Scanning electron microscope image processing using neural network

A system comprising:

• at least one processor;
• and
• a non-transitory storage medium storing instructions configured to cause the at least one processor to perform image processing when the instructions are executed by the at least one processor, wherein the image processing includes:
• obtaining N scanning electron microscope (SEM) images from an SEM with respect to an analysis target;
• selecting M target images from the N SEM images, based on frequencies respectively corresponding to the N SEM images, wherein the selecting of the M target images includes:
• generating N frequency images by converting time domain information of each of the N SEM images into frequency domain information,
• setting a zero-padding area in each of the N frequency images and substituting zero for frequency values respectively corresponding to pixels of the zero-padding area,
• generating a calculation value by calculating an average of frequency values respectively corresponding to pixels of an area other than the zero-padding area in each of the N frequency images, and
• determining whether the calculation value is greater than or equal to a preset threshold value and selecting, as one of the M target images, an SEM image based on the calculation value;
• cropping each of the M target images into K cropped images;
• generating M×K crop detection images by inferring a target object, to which each of pixels of M×K cropped images belongs, by using a segmentation model trained to detect the target object;
• and
• generating M predicted images by merging the M×K crop detection images with each other based on position information of the M×K cropped images, wherein each of the M predicted images has the same size as the N SEM images.

An operating method of a neural network device, the operating method comprising:

• obtaining scanning electron microscope (SEM) images from an SEM with respect to an analysis target;
• selecting target images from the SEM images, based on frequencies respectively corresponding to the SEM images, wherein the selecting of the target images includes:
• generating frequency images by converting time domain information of each of the SEM images into frequency domain information,
• setting a plurality of zero-padding areas of different sizes in each of the frequency images and generating calculation values respectively corresponding to the plurality of zero-padding areas,
• calculating an average calculation value for each of the frequency images by calculating an average of the calculation values respectively corresponding to the plurality of zero-padding areas, and
• selecting, as one of the target images, an SEM image corresponding to a frequency image for which the average calculation value is greater than or equal to a preset threshold value;
• cropping each of the target images into a plurality of cropped images;
• generating crop detection images by inferring a target object from each of the plurality of cropped images by using a segmentation model trained to detect the target object in each of the plurality of cropped images;
• and
• merging the crop detection images with each other to generate a merged image having the same size as the SEM images, based on position information of the plurality of cropped images.