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.