Method for process monitoring with optical inspections

    公开(公告)号:US11379969B2

    公开(公告)日:2022-07-05

    申请号:US16940373

    申请日:2020-07-27

    申请人: KLA CORPORATION

    IPC分类号: G06T7/00 G06T7/246

    摘要: 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.

    Methods And Systems For Inspection Of Semiconductor Structures With Automatically Generated Defect Features

    公开(公告)号:US20200234428A1

    公开(公告)日:2020-07-23

    申请号:US16744385

    申请日:2020-01-16

    申请人: KLA Corporation

    IPC分类号: G06T7/00 G06N3/08 G06K9/62

    摘要: 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.

    System and Method for Determining Defects Using Physics-Based Image Perturbations

    公开(公告)号:US20210027445A1

    公开(公告)日:2021-01-28

    申请号:US16935159

    申请日:2020-07-21

    申请人: KLA Corporation

    IPC分类号: G06T7/00 G06K9/62 G06N20/00

    摘要: 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.

    Methods and systems for inspection of semiconductor structures with automatically generated defect features

    公开(公告)号:US11379967B2

    公开(公告)日:2022-07-05

    申请号:US16744385

    申请日:2020-01-16

    申请人: KLA Corporation

    IPC分类号: G06T7/00 G06N3/08 G06K9/62

    摘要: 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.

    METHOD FOR PROCESS MONITORING WITH OPTICAL INSPECTIONS

    公开(公告)号:US20210035282A1

    公开(公告)日:2021-02-04

    申请号:US16940373

    申请日:2020-07-27

    申请人: KLA CORPORATION

    IPC分类号: G06T7/00 G06T7/246

    摘要: 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.

    System and Method for Generation of Wafer Inspection Critical Areas

    公开(公告)号:US20200334807A1

    公开(公告)日:2020-10-22

    申请号:US16921812

    申请日:2020-07-06

    申请人: KLA Corporation

    IPC分类号: G06T7/00 H01L21/66

    摘要: 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.

    System and method for generation of wafer inspection critical areas

    公开(公告)号:US11410291B2

    公开(公告)日:2022-08-09

    申请号:US16921812

    申请日:2020-07-06

    申请人: KLA Corporation

    IPC分类号: G06T7/00 H01L21/66

    摘要: 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.