Metric for recognizing correct library spectrum

    公开(公告)号:US09679823B2

    公开(公告)日:2017-06-13

    申请号:US13840554

    申请日:2013-03-15

    CPC classification number: H01L22/26 B24B37/005 B24B49/12 H01L22/12

    Abstract: A method of controlling polishing of a substrate is described. A controller stores a library having a plurality of reference spectra. The controller polishes a substrate and measures a sequence of spectra of light from the substrate during polishing. For each measured spectrum of the sequence of spectra, the controller finds a best matching reference spectrum from the plurality of reference spectra and generates a sequence of best matching reference spectra. The controller uses a cell counting technique for finding the best matching reference spectrum. The controller determines at least one of a polishing endpoint or an adjustment for a polishing rate based on the sequence of best matching reference spectra.

    FILM THICKNESS ESTIMATION FROM MACHINE LEARNING BASED PROCESSING OF SUBSTRATE IMAGES

    公开(公告)号:US20210407065A1

    公开(公告)日:2021-12-30

    申请号:US17359307

    申请日:2021-06-25

    Abstract: A neural network is trained for use in a substrate thickness measurement system by obtaining ground truth thickness measurements of a top layer of a calibration substrate at a plurality of locations, each location at a defined position for a die being fabricated on the substrate. A plurality of color images of the calibration substrate are obtained, each color image corresponding to a region for a die being fabricated on the substrate. A neural network is trained to convert color images of die regions from an in-line substrate imager to thickness measurements for the top layer in the die region. The training is performed using training data that includes the plurality of color images and ground truth thickness measurements with each respective color image paired with a ground truth thickness measurement for the die region associated with the respective color image.

    System using film thickness estimation from machine learning based processing of substrate images

    公开(公告)号:US12169925B2

    公开(公告)日:2024-12-17

    申请号:US18500811

    申请日:2023-11-02

    Abstract: A neural network is trained for use in a substrate thickness measurement system by obtaining ground truth thickness measurements of a top layer of a calibration substrate at a plurality of locations, each location at a defined position for a die being fabricated on the substrate. A plurality of color images of the calibration substrate are obtained, each color image corresponding to a region for a die being fabricated on the substrate. A neural network is trained to convert color images of die regions from an in-line substrate imager to thickness measurements for the top layer in the die region. The training is performed using training data that includes the plurality of color images and ground truth thickness measurements with each respective color image paired with a ground truth thickness measurement for the die region associated with the respective color image.

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