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公开(公告)号：US20200126212A1
公开(公告)日：2020-04-23
申请号：US16364161
申请日：2019-03-25
Applicant: KLA-Tencor Corporation
Inventor： Brian Duffy , Martin Plihal , Santosh Bhattacharyya , Gordon Rouse , Chris Maher , Erfan Soltanmohammadi
IPC: G06T7/00 , H01L21/66 , G06T7/11 , G01R31/308 , G03F7/20
Abstract: Methods and systems for setting up inspection of a specimen with design and noise based care areas are provided. One system includes one or more computer subsystems configured for generating a design-based care area for a specimen. The computer subsystem(s) are also configured for determining one or more output attributes for multiple instances of the care area on the specimen, and the one or more output attributes are determined from output generated by an output acquisition subsystem for the multiple instances. The computer subsystem(s) are further configured for separating the multiple instances of the care area on the specimen into different care area sub-groups such that the different care area sub-groups have statistically different values of the output attribute(s) and selecting a parameter of an inspection recipe for the specimen based on the different care area sub-groups.
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公开(公告)号：US10267748B2
公开(公告)日：2019-04-23
申请号：US15782820
申请日：2017-10-12
Applicant: KLA-Tencor Corporation
Inventor： Martin Plihal , Erfan Soltanmohammadi , Saravanan Paramasivam , Sairam Ravu , Ankit Jain , Sarath Shekkizhar , Prasanti Uppaluri
Abstract: Methods and systems for training an inspection-related algorithm are provided. One system includes one or more computer subsystems configured for performing an initial training of an inspection-related algorithm with a labeled set of defects thereby generating an initial version of the inspection-related algorithm and applying the initial version of the inspection-related algorithm to an unlabeled set of defects. The computer subsystem(s) are also configured for altering the labeled set of defects based on results of the applying. The computer subsystem(s) may then iteratively re-train the inspection-related algorithm and alter the labeled set of defects until one or more differences between results produced by a most recent version and a previous version of the algorithm meet one or more criteria. When the one or more differences meet the one or more criteria, the most recent version of the inspection-related algorithm is outputted as the trained algorithm.
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公开(公告)号：US10832396B2
公开(公告)日：2020-11-10
申请号：US16364161
申请日：2019-03-25
Applicant: KLA-Tencor Corporation
Inventor： Brian Duffy , Martin Plihal , Santosh Bhattacharyya , Gordon Rouse , Chris Maher , Erfan Soltanmohammadi
Abstract: Methods and systems for setting up inspection of a specimen with design and noise based care areas are provided. One system includes one or more computer subsystems configured for generating a design-based care area for a specimen. The computer subsystem(s) are also configured for determining one or more output attributes for multiple instances of the care area on the specimen, and the one or more output attributes are determined from output generated by an output acquisition subsystem for the multiple instances. The computer subsystem(s) are further configured for separating the multiple instances of the care area on the specimen into different care area sub-groups such that the different care area sub-groups have statistically different values of the output attribute(s) and selecting a parameter of an inspection recipe for the specimen based on the different care area sub-groups.
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公开(公告)号：US20200218241A1
公开(公告)日：2020-07-09
申请号：US16242844
申请日：2019-01-08
Applicant: KLA-Tencor Corporation
Inventor： Erfan Soltanmohammadi , Ashwin Ramakrishnan , Mohit Jani
IPC: G05B19/418
Abstract: An inspection system may receive inspection datasets from a defect inspection system associated with inspection of one or more samples, where an inspection dataset of the plurality of inspection datasets associated with a defect includes values of two or more signal attributes and values of one or more context attributes. An inspection system may further label each of the inspection datasets with a class label based on respective positions of each of the inspection datasets in a signal space defined by the two or more signal attributes, where each class label corresponds to a region of the signal space. An inspection system may further segment the inspection datasets into two or more defect groups by training a classifier with the values of the context attributes and corresponding class labels for the inspection datasets, where the two or more defect groups are identified based on the trained classifier.
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Patent Agency Ranking
公开(公告)号：US11237119B2
公开(公告)日：2022-02-01
申请号：US15835399
申请日：2017-12-07
Applicant: KLA-Tencor Corporation
Inventor： Martin Plihal , Erfan Soltanmohammadi , Saravanan Paramasivam , Sairam Ravu , Ankit Jain , Prasanti Uppaluri , Vijay Ramachandran
IPC: G01N21/95 , H01L21/67 , G05B19/418 , G01N21/88 , G06T7/00 , H01J37/22 , H01J37/28 , G01N23/04 , H01L21/66 , G06N7/00
Abstract: Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.
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公开(公告)号：US10902579B1
公开(公告)日：2021-01-26
申请号：US16188674
申请日：2018-11-13
Applicant: KLA-Tencor Corporation
Inventor： Erfan Soltanmohammadi , Martin Plihal , Tai-Kam Ng , Sang Hyun Lee
Abstract: Defects of interest can be captured by a classifier. Images of a semiconductor wafer can be received at a deep learning classification module. These images can be sorted into soft decisions with the deep learning classification module. A class of the defect of interest for an image can be determined from the soft decisions. The deep learning classification module can be in electronic communication with an optical inspection system or other types of semiconductor inspection systems.
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公开(公告)号：US20180197714A1
公开(公告)日：2018-07-12
申请号：US15835399
申请日：2017-12-07
Applicant: KLA-Tencor Corporation
Inventor： Martin Plihal , Erfan Soltanmohammadi , Saravanan Paramasivam , Sairam Ravu , Ankit Jain , Prasanti Uppaluri , Vijay Ramachandran
CPC classification number: H01J37/222 , G01N23/04 , G05B19/41875 , G05B2219/32186 , G05B2219/32196 , H01J37/28 , H01J2237/22 , H01J2237/2817 , H01L21/67288
Abstract: Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.
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公开(公告)号：US11550309B2
公开(公告)日：2023-01-10
申请号：US16242844
申请日：2019-01-08
Applicant: KLA-Tencor Corporation
Inventor： Erfan Soltanmohammadi , Ashwin Ramakrishnan , Mohit Jani
IPC: G05B19/418
Abstract: An inspection system may receive inspection datasets from a defect inspection system associated with inspection of one or more samples, where an inspection dataset of the plurality of inspection datasets associated with a defect includes values of two or more signal attributes and values of one or more context attributes. An inspection system may further label each of the inspection datasets with a class label based on respective positions of each of the inspection datasets in a signal space defined by the two or more signal attributes, where each class label corresponds to a region of the signal space. An inspection system may further segment the inspection datasets into two or more defect groups by training a classifier with the values of the context attributes and corresponding class labels for the inspection datasets, where the two or more defect groups are identified based on the trained classifier.
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公开(公告)号：US20180106732A1
公开(公告)日：2018-04-19
申请号：US15782820
申请日：2017-10-12
Applicant: KLA-Tencor Corporation
Inventor： Martin Plihal , Erfan Soltanmohammadi , Saravanan Paramasivam , Sairam Ravu , Ankit Jain , Sarath Shekkizhar , Prasanti Uppaluri
CPC classification number: G01N21/9501 , G01N21/55 , G01N21/8803 , G01N21/8851 , G01N2021/8854 , G01N2021/95676 , G06N5/003 , G06N99/005
Abstract: Methods and systems for training an inspection-related algorithm are provided. One system includes one or more computer subsystems configured for performing an initial training of an inspection-related algorithm with a labeled set of defects thereby generating an initial version of the inspection-related algorithm and applying the initial version of the inspection-related algorithm to an unlabeled set of defects. The computer subsystem(s) are also configured for altering the labeled set of defects based on results of the applying. The computer subsystem(s) may then iteratively re-train the inspection-related algorithm and alter the labeled set of defects until one or more differences between results produced by a most recent version and a previous version of the algorithm meet one or more criteria. When the one or more differences meet the one or more criteria, the most recent version of the inspection-related algorithm is outputted as the trained algorithm.
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