公开(公告)号：US11631179B2

公开(公告)日：2023-04-18

申请号：US16917692

申请日：2020-06-30

Applicant: Applied Materials Israel Ltd.

Inventor： Elad Ben Baruch ,  Shalom Elkayam ,  Shaul Cohen ,  Tal Ben-Shlomo

IPC: G06N20/00 ,  G06T7/11 ,  G06T7/187 ,  G06F17/18 ,  G06F30/27

Abstract: There is provided a system and method of segmenting an image of a fabricated semiconductor specimen. The method includes: obtaining a first probability map corresponding to the image representative of at least a portion of the fabricated semiconductor specimen and indicative of predicted probabilities of pixels in the image to correspond to one or more first structural elements presented in the image, obtaining a first label map informative of one or more segments representative of second structural elements and labels associated with the segments, performing simulation on the first label map to obtain a second probability map indicative of simulated probabilities of pixels in the first label map to correspond to the one or more segments, and generating a second label map based on the first probability map and the second probability map, the second label map being usable for segmentation of the image with enhanced repeatability.

公开(公告)号：US11232550B2

公开(公告)日：2022-01-25

申请号：US16916047

申请日：2020-06-29

Applicant: Applied Materials Israel Ltd.

Inventor： Elad Ben Baruch ,  Shalom Elkayam ,  Shaul Cohen ,  Tal Ben-Shlomo

IPC: G06T7/00 ,  G06K9/62 ,  G06T7/143

Abstract: There is provided a system and method of generating a training set for training a Deep Neural Network usable for examination of a specimen. The method includes: for each given training image in a group: i) generating a first batch of training patches, including cropping the given training image into a first plurality of original patches; and augmenting at least part of the first plurality of original patches in order to simulate variations caused by a physical process of the specimen; and ii) generating a second batch of training patches, including: shifting the plurality of first positions on the given training image to obtain a second plurality of original patches, and repeating the augmenting to the second plurality of original patches to generate a second plurality of augmented patches; and including at least the first second batches of training patches corresponding to each given training image in the training set.