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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：US11580375B2
公开(公告)日：2023-02-14
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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公开(公告)号：US10607119B2
公开(公告)日：2020-03-31
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：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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Patent Agency Ranking
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：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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公开(公告)号：US10436720B2
公开(公告)日：2019-10-08
申请号：US14991901
申请日：2016-01-08
Applicant: KLA-Tencor Corporation
Inventor： Li He , Martin Plihal , Huajun Ying , Anadi Bhatia , Amitoz Singh Dandiana , Ramakanth Ramini
Abstract: Methods and systems for classifying defects detected on a specimen with an adaptive automatic defect classifier are provided. One method includes creating a defect classifier based on classifications received from a user for different groups of defects in first lot results and a training set of defects that includes all the defects in the first lot results. The first and additional lot results are combined to create cumulative lot results. Defects in the cumulative lot results are classified with the created defect classifier. If any of the defects are classified with a confidence below a threshold, the defect classifier is modified based on a modified training set that includes the low confidence classified defects and classifications for these defects received from a user. The modified defect classifier is then used to classify defects in additional cumulative lot results.
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公开(公告)号：US20180114310A1
公开(公告)日：2018-04-26
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
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：US20170082555A1
公开(公告)日：2017-03-23
申请号：US14991901
申请日：2016-01-08
Applicant: KLA-Tencor Corporation
Inventor： Li He , Martin Plihal , Huajun Ying , Anadi Bhatia , Amitoz Singh Dandiana , Ramakanth Ramini
CPC classification number: G01N21/9501 , G01N21/8851 , G01N2021/8854 , G01N2021/8883 , G01N2201/06113 , G01N2201/12 , G06N20/00 , H01L22/12 , H01L22/20
Abstract: Methods and systems for classifying defects detected on a specimen with an adaptive automatic defect classifier are provided. One method includes creating a defect classifier based on classifications received from a user for different groups of defects in first lot results and a training set of defects that includes all the defects in the first lot results. The first and additional lot results are combined to create cumulative lot results. Defects in the cumulative lot results are classified with the created defect classifier. If any of the defects are classified with a confidence below a threshold, the defect classifier is modified based on a modified training set that includes the low confidence classified defects and classifications for these defects received from a user. The modified defect classifier is then used to classify defects in additional cumulative lot results.
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