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    DETECTION AND MEASUREMENT OF DIMENSIONS OF ASYMMETRIC STRUCTURES
    1.
    发明申请
    DETECTION AND MEASUREMENT OF DIMENSIONS OF ASYMMETRIC STRUCTURES 审中-公开

    公开(公告)号：WO2019067375A1

    公开(公告)日：2019-04-04

    申请号：PCT/US2018/052507

    申请日：2018-09-25

    Applicant: KLA-TENCOR CORPORATION

    Inventor： ATKINS, Phillip R. ,  DAI, Qi ,  LEE, Lie-Quan Rich

    IPC: H01L21/66 ,  G01D21/02

    Abstract: Methods and systems for performing spectroscopic measurements of asymmetric features of semiconductor structures are presented herein. In one aspect, measurements are performed at two or more azimuth angles to ensure sensitivity to an arbitrarily oriented asymmetric feature. Spectra associated with one or more off-diagonal Mueller matrix elements sensitive to asymmetry are integrated over wavelength to determine one or more spectral response metrics. In some embodiments, the integration is performed over one or more wavelength sub-regions selected to increase signal to noise ratio. Values of parameters characterizing an asymmetric feature are determined based on the spectral response metrics and critical dimension parameters measured by traditional spectral matching based techniques.

    MULTI-MODEL METROLOGY
    3.
    发明申请
    MULTI-MODEL METROLOGY 审中-公开
    多模式计量学

    公开(公告)号：WO2015027088A1

    公开(公告)日：2015-02-26

    申请号：PCT/US2014/052142

    申请日：2014-08-21

    Applicant: KLA-TENCOR CORPORATION

    Inventor： KIM, In-Kyo ,  LI, Xin ,  POSLAVSKY, Leonid ,  LEE, Lie-Quan Rich ,  CAO, Meng ,  YOO, Sungchul ,  PARK, Sangbong ,  SHCHEGROV, Andrei V.

    IPC: H01L21/66

    CPC classification number: H01L22/20 ,  G03F7/70616 ,  G03F7/70625 ,  G06F17/5068 ,  H01L21/67288 ,  H01L22/12

    Abstract: Disclosed are apparatus and methods for characterizing a plurality of structures of interest on a semiconductor wafer. A plurality of models having varying combinations of floating and fixed critical parameters and corresponding simulated spectra is generated. Each model is generated to determine one or more critical parameters for unknown structures based on spectra collected from such unknown structures. It is determined which one of the models best correlates with each critical parameter based on reference data that includes a plurality of known values for each of a plurality of critical parameters and corresponding known spectra. For spectra obtained from an unknown structure using a metrology tool, different ones of the models are selected and used to determine different ones of the critical parameters of the unknown structure based on determining which one of the models best correlates with each critical parameter based on the reference data.

    Abstract translation: 公开了用于在半导体晶片上表征感兴趣的多个结构的装置和方法。 产生具有浮动和固定关键参数和对应的模拟光谱的变化组合的多个模型。 生成每个模型以基于从这样的未知结构收集的光谱来确定未知结构的一个或多个关键参数。 基于包括多个关键参数和相应的已知光谱中的每一个的多个已知值的参考数据，确定哪一个模型与每个关键参数最佳相关。 对于使用计量工具从未知结构获得的光谱，选择不同的模型并且用于基于确定哪一个模型与每个关键参数最相关的来确定未知结构的关键参数的不同的参数 参考数据。

    AUTOMATIC OPTIMIZATION OF MEASUREMENT ACCURACY THROUGH ADVANCED MACHINE LEARNING TECHNIQUES
    4.
    发明申请
    AUTOMATIC OPTIMIZATION OF MEASUREMENT ACCURACY THROUGH ADVANCED MACHINE LEARNING TECHNIQUES 审中-公开

    公开(公告)号：WO2019099594A1

    公开(公告)日：2019-05-23

    申请号：PCT/US2018/061155

    申请日：2018-11-14

    Applicant: KLA-TENCOR CORPORATION

    Inventor： ZHAN, Tianrong ,  XU, Yin ,  LEE, Lie-Quan Rich

    IPC: G06N3/04 ,  G06N3/08

    Abstract: Machine learning techniques are used to predict values of fixed parameters when given reference values of critical parameters. For example, a neural network can be trained based on one or more critical parameters and a low-dimensional real-valued vector associated with a spectrum, such as a spectroscopic ellipsometry spectrum or a specular reflectance spectrum. Another neural network can map the low-dimensional real-valued vector. When using two neural networks, one neural network can be trained to map the spectra to the low-dimensional real-valued vector. Another neural network can be trained to predict the fixed parameter based on the critical parameters and the low-dimensional real-valued vector from the other neural network.

    LIBRARY EXPANSION SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR METROLOGY
    7.
    发明申请
    LIBRARY EXPANSION SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR METROLOGY 审中-公开
    图书馆扩展系统，方法和计算机程序产品

    公开(公告)号：WO2015175900A1

    公开(公告)日：2015-11-19

    申请号：PCT/US2015/031015

    申请日：2015-05-15

    Applicant: KLA-TENCOR CORPORATION

    Inventor： POSLAVSKY, Leonid ,  LEE, Lie-Quan Rich

    IPC: G06T15/00

    CPC classification number: G01B11/02 ,  G01B11/00 ,  G01B21/04 ,  G01N21/211 ,  G01N21/9501 ,  G01N21/95607 ,  G01N2021/213 ,  G01N2021/95615 ,  G03F7/70625

    Abstract: A library expansion system, method, and computer program product for metrology are provided. In use, processing within a first multi-dimensional library is performed by a metrology system. During the processing within the first multi-dimensional library, a second multi-dimensional library is identified. The processing is then transitioned to the second multi-dimensional library. Further, processing within the second multi-dimensional library is performed by the metrology system.

    Abstract translation: 提供了图书馆扩展系统，方法和计算机程序产品。 在使用中，第一多维库中的处理由计量系统执行。 在第一多维库中的处理期间，识别第二个多维库。 然后处理过渡到第二个多维库。 此外，第二多维库中的处理由计量系统执行。

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