| 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)] | 16 Claims |
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1. A sample observation device comprising:
an imaging device; and
a processor executing learning processing for learning a high-picture quality image estimation model and sample observation processing for performing defect detection, wherein
(A) in the learning processing:
(A1) one or more learning defect positions related to a learning sample are acquired;
(A2) a low-picture quality learning image under a first imaging condition is acquired for each of the learning defect positions;
(A3) a first set value related to an imaging count of a high-picture quality learning image is acquired;
(A4) for each of the learning defect positions;
(A4a) the imaging count of the high-picture quality learning image is determined based on the first set value;
(A4b) one or more imaging points as positions where the high-picture quality learning image is captured are determined based on the imaging count determined in (A4a);
(A4c) the high-picture quality learning image under a second imaging condition is acquired for each of the one or more imaging points determined in (A4b);
(A5) the high-picture quality image estimation model is learned using the low-picture quality learning image and the high-picture quality learning image; and
(A6) a defect detection parameter is adjusted using the high-picture quality image estimation model, and
(B) in the sample observation processing, based on the adjusted defect detection parameter:
(B1) a first inspection image of a defect position of an observation target sample is acquired under the first imaging condition; and
(B2) a defect candidate of the observation target sample is detected based on the first inspection image, wherein
the processor in determining the imaging point:
determines, based on a feature quantity of a defect candidate in the low-picture quality learning image, a priority of each of the defect candidates;
selects, based on the priority of each of the defect candidates, a plurality of defect candidates to be imaged under the second imaging condition from a plurality of defect candidates of the low-picture quality learning image so as to be equal to or less than the imaging count; and
determines the imaging point for imaging the plurality of selected defect candidates.
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