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公开(公告)号：US11379969B2
公开(公告)日：2022-07-05
申请号：US16940373
申请日：2020-07-27
Applicant: KLA CORPORATION
Inventor： Martin Plihal , Prasanti Uppaluri , Saravanan Paramasivam
Abstract: Machine learning approaches provide additional information about semiconductor wafer inspection stability issues that makes it possible to distinguish consequential process variations like process excursions from minor process variations that are within specification. The effect of variable defect of interest (DOI) capture rates in the inspection result and the effect of variable defect count on the wafer can be monitored independently.
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2.发明申请公开(公告)号：US20200234428A1
公开(公告)日：2020-07-23
申请号：US16744385
申请日：2020-01-16
Applicant: KLA Corporation
Inventor： Jacob George , Saravanan Paramasivam , Martin Plihal , Niveditha Lakshmi Narasimhan , Sairam Ravu , Prasanti Uppaluri
Abstract: Methods and systems for improved detection and classification of defects of interest (DOI) is realized based on values of one or more automatically generated attributes derived from images of a candidate defect. Automatically generated attributes are determined by iteratively training, reducing, and retraining a deep learning model. The deep learning model relates optical images of candidate defects to a known classification of those defects. After model reduction, attributes of the reduced model are identified which strongly relate the optical images of candidate defects to the known classification of the defects. The reduced model is subsequently employed to generate values of the identified attributes associated with images of candidate defects having unknown classification. In another aspect, a statistical classifier is employed to classify defects based on automatically generated attributes and attributes identified manually.
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公开(公告)号：US20210027445A1
公开(公告)日：2021-01-28
申请号：US16935159
申请日：2020-07-21
Applicant: KLA Corporation
Inventor： Martin Plihal , Saravanan Paramasivam , Jacob George , Niveditha Lakshmi Narasimhan , Sairam Ravu , Somesh Challapalli , Prasanti Uppaluri
Abstract: A system for characterizing a specimen is disclosed. In one embodiment, the system includes a characterization sub-system configured to acquire one or more images a specimen, and a controller communicatively coupled to the characterization sub-system. The controller may be configured to: receive from the characterization sub-system one or more training images of one or more defects of a training specimen; generate one or more augmented images of the one or more defects of the training specimen; generate a machine learning classifier based on the one or more augmented images of the one or more defects of the training specimen; receive from the characterization sub-system one or more target images of one or more target features of a target specimen; and determine one or more defects of the one or more target features with the machine learning classifier.
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4.发明申请公开(公告)号：US20250071419A1
公开(公告)日：2025-02-27
申请号：US18793931
申请日：2024-08-04
Applicant: KLA Corporation
Inventor： Raghavan Konuru , Jeetsagar Ghorai , Jacob George , Niveditha LakshmiNarasimhan , Sairam Ravu , Saravanan Paramasivam , Martin Plihal , Prasanti Uppaluri
IPC: H04N23/667
Abstract: Methods and systems for selecting modes for a mode selection process are provided. One system includes a computer subsystem configured for determining information for a specimen and at least one value of a characteristic of the information from images generated for the specimen with an initial subset of different modes of an imaging subsystem. The computer subsystem is also configured for predicting probabilities that better values of the characteristic are determined from the images generated with the different modes other than the initial subset based on the determined at least one value of the characteristic and a relationship between the different modes and associated values of the characteristic of the information. In addition, the computer subsystem is configured for selecting an additional subset of the different modes for which the generating and determining steps are performed next by the imaging and computer subsystems, respectively, based on the predicted probabilities.
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公开(公告)号：US20220383456A1
公开(公告)日：2022-12-01
申请号：US17721300
申请日：2022-04-14
Applicant: KLA Corporation
Inventor： Aditya Gulati , Raghavan Konuru , Niveditha Lakshmi Narasimhan , Saravanan Paramasivam , Martin Plihal , Prasanti Uppaluri
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. The one or more components include a deep learning model configured for denoising an image of a specimen generated by an imaging subsystem. The computer subsystem is configured for determining information for the specimen from the denoised image.
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Patent Agency Ranking
公开(公告)号：US12299848B2
公开(公告)日：2025-05-13
申请号：US17721300
申请日：2022-04-14
Applicant: KLA Corporation
Inventor： Aditya Gulati , Raghavan Konuru , Niveditha Lakshmi Narasimhan , Saravanan Paramasivam , Martin Plihal , Prasanti Uppaluri
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. The one or more components include a deep learning model configured for denoising an image of a specimen generated by an imaging subsystem. The computer subsystem is configured for determining information for the specimen from the denoised image.
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公开(公告)号：US11676264B2
公开(公告)日：2023-06-13
申请号：US16935159
申请日：2020-07-21
Applicant: KLA Corporation
Inventor： Martin Plihal , Saravanan Paramasivam , Jacob George , Niveditha Lakshmi Narasimhan , Sairam Ravu , Somesh Challapalli , Prasanti Uppaluri
IPC: G06T7/00 , G06N20/00 , G06F18/214 , G06F18/21 , G06V20/69
CPC classification number: G06T7/001 , G06F18/214 , G06F18/217 , G06N20/00 , G06V20/69 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
Abstract: A system for characterizing a specimen is disclosed. In one embodiment, the system includes a characterization sub-system configured to acquire one or more images a specimen, and a controller communicatively coupled to the characterization sub-system. The controller may be configured to: receive from the characterization sub-system one or more training images of one or more defects of a training specimen; generate one or more augmented images of the one or more defects of the training specimen; generate a machine learning classifier based on the one or more augmented images of the one or more defects of the training specimen; receive from the characterization sub-system one or more target images of one or more target features of a target specimen; and determine one or more defects of the one or more target features with the machine learning classifier.
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公开(公告)号：US11379967B2
公开(公告)日：2022-07-05
申请号：US16744385
申请日：2020-01-16
Applicant: KLA Corporation
Inventor： Jacob George , Saravanan Paramasivam , Martin Plihal , Niveditha Lakshmi Narasimhan , Sairam Ravu , Prasanti Uppaluri
Abstract: Methods and systems for improved detection and classification of defects of interest (DOI) is realized based on values of one or more automatically generated attributes derived from images of a candidate defect. Automatically generated attributes are determined by iteratively training, reducing, and retraining a deep learning model. The deep learning model relates optical images of candidate defects to a known classification of those defects. After model reduction, attributes of the reduced model are identified which strongly relate the optical images of candidate defects to the known classification of the defects. The reduced model is subsequently employed to generate values of the identified attributes associated with images of candidate defects having unknown classification. In another aspect, a statistical classifier is employed to classify defects based on automatically generated attributes and attributes identified manually.
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公开(公告)号：US20210035282A1
公开(公告)日：2021-02-04
申请号：US16940373
申请日：2020-07-27
Applicant: KLA CORPORATION
Inventor： Martin Plihal , Prasanti Uppaluri , Saravanan Paramasivam
Abstract: Machine learning approaches provide additional information about semiconductor wafer inspection stability issues that makes it possible to distinguish consequential process variations like process excursions from minor process variations that are within specification. The effect of variable defect of interest (DOI) capture rates in the inspection result and the effect of variable defect count on the wafer can be monitored independently.
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公开(公告)号：US20200334807A1
公开(公告)日：2020-10-22
申请号：US16921812
申请日：2020-07-06
Applicant: KLA Corporation
Inventor： Prasanti Uppaluri , Rajesh Manepalli , Ashok V. Kulkarni , Saibal Banerjee , John Kirkland
Abstract: A method includes receiving one or more sets of wafer data, identifying one or more primitives from one or more shapes in one or more layers in the one or more sets of wafer data, classifying each of the one or more primitives as a particular primitive type, identifying one or more primitive characteristics for each of the one or more primitives, generating a primitive database of the one or more primitives, generating one or more rules based on the primitive database, receiving one or more sets of design data, applying the one or more rules to the one or more sets of design data to identify one or more critical areas, and generating one or more wafer inspection recipes including the one or more critical areas for an inspection sub-system.
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