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公开(公告)号：US11568531B2
公开(公告)日：2023-01-31
申请号：US16892123
申请日：2020-06-03
Applicant: APPLIED MATERIALS ISRAEL LTD.
Inventor： Ohad Shaubi , Denis Suhanov , Assaf Asbag , Boaz Cohen
Abstract: There is provided a method of examination of a semiconductor specimen and a system thereof. The method comprises: using a trained Deep Neural Network (DNN) to process a fabrication process (FP) sample, wherein the FP sample comprises first FP image(s) received from first examination modality(s) and second FP image(s) received from second examination modality(s) which differs from the first examination modality(s), and wherein the trained DNN processes the first FP image(s) separately from the second FP image(s); and further processing by the trained DNN the results of such separate processing to obtain examination-related data specific for the given application and characterizing at least one of the processed FP images. When the FP sample further comprises numeric data associated with the FP image(s), the method further comprises processing by the trained DNN at least part of the numeric data separately from processing the first and the second FP images.
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公开(公告)号：US11037286B2
公开(公告)日：2021-06-15
申请号：US15719447
申请日：2017-09-28
Applicant: Applied Materials Israel Ltd.
Inventor： Assaf Asbag , Ohad Shaubi , Kirill Savchenko , Shiran Gan-Or , Boaz Cohen , Zeev Zohar
Abstract: There are provided a classifier and a method of classifying defects in a semiconductor specimen. The classifier enables assigning each class to a classification group among three or more classification groups with different priorities. Classifier further enables setting purity, accuracy and/or extraction requirements separately for each class, and optimizing the classification results in accordance with per-class requirements. During training, the classifier is configured to generate a classification rule enabling the highest possible contribution of automated classification while meeting per-class quality requirements defined for each class.
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公开(公告)号：US10921334B2
公开(公告)日：2021-02-16
申请号：US15933306
申请日：2018-03-22
Applicant: Applied Materials Israel Ltd.
Inventor： Kirill Savchenko , Assaf Asbag , Boaz Cohen
Abstract: An examination system, a method of obtaining a training set for a classifier, and a non-transitory computer readable medium, the method comprising: upon receiving in a memory device object inspection results comprising data indicative of potential defects, each potential defect of the potential defects associated with a multiplicity of attribute values defining a location of the potential defect in an attribute space: sampling by the processor a first set of defects from the potential defects, wherein the defects within the first set are dispersed independently of a density of the potential defects in the attribute space; and obtaining by the processor a training defect sample set comprising the first set of defects and data or parameters representative of the density of the potential defects in the attribute space.
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公开(公告)号：US11138507B2
公开(公告)日：2021-10-05
申请号：US15719433
申请日：2017-09-28
Applicant: Applied Materials Israel Ltd.
Inventor： Assaf Asbag , Boaz Cohen
Abstract: A system, method and computer software product, the system capable of classifying defects and comprising: an hardware-based GUI component; and a processing and memory circuitry configured to: a. upon obtaining data informative of a plurality of defects and attribute values thereof, using the attribute values to create initial classification of the plurality of defects into a plurality of classes; b. for a given class, presenting to a user, by the hardware-based GUI component, an image of a defect initially classified to the given class with a low likelihood, wherein the image is presented along with images of one or more defects initially classified to the given class with the highest likelihood; and c. subject to confirming by the user, using the hardware-based GUI component, that the at least one defect is to be classified to the given class, indicating the at least one defect as belonging to the given class.
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5.发明授权公开(公告)号：US10360669B2
公开(公告)日：2019-07-23
申请号：US15685974
申请日：2017-08-24
Applicant: APPLIED MATERIALS ISRAEL LTD.
Inventor： Ohad Shaubi , Assaf Asbag , Idan Kaizerman
Abstract: There are provided a system, computer software product and method of generating a training set for a classifier using a processor. The method comprises: receiving a training set comprising training defects each having assigned attribute values, the training defects externally classified into classes comprising first and second major classes and a minor class; training a classifier upon the training set; receiving results of automatic classification of the training defects; automatically identifying a first defect that was externally classified into the first major class and automatically classified into the second major class; automatically identifying by the processor a second defect from the multiplicity of training defects that was externally classified into the minor class and automatically classified to the first or second major classes; and correcting the training set to include the first defect into the second major class, or to include the second defect into the first or the second major class.
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Patent Agency Ranking
公开(公告)号：US11526979B2
公开(公告)日：2022-12-13
申请号：US16993869
申请日：2020-08-14
Applicant: Applied Materials Israel, Ltd.
Inventor： Assaf Asbag , Orly Zvitia , Idan Kaizerman , Efrat Rosenman
Abstract: There are provided system and method of classifying defects in a specimen. The method includes: obtaining one or more defect clusters detected on a defect map of the specimen, each cluster characterized by a set of cluster attributes comprising spatial attributes including spatial density indicative of density of defects in one or more regions accommodating the cluster, each given defect cluster being detected at least based on the spatial density thereof meeting a criterion. The defect map also comprises non-clustered defects. Defects of interest (DOI) are identified in each cluster by performing respective defect filtrations for each cluster and non-clustered defects.
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公开(公告)号：US11321633B2
公开(公告)日：2022-05-03
申请号：US16228676
申请日：2018-12-20
Applicant: Applied Materials Israel Ltd.
Inventor： Assaf Asbag , Boaz Cohen , Shiran Gan-Or
Abstract: There are provided a classifier and method of classifying defects in a semiconductor specimen. The method comprises receiving defects classified into a majority class, each having values for plurality of attributes, some defects belonging to a minority class, and some to the majority; selecting an attribute subset and defining differentiators for attributes wherein a second classifier using the subset and differentiators classifies correctly to minority and majority classes at least part of the defects; generating a training set comprising: defects of the majority and minority classes, and additional defects which the second classifier classifies as minority; training, upon the training set, subset, and differentiators, an engine obtaining a confidence level that a defect belongs to the majority class; applying the engine to second defects classified to the majority class, to obtain a confidence level of classifying each defect to the majority class; and outputting defects having a low confidence level.
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8.发明授权公开(公告)号：US10803575B2
公开(公告)日：2020-10-13
申请号：US16518589
申请日：2019-07-22
Applicant: APPLIED MATERIALS ISRAEL LTD.
Inventor： Ohad Shaubi , Assaf Asbag , Idan Kaizerman
Abstract: There is provided a system that includes a review tool configured to review at least part of potential defects of an examined object, and assign each of the at least part of the potential defects with a multiplicity of attribute values. The system also includes a computer-based classifier configured to classify, based on the attribute values as assigned, the at least part of potential defects into a set of classes, the set comprising at least a first major class, a second major class and a first minor class, the classifier trained based on a training set comprising a multiplicity of training defects with assigned attribute values, the training defects classified into the set of classes.
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公开(公告)号：US11199506B2
公开(公告)日：2021-12-14
申请号：US16280869
申请日：2019-02-20
Applicant: Applied Materials Israel Ltd.
Inventor： Ohad Shaubi , Assaf Asbag , Boaz Cohen
Abstract: There is provided a system and method of generating a training set usable for examination of a semiconductor specimen. The method comprises: obtaining a simulation model capable of simulating effect of a physical process on fabrication process (FP) images depending on the values of parameters of the physical process; applying the simulation model to an image to be augmented for the training set and thereby generating one or more augmented images corresponding to one or more different values of the parameters of the physical process; and including the generated one or more augmented images into the training set. The training set can be usable for examination of the specimen using a trained Deep Neural Network, automated defect review, automated defect classification, automated navigation during the examination, automated segmentation of FP images, automated metrology based on FP images and other examination processes that include machine learning.
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10.发明授权公开(公告)号：US10832092B2
公开(公告)日：2020-11-10
申请号：US16631155
申请日：2019-02-07
Applicant: APPLIED MATERIALS ISRAEL LTD.
Inventor： Ohad Shaubi , Assaf Asbag , Boaz Cohen
Abstract: There is provided a method of examination of a semiconductor specimen. The method comprises: upon obtaining by a computer 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 synthetic images specific for the given application; and obtaining, by the computer, examination-related data specific for the given application, and characterizing at least one of the processed one or more FP images. Generating the training set can comprise: training an auxiliary DNN to generate a latent space, generating a synthetic image by applying the trained auxiliary DNN to a point selected in the generated latent space, and adding the generated synthetic image to the training set.
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