公开(公告)号：US10186026B2

公开(公告)日：2019-01-22

申请号：US15353210

申请日：2016-11-16

Applicant: KLA-Tencor Corporation

Inventor： Laurent Karsenti ,  Kris Bhaskar ,  John Raymond Jordan, III ,  Sankar Venkataraman ,  Yair Carmon

IPC: G06K9/00 ,  G06T7/00 ,  G06K9/62 ,  G06K9/46

Abstract: Methods and systems for detecting defects on a specimen are provided. One system includes a generative model. The generative model includes a non-linear network configured for mapping blocks of pixels of an input feature map volume into labels. The labels are indicative of one or more defect-related characteristics of the blocks. The system inputs a single test image into the generative model, which determines features of blocks of pixels in the single test image and determines labels for the blocks based on the mapping. The system detects defects on the specimen based on the determined labels.

公开(公告)号：US20170140524A1

公开(公告)日：2017-05-18

申请号：US15353210

申请日：2016-11-16

Applicant: KLA-Tencor Corporation

Inventor： Laurent Karsenti ,  Kris Bhaskar ,  John Raymond Jordan, III ,  Sankar Venkataraman ,  Yair Carmon

IPC: G06T7/00 ,  G06K9/62 ,  G06K9/46

CPC classification number: G06T7/0004 ,  G06K9/4642 ,  G06K9/6256 ,  G06K9/6269 ,  G06K9/6287 ,  G06T2207/10061 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30148

Abstract: Methods and systems for detecting defects on a specimen are provided. One system includes a generative model. The generative model includes a non-linear network configured for mapping blocks of pixels of an input feature map volume into labels. The labels are indicative of one or more defect-related characteristics of the blocks. The system inputs a single test image into the generative model, which determines features of blocks of pixels in the single test image and determines labels for the blocks based on the mapping. The system detects defects on the specimen based on the determined labels.

公开(公告)号：US20180293721A1

公开(公告)日：2018-10-11

申请号：US15896060

申请日：2018-02-14

Applicant: KLA-Tencor Corporation

Inventor： Ajay Gupta ,  Mohan Mahadevan ,  Sankar Venkataraman ,  Hedong Yang ,  Laurent Karsenti ,  Yair Carmon ,  Noga Bullkich ,  Udy Danino

IPC: G06T7/00 ,  G06T7/564 ,  G01N21/95 ,  G01N21/956 ,  G01N21/88

CPC classification number: G06T7/001 ,  G01N21/8851 ,  G01N21/9501 ,  G01N21/956 ,  G01N21/95607 ,  G01N2021/8867 ,  G01N2021/8887 ,  G05B19/41875 ,  G06N20/00 ,  G06T7/564 ,  G06T2207/10061 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30148 ,  H01L22/12 ,  H01L22/20

Abstract: Methods and systems for detecting defects in patterns formed on a specimen are provided. One system includes one or more components executed by one or more computer subsystems, and the component(s) include first and second learning based models. The first learning based model generates simulated contours for the patterns based on a design for the specimen, and the simulated contours are expected contours of a defect free version of the patterns in images of the specimen generated by an imaging subsystem. The second learning based model is configured for generating actual contours for the patterns in at least one acquired image of the patterns formed on the specimen. The computer subsystem(s) are configured for comparing the actual contours to the simulated contours and detecting defects in the patterns formed on the specimen based on results of the comparing.

公开(公告)号：US10395362B2

公开(公告)日：2019-08-27

申请号：US15896060

申请日：2018-02-14

Applicant: KLA-Tencor Corporation

Inventor： Ajay Gupta ,  Mohan Mahadevan ,  Sankar Venkataraman ,  Hedong Yang ,  Laurent Karsenti ,  Yair Carmon ,  Noga Bullkich ,  Udy Danino

IPC: G06T7/00 ,  G06T7/564 ,  G01N21/95 ,  G01N21/88 ,  G01N21/956

Abstract: Methods and systems for detecting defects in patterns formed on a specimen are provided. One system includes one or more components executed by one or more computer subsystems, and the component(s) include first and second learning based models. The first learning based model generates simulated contours for the patterns based on a design for the specimen, and the simulated contours are expected contours of a defect free version of the patterns in images of the specimen generated by an imaging subsystem. The second learning based model is configured for generating actual contours for the patterns in at least one acquired image of the patterns formed on the specimen. The computer subsystem(s) are configured for comparing the actual contours to the simulated contours and detecting defects in the patterns formed on the specimen based on results of the comparing.