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Patent Agency Ranking
公开(公告)号：US12183066B2
公开(公告)日：2024-12-31
申请号：US17521499
申请日：2021-11-08
Applicant: Applied Materials Israel Ltd.
Inventor： Leonid Karlinsky , Boaz Cohen , Idan Kaizerman , Efrat Rosenman , Amit Batikoff , Daniel Ravid , Moshe Rosenweig
IPC: G06N3/08 , G06F18/2413 , G06N3/045 , G06V10/44 , G06V10/764 , G06V10/82 , G06V10/98
Abstract: A computerized system and method of training a deep neural network (DNN) is provided. The DNN is trained in a first training cycle using a first training set including first training samples. Each first training sample includes at least one first training image synthetically generated based on design data. Upon receiving a user feedback with respect to the DNN trained using the first training set, a second training cycle is adjusted based on the user feedback by obtaining a second training set including augmented training samples. The DNN is re-trained using the second training set. The augmented training samples are obtained by augmenting at least part of the first training samples using defect-related synthetic data. The trained DNN is usable for examination of a semiconductor specimen.
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公开(公告)号：US09715724B2
公开(公告)日：2017-07-25
申请号：US14446295
申请日：2014-07-29
Applicant: Applied Materials Israel Ltd.
Inventor： Ishai Schwarzband , Yan Ivanchenko , Daniel Ravid , Orly Zvitia , Idan Kaizerman
CPC classification number: G06T7/001 , G06K9/4604 , G06K9/6211 , G06K2209/19 , G06K2209/403 , G06T2207/10028 , G06T2207/10061 , G06T2207/30148
Abstract: A method for image processing includes providing a microscopic image of a structure fabricated on a substrate and computer-aided design (CAD) data used in fabricating the structure. The microscopic image is processed by a computer so as to generate a first directionality map, which includes, for a matrix of points in the microscopic image, respective directionality vectors corresponding to magnitudes and directions of edges at the points irrespective of a sign of the magnitudes. The CAD data are processed by the computer so as to produce a simulated image based on the CAD data and to generate a second directionality map based on the simulated image. The first and second directionality maps are compared by the computer so as to register the microscopic image with the CAD data.
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公开(公告)号：US11348001B2
公开(公告)日：2022-05-31
申请号：US15675477
申请日：2017-08-11
Applicant: Applied Materials Israel Ltd.
Inventor： Leonid Karlinsky , Boaz Cohen , Idan Kaizerman , Efrat Rosenman , Amit Batikoff , Daniel Ravid , Moshe Rosenweig
Abstract: There are provided system and method of classifying defects in a semiconductor specimen. The method comprises: upon obtaining by a computer a Deep Neural Network (DNN) trained to provide classification-related attributes enabling minimal defect classification error, processing a fabrication process (FP) sample using the obtained trained DNN; and, resulting from the processing, obtaining by the computer classification-related attributes characterizing the at least one defect to be classified, thereby enabling automated classification, in accordance with the obtained classification-related attributes, of the at least one defect presented in the FP image. The DNN is trained using a classification training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprising a training image presenting at least one defect and the ground truth data is informative of classes and/or class distribution of defects presented in the respective first training samples; the FP sample comprises a FP image presenting at least one defect to be classified.
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公开(公告)号：US11205119B2
公开(公告)日：2021-12-21
申请号：US15384058
申请日：2016-12-19
Applicant: Applied Materials Israel Ltd.
Inventor： Leonid Karlinsky , Boaz Cohen , Idan Kaizerman , Efrat Rosenman , Amit Batikoff , Daniel Ravid , Moshe Rosenweig
Abstract: There are provided system and method of examining a semiconductor specimen. The method comprises: upon obtaining a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process, processing together one or more fabrication process (FP) images using the obtained trained DNN, wherein the DNN is trained using a training set comprising ground truth data specific for the given application; and obtaining examination-related data specific for the given application and characterizing at least one of the processed one or more FP images. The examination-related application can be, for example, classifying at least one defect presented by at least one FP image, segmenting the at least one FP image, detecting defects in the specimen presented by the at least one FP image, registering between at least two FP images, regression application enabling reconstructing the at least one FP image in correspondence with different examination modality, etc.
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公开(公告)号：US11010665B2
公开(公告)日：2021-05-18
申请号：US15668623
申请日：2017-08-03
Applicant: Applied Materials Israel Ltd.
Inventor： Leonid Karlinsky , Boaz Cohen , Idan Kaizerman , Efrat Rosenman , Amit Batikoff , Daniel Ravid , Moshe Rosenweig
Abstract: There are provided system and method of segmentation a fabrication process (FP) image obtained in a fabrication of a semiconductor specimen. The method comprises: upon obtaining a Deep Neural Network (DNN) trained to provide segmentation-related data, processing a fabrication process (FP) sample using the obtained trained DNN and, resulting from the processing, obtaining by the computer segments-related data characterizing the FP image to be segmented, the obtained segments-related data usable for automated examination of the semiconductor specimen. The DNN is trained using a segmentation training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprises a training image; FP sample comprises the FP image to be segmented.
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