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

IPC: G01N21/95 ,  G06T7/00

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.

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.

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

IPC: G06T7/00 ,  G01N21/95

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.

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.

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

IPC: G06T7/00 ,  G01N21/95

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.

公开(公告)号：US20230136110A1

公开(公告)日：2023-05-04

申请号：US17677887

申请日：2022-02-22

Applicant: KLA Corporation

Inventor： Rajkumar Theagarajan ,  Jing Zhang ,  Yujie Dong ,  Kris Bhaskar

IPC: G06N5/02 ,  G06N20/20 ,  G06V10/774

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 multiple deep learning (DL) models configured for determining information for a specimen based on output generated by the specimen with learning mode(s) of an imaging subsystem. The one or more components also include a knowledge distillation component configured for combining output generated by the multiple DL models. In addition, the one or more components include a final knowledge distilled DL model configured for determining information for the specimen or an additional specimen based on output generated for the specimen or the additional specimen with runtime mode(s) of the imaging subsystem. Before the final KD DL model determines the information, the knowledge distillation component is configured for supervised training of the final knowledge distilled DL model using the combined output.

公开(公告)号：US20200327654A1

公开(公告)日：2020-10-15

申请号：US16838037

申请日：2020-04-02

Applicant: KLA Corporation

Inventor： Jing Zhang ,  Zhuoning Yuan ,  Yujie Dong ,  Kris Bhaskar

IPC: G06T7/00 ,  G06N3/04 ,  G06N20/00

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.

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

IPC: G06T7/00 ,  G06N3/04 ,  G06N20/00

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.

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

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06T7/00 ,  G06N20/00

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.