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公开(公告)号：US20210349038A1
公开(公告)日：2021-11-11
申请号：US17136919
申请日：2020-12-29
Applicant: KLA Corporation
Inventor： Brian Duffy , Mark Roulo , Ashok Mathew , Jing Zhang , Kris Bhaskar
Abstract: An inspection system is disclosed. The inspection system includes a shared memory configured to receive image data from a defect inspection tool and a controller communicatively coupled to the shared memory. The controller includes a host image module configured to apply one or more general-purpose defect-inspection algorithms to the image data using central-processing unit (CPU) architectures, a results module configured to generate inspection data for defects identified by the host image module, and secondary image module(s) configured to apply one or more targeted defect-inspection algorithms to the image data. The secondary image module(s) employ flexible sampling of the image data to match a data processing rate of the host image module within a selected tolerance. The flexible sampling of the image data is adjusted responsive to the inspection data generated by the results module and the host image module.
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公开(公告)号：US20250020598A1
公开(公告)日：2025-01-16
申请号：US18646704
申请日：2024-04-25
Applicant: KLA Corporation
Inventor： Rajkumar Theagarajan , Yujie Dong , Jing Zhang , Brian Duffy , Atiqur Rahman Chowdhury , Kris Bhaskar , Yang Li , Graham Jensen
Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem that include a setup deep learning (DL) model configured for separately performing defect detection for a specimen based on output generated for the specimen by each of two or more modes of an inspection system, respectively, and separately re-performing defect detection for the specimen based on masked output generated for each of the modes, respectively. The computer subsystem determines a difference between results of separately performing and separately re-performing the defect detections for each of the modes, respectively, and identifies a subset of the modes for which the difference is larger than other modes as candidate mode(s) for inspection of the specimen.
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公开(公告)号：US20230260100A1
公开(公告)日：2023-08-17
申请号：US17671519
申请日：2022-02-14
Applicant: KLA Corporation
Inventor： Jing Zhang , Rajkumar Theagarajan , Yujie Dong , John Song , Kris Bhaskar
IPC: G06T7/00
CPC classification number: G06T7/0004 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem that include a deep learning (DL) model trained without labeled data (e.g., in an unsupervised or self-supervised manner) and configured to generate a reference for a specimen from one or more inputs that include at least a specimen image or data generated from the specimen image. The computer subsystem is configured for determining information for the specimen from the reference and at least the specimen image or the data generated from the specimen image.
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公开(公告)号：US11415526B2
公开(公告)日：2022-08-16
申请号：US17136919
申请日：2020-12-29
Applicant: KLA Corporation
Inventor： Brian Duffy , Mark Roulo , Ashok Mathew , Jing Zhang , Kris Bhaskar
Abstract: An inspection system is disclosed. The inspection system includes a shared memory configured to receive image data from a defect inspection tool and a controller communicatively coupled to the shared memory. The controller includes a host image module configured to apply one or more general-purpose defect-inspection algorithms to the image data using central-processing unit (CPU) architectures, a results module configured to generate inspection data for defects identified by the host image module, and secondary image module(s) configured to apply one or more targeted defect-inspection algorithms to the image data. The secondary image module(s) employ flexible sampling of the image data to match a data processing rate of the host image module within a selected tolerance. The flexible sampling of the image data is adjusted responsive to the inspection data generated by the results module and the host image module.
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公开(公告)号：US20210366103A1
公开(公告)日：2021-11-25
申请号：US17128502
申请日：2020-12-21
Applicant: KLA Corporation
Inventor： Jing Zhang , Yujie Dong , Vishank Bhatia , Patrick McBride , Kris Bhaskar , Brian Duffy
Abstract: A system may be configured for joint defect discovery and optical mode selection. Defects are detected during a defect discovery step. The discovered defects are accumulated into a mode selection dataset. The mode selection dataset is used to perform mode selection to determine a mode combination. The mode combination may then be used to train the defect detection model. Additional defects may then be detected by the defect detection model. The additional defects may then be provided to the mode selection dataset, for further performing mode selection and training the defect detection model. One or more run-time modes may then be determined. The system may be configured for mode selection and defect detection at an image pixel level.
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Patent Agency Ranking
公开(公告)号：US20230118839A1
公开(公告)日：2023-04-20
申请号：US18078989
申请日：2022-12-11
Applicant: KLA Corporation
Inventor： Jing Zhang , Zhuoning Yuan , Yujie Dong , Kris Bhaskar
Abstract: Methods and systems for learnable defect detection for semiconductor applications are provided. One system includes a deep metric learning defect detection model configured for projecting a test image for a specimen and a corresponding reference image into latent space, determining a distance in the latent space between one or more different portions of the test image and corresponding portion(s) of the corresponding reference image, and detecting defects in the one or more different portions of the test image based on the determined distances. Another system includes a learnable low-rank reference image generator configured for removing noise from one or more test images for a specimen thereby generating one or more reference images corresponding to the one or more test images.
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公开(公告)号：US11551348B2
公开(公告)日：2023-01-10
申请号：US16838037
申请日：2020-04-02
Applicant: KLA Corporation
Inventor： Jing Zhang , Zhuoning Yuan , Yujie Dong , Kris Bhaskar
Abstract: Methods and systems for learnable defect detection for semiconductor applications are provided. One system includes a deep metric learning defect detection model configured for projecting a test image for a specimen and a corresponding reference image into latent space, determining a distance in the latent space between one or more different portions of the test image and corresponding portion(s) of the corresponding reference image, and detecting defects in the one or more different portions of the test image based on the determined distances. Another system includes a learnable low-rank reference image generator configured for removing noise from one or more test images for a specimen thereby generating one or more reference images corresponding to the one or more test images.
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公开(公告)号：US20240013365A9
公开(公告)日：2024-01-11
申请号：US17671519
申请日：2022-02-14
Applicant: KLA Corporation
Inventor： Jing Zhang , Rajkumar Theagarajan , Yujie Dong , John Song , Kris Bhaskar
IPC: G06T7/00
CPC classification number: G06T7/0004 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem that include a deep learning (DL) model trained without labeled data (e.g., in an unsupervised or self-supervised manner) and configured to generate a reference for a specimen from one or more inputs that include at least a specimen image or data generated from the specimen image. The computer subsystem is configured for determining information for the specimen from the reference and at least the specimen image or the data generated from the specimen image.
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公开(公告)号：US11769242B2
公开(公告)日：2023-09-26
申请号：US17128502
申请日：2020-12-21
Applicant: KLA Corporation
Inventor： Jing Zhang , Yujie Dong , Vishank Bhatia , Patrick McBride , Kris Bhaskar , Brian Duffy
CPC classification number: G06T7/0004 , G01N21/9501 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
Abstract: A system may be configured for joint defect discovery and optical mode selection. Defects are detected during a defect discovery step. The discovered defects are accumulated into a mode selection dataset. The mode selection dataset is used to perform mode selection to determine a mode combination. The mode combination may then be used to train the defect detection model. Additional defects may then be detected by the defect detection model. The additional defects may then be provided to the mode selection dataset, for further performing mode selection and training the defect detection model. One or more run-time modes may then be determined. The system may be configured for mode selection and defect detection at an image pixel level.
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10.发明公开公开(公告)号：US20240169116A1
公开(公告)日：2024-05-23
申请号：US18334357
申请日：2023-06-13
Applicant: KLA Corporation
Inventor： Brian Duffy , Kris Bhaskar
IPC: G06F30/20 , G06F30/3308
CPC classification number: G06F30/20 , G06F30/3308
Abstract: Methods and systems for performing functions with protected data sources from different entities are provided. One system includes a virtual system coupled to an actual system to thereby receive output generated by the actual system for a physical version of the specimen while the specimen is disposed within the actual system. The virtual system includes at least a computer system and a storage medium. The virtual system is not capable of having the physical version of the specimen disposed therein. The virtual system is configured for performing one or more functions for the specimen with two or more protected data sources from two or more different entities, respectively. The virtual system is also configured for performing a virtual version of a process capable of being performed by the actual system for the physical version of the specimen.
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