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1.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：US20190096053A1
公开(公告)日：2019-03-28
申请号：US15719447
申请日：2017-09-28
发明人： Assaf ASBAG , Ohad SHAUBI , Kirill SAVCHENKO , Shiran GAN-OR , Boaz COHEN , Zeev ZOHAR
摘要： 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：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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4.发明申请公开(公告)号：US20190066290A1
公开(公告)日：2019-02-28
申请号：US15685974
申请日：2017-08-24
发明人： Ohad SHAUBI , Assaf ASBAG , Idan KAIZERMAN
CPC分类号： G06T7/0004 , G05B23/00 , G06K9/6256 , G06K9/6262 , G06K9/6269 , G06N20/00 , G06N20/10 , G06N20/20 , G06T2207/30148
摘要： 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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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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6.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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