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公开(公告)号：US20220237759A1
公开(公告)日：2022-07-28
申请号：US17567847
申请日：2022-01-03
Applicant: ASML Netherlands B.V.
Inventor： Lingling PU , Wei FANG , Nan ZHAO , Wentian ZHOU , Teng WANG , Ming XU
Abstract: Systems and methods for optimal electron beam metrology guidance are disclosed. According to certain embodiments, the method may include receiving an acquired image of a sample, determining a set of image parameters based on an analysis of the acquired image, determining a set of model parameters based on the set of image parameters, generating a set of simulated images based on the set of model parameters. The method may further comprise performing measurement of critical dimensions on the set of simulated images and comparing critical dimension measurements with the set of model parameters to provide a set of guidance parameters based on comparison of information from the set of simulated images and the set of model parameters. The method may further comprise receiving auxiliary information associated with target parameters including critical dimension uniformity.
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Patent Agency Ranking
公开(公告)号：US20200074610A1
公开(公告)日：2020-03-05
申请号：US16554110
申请日：2019-08-28
Applicant: ASML Netherlands B.V.
Inventor： Lingling PU , Wei FANG , Nan ZHAO , Wentian ZHOU , Teng WANG , Ming XU
Abstract: Systems and methods for optimal electron beam metrology guidance are disclosed. According to certain embodiments, the method may include receiving an acquired image of a sample, determining a set of image parameters based on an analysis of the acquired image, determining a set of model parameters based on the set of image parameters, generating a set of simulated images based on the set of model parameters. The method may further comprise performing measurement of critical dimensions on the set of simulated images and comparing critical dimension measurements with the set of model parameters to provide a set of guidance parameters based on comparison of information from the set of simulated images and the set of model parameters. The method may further comprise receiving auxiliary information associated with target parameters including critical dimension uniformity.
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公开(公告)号：US20240046620A1
公开(公告)日：2024-02-08
申请号：US18365134
申请日：2023-08-03
Applicant: ASML Netherlands B.V.
Inventor： Wentian ZHOU , Liangjiang YU , Teng WANG , Lingling PU , Wei FANG
IPC: G06V10/774 , G06T7/00 , G06F18/214 , G06V10/776 , G06V10/98
CPC classification number: G06V10/774 , G06T7/0006 , G06F18/214 , G06V10/776 , G06V10/993 , G06T2207/10061 , G06T2207/20081 , G06T2207/30148
Abstract: Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.
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公开(公告)号：US20210350507A1
公开(公告)日：2021-11-11
申请号：US17308835
申请日：2021-05-05
Applicant: ASML Netherlands B.V.
Inventor： Wei FANG , Ruochong FEI , Lingling PU , Wentian ZHOU , Liangjiang YU , Bo WANG
Abstract: An improved method and apparatus for enhancing an inspection image in a charged-particle beam inspection system. An improved method for enhancing an inspection image comprises acquiring a first image and a second image of multiple stacked layers of a sample that are taken with a first focal point and a second focal point, respectively, associating a first segment of the first image with a first layer among the multiple stacked layers and associating a second segment of the second image with a second layer among the multiple stacked layers, updating the first segment based on a first reference image corresponding to the first layer and updating the second segment based on a second reference image corresponding to the second layer, and combining the updated first segment and the updated second segment to generate a combined image including the first layer and the second layer.
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公开(公告)号：US20250078478A1
公开(公告)日：2025-03-06
申请号：US18882681
申请日：2024-09-11
Applicant: ASML Netherlands B.V.
Inventor： Wentian ZHOU , Liangjiang YU , Teng WANG , Lingling PU , Wei FANG
IPC: G06V10/774 , G06F18/214 , G06T7/00 , G06V10/776 , G06V10/98
Abstract: Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.
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公开(公告)号：US20200211178A1
公开(公告)日：2020-07-02
申请号：US16718706
申请日：2019-12-18
Applicant: ASML Netherlands B.V.
Inventor： Wentian ZHOU , Liangjiang YU , Teng WANG , Lingling PU , Wei FANG
Abstract: Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.
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