Image inspection equipment and image processing method

Abstract

Provided is an image inspection device which can prevent a degradation in the accuracy of an estimation value of a probability distribution caused by position displacement between design data and a captured image in image processing in which a model that estimates a pixel value probability distribution of a captured image is trained by using design data and the captured image of a sample. The image inspection device inspects a captured image by using design data and the capture image of a sample, the device being characterized by comprising: a training processing unit which trains a probability distribution estimation model that estimates, from the design data, a pixel value probability distribution of the captured image; and an inspection processing unit which inspects a captured image for inspection by using the probability distribution estimation model created in the training processing unit, design data for inspection, and the captured image for inspection, wherein the training processing unit includes: a probability distribution estimation unit which estimates, from sample design data for training, a pixel value probability distribution of a sample captured image for training; a position displacement amount estimation unit which estimates a position displacement amount between a probability distribution during training estimated by the probability distribution estimation unit, and the captured image for training; a position displacement reflection unit which reflects, to the probability distribution during training, the estimation position displacement amount estimated by the position displacement amount estimation unit; and a model update unit which evaluates the probability distribution estimation model of the probability distribution estimation unit by using the captured image for training and the probability distribution during training to which the position displacement calculated by the position displacement reflection unit has been reflected, and updates parameters of the probability distribution estimation model according to the evaluation values.

Dependencies are inferred from the claim text.