公开(公告)号：US20240085321A1

公开(公告)日：2024-03-14

申请号：US18136739

申请日：2023-04-19

Applicant: KLA Corporation

Inventor： John Hench ,  Akshay Krishna ,  Christopher Liman ,  Jeremy Smith ,  Liang Yin ,  Hyowon Park ,  Tianhan Wang ,  Boxue Chen

IPC: G01N21/47

CPC classification number: G01N21/4738

Abstract: Methods and systems for performing model-less measurements of semiconductor structures based on scatterometry measurement data are described herein. Scatterometry measurement data is processed directly, without the use of a traditional measurement model. Measurement sensitivity is defined by the changes in detected diffraction images at one or more non-zero diffraction orders over at least two different illumination incidence angles. Discrete values of a scalar function are determined directly from measured images at each incidence angle. A continuous mathematical function is fit to the set of discrete values of the scalar function determined at each incidence angle. A value of a parameter of interest is determined based on analysis of the mathematical function. In some embodiments, the scalar function includes a weighting function, and the weighting values associated with weighting function are optimized to yield an accurate fit of the mathematical function to the scalar values.

公开(公告)号：US20230169255A1

公开(公告)日：2023-06-01

申请号：US17993565

申请日：2022-11-23

Applicant: KLA Corporation

Inventor： Stilian Ivanov Pandev ,  Arvind Jayaraman ,  Proteek Chandan Roy ,  Hyowon Park ,  Antonio Arion Gellineau ,  Sungchol Yoo

IPC: G06F30/398

CPC classification number: G06F30/398

Abstract: Methods and systems for generating optimized geometric models of semiconductor structures parameterized by a set of variables in a latent mathematical space are presented herein. Reference shape profiles characterize the shape of a semiconductor structure of interest over a process space. A set of observable geometric variables describing the reference shape profiles is transformed to a set of latent variables. The number of latent variables is smaller than the number of observable geometric variables, thus the dimension of the parameter space employed to characterize the structure of interest is reduced. This dramatically reduces the mathematical dimension of the measurement problem to be solved. As a result, measurement model solutions involving regression are more robust, and training of machine learning based measurement models is simplified. Geometric models parameterized by a set of latent variables are useful for generating measurement models for optical metrology, x-ray metrology, and electron beam based metrology.

公开(公告)号：US20250146961A1

公开(公告)日：2025-05-08

申请号：US18430350

申请日：2024-02-01

Applicant: KLA Corporation

Inventor： Daniel James Haxton ,  Christopher Liman ,  InKyo Kim ,  Boxue Chen ,  Hyowon Park ,  Thaddeus Gerard Dziura ,  Nakyoon Kim ,  Houssam Chouaib ,  Anderson Chou ,  Dimitry Sanko

IPC: G01N23/20 ,  G01N21/95

Abstract: Methods and systems for determining random variation in one or more structures on a specimen are provided. One method includes determining characteristic(s) of output generated by an output acquisition subsystem for structure(s) formed on a specimen and simulating the characteristic(s) of the output with initial parameter values for the structure(s). The method also includes determining parameter values of the structure(s) formed on the specimen as the initial parameter values that resulted in the simulated characteristic(s) that best match the determined characteristic(s). The determined parameter values are responsive to random variation in parameter(s) of the structure(s) on the specimen.

公开(公告)号：US20240302301A1

公开(公告)日：2024-09-12

申请号：US18416113

申请日：2024-01-18

Applicant: KLA Corporation

Inventor： Sandeep Inampudi ,  Hyowon Park ,  Daniel Haxton ,  Boxue Chen ,  Sungchul Yoo ,  Robert D. Press

IPC: G01N23/205 ,  G01N23/2055 ,  H01L21/66

CPC classification number: G01N23/205 ,  G01N23/2055 ,  G01N2223/6116 ,  H01L22/12

Abstract: Methods and systems for performing measurements of stacked semiconductor structures, e.g., stacked memory and logic structures, based on X-Ray transmission scatterometry measurement data are described herein. In some examples, the scattering response of logic structures is modelled directly in signal space by a mathematical expression including a relatively small number of weighted basis functions. The scattering response of the logic structures and the scattering response of the memory structures determined by an electromagnetic response model are combined, e.g., by summation or convolution. The combined modelled signals are compared to the measured signals at the detector to generate an error signal. The error signal is employed to drive a regression analysis employed to optimize parameter values characterizing the memory structures, values of the weighting coefficients of the signal space model, or both. In other examples, the scattering response of the logic structures is known, and a model is not needed.

公开(公告)号：US12019030B2

公开(公告)日：2024-06-25

申请号：US17578310

申请日：2022-01-18

Applicant: KLA Corporation

Inventor： Antonio Arion Gellineau ,  Andrei V. Shchegrov ,  Hyowon Park ,  Pavan Gurudath ,  Christopher Liman ,  Jung Heon Song

IPC: G01N21/95 ,  G01N21/88

CPC classification number: G01N21/9501 ,  G01N2021/8845 ,  G01N2021/8848 ,  G01N2021/8883

Abstract: Methods and systems for monitoring the quality of a semiconductor measurement in a targeted manner are presented herein. Rather than relying on one or more general indices to determine overall measurement quality, one or more targeted measurement quality indicators are determined. Each targeted measurement quality indicator provides insight into whether a specific operational issue is adversely affecting measurement quality. In this manner, the one or more targeted measurement quality indicators not only highlight deficient measurements, but also provide insight into specific operational issues contributing to measurement deficiency. In some embodiments, values of one or more targeted measurement quality indicators are determined based on features extracted from measurement data. In some embodiments, values of one or more targeted measurement quality indicators are determined based on features extracted from one or more indications of a comparison between measurement data and corresponding measurement data simulated by a trained measurement model.

公开(公告)号：US20230228692A1

公开(公告)日：2023-07-20

申请号：US17578310

申请日：2022-01-18

Applicant: KLA Corporation

Inventor： Antonio Arion Gellineau ,  Andrei V. Shchegrov ,  Hyowon Park ,  Pavan Gurudath ,  Christopher Liman ,  Jung Heon Song

IPC: G01N21/95

CPC classification number: G01N21/9501 ,  G01N2021/8845

Abstract: Methods and systems for monitoring the quality of a semiconductor measurement in a targeted manner are presented herein. Rather than relying on one or more general indices to determine overall measurement quality, one or more targeted measurement quality indicators are determined. Each targeted measurement quality indicator provides insight into whether a specific operational issue is adversely affecting measurement quality. In this manner, the one or more targeted measurement quality indicators not only highlight deficient measurements, but also provide insight into specific operational issues contributing to measurement deficiency. In some embodiments, values of one or more targeted measurement quality indicators are determined based on features extracted from measurement data. In some embodiments, values of one or more targeted measurement quality indicators are determined based on features extracted from one or more indications of a comparison between measurement data and corresponding measurement data simulated by a trained measurement model.