defect detection in the sample observation processing using the high-picture quality image estimation model (step S).


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