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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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Patent Agency Ranking
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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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：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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公开(公告)号：US20240264539A1
公开(公告)日：2024-08-08
申请号：US18565951
申请日：2022-05-20
Applicant: ASML NETHERLANDS B.V.
Inventor： Lingling LU , Yuzhang LIN , Teng WANG , Bo WANG , Raphael Eric LA GRECA , Stefan HUNSCHE
CPC classification number: G03F7/706837 , G03F7/70625 , G03F7/70655 , G06T7/0006 , G06T7/12 , G06T2207/10061 , G06T2207/30148
Abstract: To monitor semiconductor manufacturing process variation, contours of identical pattern features are determined based on SEM images, and the contours are aggregated and statistically analyzed to determine the variation of the feature. Some of the contours are outliers, and the aggregation and averaging of the contours “hides” these outliers. The present disclosure describes filtering certain outlier contours before they are aggregated and statistically analyzed. The filtering can be performed at multiple levels, such as based on individual points on the contours in the set of inspection contours, or based on overall geometrical shapes of the contours in the set of inspection contours.
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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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