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公开(公告)号：US20190287230A1
公开(公告)日：2019-09-19
申请号：US16106341
申请日：2018-08-21
Applicant: KLA-TENCOR CORPORATION
Inventor： Shaoyu Lu , Li He , Sankar Venkataraman
IPC: G06T7/00
Abstract: Autoencoder-based, semi-supervised approaches are used for anomaly detection. Defects on semiconductor wafers can be discovered using these approaches. The model can include a variational autoencoder, such as a one that includes ladder networks. Defect-free or clean images can be used to train the model that is later used to discover defects or other anomalies.
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公开(公告)号：US09898811B2
公开(公告)日：2018-02-20
申请号：US14749316
申请日：2015-06-24
Applicant: KLA-Tencor Corporation
Inventor： Li He , Chien-Huei Adam Chen , Sankar Venkataraman , John R. Jordan, III , Huajun Ying , Harsh Sinha
CPC classification number: G06T7/0004 , G06K9/6256 , G06K9/6292 , G06K2009/6295 , G06T2207/20081 , G06T2207/30148
Abstract: Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.
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公开(公告)号：US20160328837A1
公开(公告)日：2016-11-10
申请号：US14749316
申请日：2015-06-24
Applicant: KLA-Tencor Corporation
Inventor： Li He , ChienHuei Adam Chen , Sankar Venkataraman , John R. Jordan, III , Huajun Ying , Sinha Harsh
CPC classification number: G06T7/0004 , G06K9/6256 , G06K9/6292 , G06K2009/6295 , G06T2207/20081 , G06T2207/30148
Abstract: Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.
Abstract translation: 缺陷分类包括获取样本的一个或多个图像，基于一个或多个训练缺陷的一个或多个属性接收一个或多个训练缺陷的手动分类，基于所接收到的手动分类和属性生成集合学习分类器 的一个或多个训练缺陷，基于接收到的分类纯度要求，生成针对所述一个或多个训练缺陷的每个缺陷类型的置信阈值，获取包括一个或多个测试缺陷的一个或多个图像，分类一个或多个测试缺陷 利用所生成的集体学习分类器，利用所生成的集体学习分类器计算每个一个或多个测试缺陷的置信水平，并且经由用户界面设备报告具有低于生成的置信度阈值的置信水平的一个或多个测试缺陷以进行手动分类 。
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公开(公告)号：US10789703B2
公开(公告)日：2020-09-29
申请号：US16106341
申请日：2018-08-21
Applicant: KLA-TENCOR CORPORATION
Inventor： Shaoyu Lu , Li He , Sankar Venkataraman
Abstract: Autoencoder-based, semi-supervised approaches are used for anomaly detection. Defects on semiconductor wafers can be discovered using these approaches. The model can include a variational autoencoder, such as a one that includes ladder networks. Defect-free or clean images can be used to train the model that is later used to discover defects or other anomalies.
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公开(公告)号：US20190294923A1
公开(公告)日：2019-09-26
申请号：US16357360
申请日：2019-03-19
Applicant: KLA-Tencor Corporation
Inventor： Ian Riley , Li He , Sankar Venkataraman , Michael Kowalski , Arjun Hegde
IPC: G06K9/62 , G06N20/00 , G06F3/0482 , G06T7/00
Abstract: Methods and systems for training a machine learning model using synthetic defect images are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a graphical user interface (GUI) configured for displaying one or more images for a specimen and image editing tools to a user and for receiving input from the user that includes one or more alterations to at least one of the images using one or more of the image editing tools. The component(s) also include an image processing module configured for applying the alteration(s) to the at least one image thereby generating at least one modified image and storing the at least one modified image in a training set. The computer subsystem(s) are configured for training a machine learning model with the training set in which the at least one modified image is stored.
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Patent Agency Ranking
公开(公告)号：US10186026B2
公开(公告)日：2019-01-22
申请号：US15353210
申请日：2016-11-16
Applicant: KLA-Tencor Corporation
Inventor： Laurent Karsenti , Kris Bhaskar , John Raymond Jordan, III , Sankar Venkataraman , Yair Carmon
Abstract: Methods and systems for detecting defects on a specimen are provided. One system includes a generative model. The generative model includes a non-linear network configured for mapping blocks of pixels of an input feature map volume into labels. The labels are indicative of one or more defect-related characteristics of the blocks. The system inputs a single test image into the generative model, which determines features of blocks of pixels in the single test image and determines labels for the blocks based on the mapping. The system detects defects on the specimen based on the determined labels.
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公开(公告)号：US10482590B2
公开(公告)日：2019-11-19
申请号：US15839690
申请日：2017-12-12
Applicant: KLA-Tencor Corporation
Inventor： Li He , Chien-Huei Adam Chen , Sankar Venkataraman , John R. Jordan , Huajun Ying , Sinha Harsh
Abstract: Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.
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公开(公告)号：US20190073568A1
公开(公告)日：2019-03-07
申请号：US15697426
申请日：2017-09-06
Applicant: KLA-Tencor Corporation
Inventor： Li He , Mohan Mahadevan , Sankar Venkataraman , Huajun Ying , Hedong Yang
Abstract: Methods and systems for detecting and classifying defects on a specimen are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a neural network configured for detecting defects on a specimen and classifying the defects detected on the specimen. The neural network includes a first portion configured for determining features of images of the specimen generated by an imaging subsystem. The neural network also includes a second portion configured for detecting defects on the specimen based on the determined features of the images and classifying the defects detected on the specimen based on the determined features of the images.
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公开(公告)号：US09922269B2
公开(公告)日：2018-03-20
申请号：US15010887
申请日：2016-01-29
Applicant: KLA-Tencor Corporation
Inventor： Sankar Venkataraman , Li He , John R. Jordan, III , Oksen Baris , Harsh Sinha
CPC classification number: G06K9/6254 , G06K9/6255 , G06K9/6269 , G06K9/628 , G06K9/6282 , G06T7/0004 , G06T2207/10061 , G06T2207/20081 , G06T2207/30148 , H01L22/00 , H01L22/12 , H01L22/20
Abstract: Defect classification includes acquiring one or more images of a specimen including multiple defects, grouping the defects into groups of defect types based on the attributes of the defects, receiving a signal from a user interface device indicative of a first manual classification of a selected number of defects from the groups, generating a classifier based on the first manual classification and the attributes of the defects, classifying, with the classifier, one or more defects not manually classified by the manual classification, identifying the defects classified by the classifier having the lowest confidence level, receiving a signal from the user interface device indicative of an additional manual classification of the defects having the lowest confidence level, determining whether the additional manual classification identifies one or more additional defect types not identified in the first manual classification, and iterating the procedure until no new defect types are found.
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10.发明申请公开(公告)号：US20170193400A1
公开(公告)日：2017-07-06
申请号：US15394790
申请日：2016-12-29
Applicant: KLA-Tencor Corporation
Inventor： Kris Bhaskar , Laurent Karsenti , Scott Young , Mohan Mahadevan , Jing Zhang , Brian Duffy , Li He , Huajun Ying , Hung Nien , Sankar Venkataraman
Abstract: Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.
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