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公开(公告)号：US11423529B2
公开(公告)日：2022-08-23
申请号：US16794172
申请日：2020-02-18
Applicant: Applied Materials Israel Ltd.
Inventor： Doron Girmonsky , Rafael Ben Ami , Boaz Cohen , Dror Shemesh
Abstract: There is provided a method and a system configured to obtain an image of a one or more first areas of a semiconductor specimen acquired by an examination tool, determine data Datt informative of defectivity in the one or more first areas, determine one or more second areas of the semiconductor specimen for which presence of a defect is suspected based at least on an evolution of Datt, or of data correlated to Datt, in the one or more first areas, and select the one or more second areas for inspection by the examination tool.
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公开(公告)号：US11263741B2
公开(公告)日：2022-03-01
申请号：US16752353
申请日：2020-01-24
Applicant: Applied Materials Israel Ltd.
Inventor： Boaz Cohen , Gadi Greenberg , Sivan Lifschitz , Shay Attal , Oded O. Dassa , Ziv Parizat
Abstract: Implementations of the disclosure provide methods for generating an in-die reference for die-to-die defect detection techniques. The inspection methods using in-die reference comprise finding similar blocks of a lithographic mask, the similar blocks are defined by similar CAD information. A comparison distance is selected based on (i) areas of the similar blocks and (ii) spatial relationships between the similar blocks. The similar blocks are aggregated, based on the comparison distance, to provide multiple aggregated areas; and comparable regions of the lithographic mask are defined based on the multiple aggregate blocks. Images of at least some of the comparable regions of the lithographic mask are acquired using an inspection module. The acquired images are compared.
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13.发明授权公开(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：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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15.发明授权公开(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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Patent Agency Ranking
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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17.发明授权公开(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：US11756188B2
公开(公告)日：2023-09-12
申请号：US17694131
申请日：2022-03-14
Applicant: Applied Materials Israel Ltd.
Inventor： Vadim Vereschagin , Roman Kris , Ishai Schwarzband , Boaz Cohen , Evgeny Bal , Ariel Shkalim
CPC classification number: G06T7/001 , G06T7/13 , G06T7/60 , G06T2207/30148
Abstract: Input data may be received. The input data may include an image of a pattern and location data that identifies a modified portion of the pattern. A processing device may determine a first parameter of a first dimension within the pattern and a second parameter of a second dimension outside of the pattern. A combined set may be generated based on the first parameter and the second parameter. A defect associated with the modified portion may be classified based on the combined set.
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19.发明授权公开(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：US20220291138A1
公开(公告)日：2022-09-15
申请号：US17829593
申请日：2022-06-01
Applicant: Applied Materials Israel Ltd.
Inventor： Yotam Sofer , Shaul Engler , Boaz Cohen , Saar Shabtay , Amir Bar , Marcelo Gabriel Bacher
Abstract: A system, method and computer readable medium for examining a specimen, the method comprising: obtaining defects of interest (DOIs) and false alarms (FAs) from a review subset selected from a group of potential defects received from an inspection tool, each potential defect is associated with attribute values defining a location of the potential defect in an attribute space; generating a representative subset of the group, comprising potential defects selected in accordance with a distribution of the potential defects within the attribute space, and indicating the potential defects in the representative subset as FA; and training a classifier using data informative of the attribute values of the DOIs, the potential defects of the representative subset, and respective indications thereof as DOIs or FAs, wherein the trained classifier is to be applied to at least some of the potential defects to obtain an estimation of a number of expected DOIs.
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