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公开(公告)号：US20210383530A1
公开(公告)日：2021-12-09
申请号：US16892139
申请日：2020-06-03
发明人： Irad PELEG , Ran SCHLEYEN , Boaz COHEN
摘要： A system of classifying a pattern of interest (POI) on a semiconductor specimen, the system comprising a processor and memory circuitry configured to: obtain a high-resolution image of the POI, and generate data usable for classifying the POI in accordance with a defectiveness-related classification, wherein the generating utilizes a machine learning model that has been trained in accordance with training samples comprising: a high-resolution training image captured by scanning a respective training pattern on a specimen, the respective training pattern being similar to the POI, and a label associated with the image, the label being derivative of low-resolution inspection of the respective training pattern.
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2.发明申请公开(公告)号：US20200226420A1
公开(公告)日：2020-07-16
申请号：US16631155
申请日：2019-02-07
发明人： Ohad SHAUBI , Assaf ASBAG , Boaz COHEN
摘要： 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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公开(公告)号：US20190293669A1
公开(公告)日：2019-09-26
申请号：US15933306
申请日：2018-03-22
发明人： Kirill SAVCHENKO , Assaf ASBAG , Boaz COHEN
摘要： 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：US20210073963A1
公开(公告)日：2021-03-11
申请号：US16833380
申请日：2020-03-27
发明人： Ariel SHKALIM , Vladimir OVECHKIN , Evgeny BAL , Ronen MADMON , Ori PETEL , Alexander CHERESHNYA , Oren Shmuel COHEN , Boaz COHEN
摘要： There is provided a mask inspection system and a method of mask inspection. The method comprises: during a runtime scan of a mask of a semiconductor specimen, processing a plurality of aerial images of the mask acquired by the mask inspection system to calculate a statistic-based Edge Positioning Displacement (EPD) of a potential defect, wherein the statistic-based EPD is calculated using a Print Threshold (PT) characterizing the mask and is applied to each of the one or more acquired aerial images to calculate respective EPD of the potential defect therein; and filtering the potential defect as a “runtime true” defect when the calculated statistic-based EPD exceeds a predefined EPD threshold, and filtering out the potential defect as a “false” defect when the calculated statistic-based EPD is lower than the predefined EPD threshold. The method can further comprise after-runtime EPD-based filtering of the plurality of “runtime true” defects.
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公开(公告)号：US20190257767A1
公开(公告)日：2019-08-22
申请号：US16280869
申请日：2019-02-20
发明人： Ohad SHAUBI , Assaf ASBAG , Boaz COHEN
摘要： 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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公开(公告)号：US20240078659A1
公开(公告)日：2024-03-07
申请号：US17903262
申请日：2022-09-06
CPC分类号： G06T7/001 , G06T5/002 , G06T5/005 , G06T7/30 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
摘要： There is provided a system and method for defect examination on a semiconductor specimen. The method comprises obtaining a runtime image of the semiconductor specimen, generating a reference image based on the runtime image using a machine learning (ML) model, and performing defect examination on the runtime image using the generated reference image. The ML model is previously trained during setup using a training set comprising one or more pairs of training images, each pair including a defective image and a corresponding defect-free image. The training comprises, for each pair, processing the defective image by the ML model to obtain a predicted image, and optimizing the ML model to minimize a difference between the predicted image and the defect-free image.
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公开(公告)号：US20220222806A1
公开(公告)日：2022-07-14
申请号：US17605217
申请日：2020-03-24
发明人： Ohad SHAUBI , Boaz COHEN , Kirill SAVCHENKO , Ore SHTALRID
IPC分类号： G06T7/00 , G06V10/764 , G06V10/774 , G06V10/776
摘要： There is provided a method of automated defects' classification, and a system thereof. The method comprises obtaining data informative of a set of defects' physical attributes usable to distinguish between defects of different classes among the plurality of classes; training a first machine learning model to generate, for the given defect, a multi-label output vector informative of values of the physical attributes, thereby generating for the given defect a multi-label descriptor; and using the trained first machine learning model to generate multi-label descriptors of the defects in the specimen. The method can further comprise obtaining data informative of multi-label data sets, each data set being uniquely indicative of a respective class of the plurality of classes and comprising a unique set of values of the physical attributes; and classifying defects in the specimen by matching respectively generated multi-label descriptors of the defects to the multi-label data sets.
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8.发明申请公开(公告)号：US20220067523A1
公开(公告)日：2022-03-03
申请号：US17521499
申请日：2021-11-08
发明人： Leonid KARLINSKY , Boaz COHEN , Idan KAIZERMAN , Efrat ROSENMAN , Amit BATIKOFF , Daniel RAVID , Moshe ROSENWEIG
摘要： 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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9.发明申请公开(公告)号：US20200294224A1
公开(公告)日：2020-09-17
申请号：US16892123
申请日：2020-06-03
发明人： Ohad SHAUBI , Denis SUHANOV , Assaf ASBAG , Boaz COHEN
摘要： 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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10.发明申请公开(公告)号：US20170357895A1
公开(公告)日：2017-12-14
申请号：US15668623
申请日：2017-08-03
发明人： Leonid KARLINSKY , Boaz COHEN , Idan KAIZERMAN , Efrat ROSENMAN , Amit BATIKOFF , Daniel RAVID , Moshe ROSENWEIG
IPC分类号： G06N3/08
CPC分类号： G06N3/08 , G06K9/036 , G06K9/4628 , G06K9/6271 , G06N3/0454
摘要： 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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