Patents by Inventor Huajun Ying
Huajun Ying has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11580375Abstract: 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.Type: GrantFiled: December 29, 2016Date of Patent: February 14, 2023Assignee: KLA-Tencor Corp.Inventors: Kris Bhaskar, Laurent Karsenti, Scott Young, Mohan Mahadevan, Jing Zhang, Brian Duffy, Li He, Huajun Ying, Hung Nien, Sankar Venkataraman
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Patent number: 10607119Abstract: 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.Type: GrantFiled: September 6, 2017Date of Patent: March 31, 2020Assignee: KLA-Tencor Corp.Inventors: Li He, Mohan Mahadevan, Sankar Venkataraman, Huajun Ying, Hedong Yang
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Patent number: 10482590Abstract: 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.Type: GrantFiled: December 12, 2017Date of Patent: November 19, 2019Assignee: KLA-Tencor CorporationInventors: Li He, Chien-Huei Adam Chen, Sankar Venkataraman, John R. Jordan, Huajun Ying, Sinha Harsh
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Patent number: 10436720Abstract: 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.Type: GrantFiled: January 8, 2016Date of Patent: October 8, 2019Assignee: KLA-Tenfor Corp.Inventors: Li He, Martin Plihal, Huajun Ying, Anadi Bhatia, Amitoz Singh Dandiana, Ramakanth Ramini
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Publication number: 20190073568Abstract: 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.Type: ApplicationFiled: September 6, 2017Publication date: March 7, 2019Inventors: Li He, Mohan Mahadevan, Sankar Venkataraman, Huajun Ying, Hedong Yang
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Publication number: 20180114310Abstract: 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.Type: ApplicationFiled: December 12, 2017Publication date: April 26, 2018Inventors: Li He, Chien-Huei Adam Chen, Sankar Venkataraman, John R. Jordan, Huajun Ying, Sinha Harsh
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Patent number: 9898811Abstract: 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.Type: GrantFiled: June 24, 2015Date of Patent: February 20, 2018Assignee: KLA-Tencor CorporationInventors: Li He, Chien-Huei Adam Chen, Sankar Venkataraman, John R. Jordan, III, Huajun Ying, Harsh Sinha
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Publication number: 20170193400Abstract: 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.Type: ApplicationFiled: December 29, 2016Publication date: July 6, 2017Inventors: Kris Bhaskar, Laurent Karsenti, Scott Young, Mohan Mahadevan, Jing Zhang, Brian Duffy, Li He, Huajun Ying, Hung Nien, Sankar Venkataraman
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Publication number: 20170082555Abstract: 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.Type: ApplicationFiled: January 8, 2016Publication date: March 23, 2017Inventors: Li He, Martin Plihal, Huajun Ying, Anadi Bhatia, Amitoz Singh Dandiana, Ramakanth Ramini
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Publication number: 20160328837Abstract: 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.Type: ApplicationFiled: June 24, 2015Publication date: November 10, 2016Inventors: Li He, ChienHuei Adam Chen, Sankar Venkataraman, John R. Jordan, III, Huajun Ying, Sinha Harsh