公开(公告)号：US20240169116A1

公开(公告)日：2024-05-23

申请号：US18334357

申请日：2023-06-13

Applicant: KLA Corporation

Inventor： Brian Duffy ,  Kris Bhaskar

IPC: G06F30/20 ,  G06F30/3308

CPC classification number: G06F30/20 ,  G06F30/3308

Abstract: Methods and systems for performing functions with protected data sources from different entities are provided. One system includes a virtual system coupled to an actual system to thereby receive output generated by the actual system for a physical version of the specimen while the specimen is disposed within the actual system. The virtual system includes at least a computer system and a storage medium. The virtual system is not capable of having the physical version of the specimen disposed therein. The virtual system is configured for performing one or more functions for the specimen with two or more protected data sources from two or more different entities, respectively. The virtual system is also configured for performing a virtual version of a process capable of being performed by the actual system for the physical version of the specimen.

公开(公告)号：US11644756B2

公开(公告)日：2023-05-09

申请号：US17393979

申请日：2021-08-04

Applicant: KLA Corporation

Inventor： Scott A. Young ,  Kris Bhaskar ,  Lena Nicolaides

IPC: G03F7/20 ,  G06T7/00 ,  G06N3/04

CPC classification number: G03F7/7065 ,  G03F7/70508 ,  G03F7/70666 ,  G06N3/04 ,  G06T7/0004 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30148

Abstract: Methods and systems for determining information for a specimen are provided. Certain embodiments relate to bump height 3D inspection and metrology using deep learning artificial intelligence. For example, one embodiment includes a deep learning (DL) model configured for predicting height of one or more 3D structures formed on a specimen based on one or more images of the specimen generated by an imaging subsystem. One or more computer systems are configured for determining information for the specimen based on the predicted height. Determining the information may include, for example, determining if any of the 3D structures are defective based on the predicted height. In another example, the information determined for the specimen may include an average height metric for the one or more 3D structures.

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.