公开(公告)号：US11530913B2

公开(公告)日：2022-12-20

申请号：US17030690

申请日：2020-09-24

Applicant: KLA Corporation

Inventor： Dzmitry Sanko ,  Min-Yeong Moon ,  Stilian Ivanov Pandev

IPC: G01B11/25 ,  G01B15/04

Abstract: Methods and systems for estimating a value of a quality metric indicative of one or more performance characteristics of a semiconductor measurement are presented herein. The value of the quality metric is normalized to ensure applicability across a broad range of measurement scenarios. In some embodiments, a value of a quality metric is determined for each measurement sample during measurement inference. In some embodiments, a trained quality metric model is employed to determine the uncertainty of defect classification. In some embodiments, a trained quality metric model is employed to determine the uncertainty of estimated parameters of interest, such as geometric, dispersion, process, and electrical parameters. In some examples, a quality metric is employed as a filter to detect measurement outliers. In some other examples, a quality metric is employed as a trigger to adjust a semiconductor process.

公开(公告)号：US11520321B2

公开(公告)日：2022-12-06

申请号：US17065021

申请日：2020-10-07

Applicant: KLA Corporation

Inventor： Stilian Ivanov Pandev ,  Wei Lu ,  Dzmitry Sanko

IPC: G05B19/418 ,  G06T7/00 ,  G06F30/367

Abstract: Methods and systems for training and implementing metrology recipes based on performance metrics employed to quantitatively characterize the measurement performance of a metrology system in a particular measurement application. Performance metrics are employed to regularize the optimization process employed during measurement model training, model-based regression, or both. For example, the known distributions associated with important measurement performance metrics such as measurement precision, wafer mean, etc., are specifically employed to regularize the optimization that drives measurement model training. In a further aspect, a trained measurement model is employed to estimate values of parameters of interest based on measurements of structures having unknown values of one or more parameters of interest. In a further aspect, trained measurement model performance is validated with test data using error budget analysis. In another aspect, a model-based regression on a measurement model is physically regularized by on one or more measurement performance metrics.

公开(公告)号：US20210165398A1

公开(公告)日：2021-06-03

申请号：US17065021

申请日：2020-10-07

Applicant: KLA Corporation

Inventor： Stilian Ivanov Pandev ,  Wei Lu ,  Dzmitry Sanko

IPC: G05B19/418 ,  G06F30/367 ,  G06T7/00

Abstract: Methods and systems for training and implementing metrology recipes based on performance metrics employed to quantitatively characterize the measurement performance of a metrology system in a particular measurement application. Performance metrics are employed to regularize the optimization process employed during measurement model training, model-based regression, or both. For example, the known distributions associated with important measurement performance metrics such as measurement precision, wafer mean, etc., are specifically employed to regularize the optimization that drives measurement model training. In a further aspect, a trained measurement model is employed to estimate values of parameters of interest based on measurements of structures having unknown values of one or more parameters of interest. In a further aspect, trained measurement model performance is validated with test data using error budget analysis. In another aspect, a model-based regression on a measurement model is physically regularized by on one or more measurement performance metrics.

公开(公告)号：US20220090912A1

公开(公告)日：2022-03-24

申请号：US17030690

申请日：2020-09-24

Applicant: KLA Corporation

Inventor： Dzmitry Sanko ,  Min-Yeong Moon ,  Stilian Ivanov Pandev

IPC: G01B11/25 ,  G01B15/04

Abstract: Methods and systems for estimating a value of a quality metric indicative of one or more performance characteristics of a semiconductor measurement are presented herein. The value of the quality metric is normalized to ensure applicability across a broad range of measurement scenarios. In some embodiments, a value of a quality metric is determined for each measurement sample during measurement inference. In some embodiments, a trained quality metric model is employed to determine the uncertainty of defect classification. In some embodiments, a trained quality metric model is employed to determine the uncertainty of estimated parameters of interest, such as geometric, dispersion, process, and electrical parameters. In some examples, a quality metric is employed as a filter to detect measurement outliers. In some other examples, a quality metric is employed as a trigger to adjust a semiconductor process.