公开(公告)号：US20220357673A1

公开(公告)日：2022-11-10

申请号：US17488010

申请日：2021-09-28

Applicant: KLA Corporation

Inventor： Stilian Pandev ,  Min-Yeong Moon ,  Andrei V. Shchegrov ,  Jonathan Madsen ,  Dimitry Sanko ,  Liran Yerushalmi ,  Alexander Kuznetsov ,  Mahendra Dubey

IPC: G03F7/20

Abstract: A self-calibrating overlay metrology system may receive device overlay data from device targets on a sample, determine preliminary device overlay measurements for the device targets including device-scale features using an overlay recipe with the device overlay data as inputs, receive assist overlay data from sets of assist targets on the sample including device-scale features, where a particular set of assist targets includes one or more target pairs formed with two overlay targets having programmed overlay offsets of a selected value with opposite signs along a particular measurement direction. The system may further determine self-calibrating assist overlay measurements for the sets of assist targets based on the assist overlay data, where the self-calibrating assist overlay measurements are linearly proportional to overlay on the sample, and generate corrected overlay measurements for the device targets by adjusting the preliminary device overlay measurements based on the self-calibrating assist overlay measurements.

公开(公告)号：US11880142B2

公开(公告)日：2024-01-23

申请号：US18118420

申请日：2023-03-07

Applicant: KLA Corporation

Inventor： Stilian Pandev ,  Min-Yeong Moon ,  Andrei V. Shchegrov ,  Jonathan Madsen ,  Dimitry Sanko ,  Liran Yerushalmi ,  Alexander Kuznetsov ,  Mahendra Dubey

IPC: G03F7/00

CPC classification number: G03F7/70633 ,  G03F7/70516 ,  G03F7/70775

Abstract: A self-calibrating overlay metrology system may receive device overlay data for a device targets on a sample from an overlay metrology tool, determine preliminary device overlay measurements for the device targets including device-scale features using an overlay recipe with the device overlay data as inputs, receive assist overlay data for one or more assist targets on the sample including device-scale features from the overlay metrology tool, where at least one of the one or more assist targets has a programmed overlay offset of a selected value along a particular measurement direction, determine self-calibrating assist overlay measurements for the one or more assist targets based on the assist overlay data, where the self-calibrating assist overlay measurements are linearly proportional to overlay on the sample, and generate corrected overlay measurements for the device targets by adjusting the preliminary device overlay measurements based on the self-calibrating assist overlay measurements.

公开(公告)号：US20230221656A1

公开(公告)日：2023-07-13

申请号：US18118420

申请日：2023-03-07

Applicant: KLA Corporation

Inventor： Stilian Pandev ,  Min-Yeong Moon ,  Andrei V. Shchegrov ,  Jonathan Madsen ,  Dimitry Sanko ,  Liran Yerushalmi ,  Alexander Kuznetsov ,  Mahendra Dubey

IPC: G03F7/20

CPC classification number: G03F7/70633 ,  G03F7/70516 ,  G03F7/70775

Abstract: A self-calibrating overlay metrology system may receive device overlay data for a device targets on a sample from an overlay metrology tool, determine preliminary device overlay measurements for the device targets including device-scale features using an overlay recipe with the device overlay data as inputs, receive assist overlay data for one or more assist targets on the sample including device-scale features from the overlay metrology tool, where at least one of the one or more assist targets has a programmed overlay offset of a selected value along a particular measurement direction, determine self-calibrating assist overlay measurements for the one or more assist targets based on the assist overlay data, where the self-calibrating assist overlay measurements are linearly proportional to overlay on the sample, and generate corrected overlay measurements for the device targets by adjusting the preliminary device overlay measurements based on the self-calibrating assist overlay measurements.

公开(公告)号：US20240120221A1

公开(公告)日：2024-04-11

申请号：US17959008

申请日：2022-10-03

Applicant: KLA Corporation

Inventor： Min-Yeong Moon ,  Phalguna Kumar Rachinayani ,  Jean-Christophe Perrin ,  Stilian Pandev

IPC: H01L21/67 ,  H01L21/66

CPC classification number: H01L21/67248 ,  H01L22/12

Abstract: Methods and systems for determining information for a specimen are provided. One system includes an output acquisition subsystem configured to generate output for a specimen at one or more target locations on the specimen and one or more temperature sensors configured to measure one or more temperatures within the system. The system also includes a deep learning model configured for predicting error in at least one of the one or more target locations based on at least one of the one or more measured temperatures input to the deep learning model by the computer subsystem. The computer subsystem is configured for determining a corrected target location for the at least one of the one or more target locations by applying the predicted error to the at least one of the one or more target locations.

公开(公告)号：US11796390B2

公开(公告)日：2023-10-24

申请号：US17856660

申请日：2022-07-01

Applicant: KLA Corporation

Inventor： Tianhan Wang ,  Aaron Rosenberg ,  Dawei Hu ,  Alexander Kuznetsov ,  Manh Dang Nguyen ,  Stilian Pandev ,  John Lesoine ,  Qiang Zhao ,  Liequan Lee ,  Houssam Chouaib ,  Ming Di ,  Torsten R. Kaack ,  Andrei V. Shchegrov ,  Zhengquan Tan

IPC: G01J3/18 ,  G01J3/28 ,  G01N21/55 ,  G01N21/956 ,  G01N21/21 ,  G01N21/25 ,  G01N21/88 ,  G01N21/84

CPC classification number: G01J3/18 ,  G01J3/28 ,  G01N21/21 ,  G01N21/25 ,  G01N21/55 ,  G01N21/8422 ,  G01N21/8851 ,  G01N21/956 ,  G01J2003/2836 ,  G01N2021/8883

Abstract: A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

公开(公告)号：US20240378483A1

公开(公告)日：2024-11-14

申请号：US18195115

申请日：2023-05-09

Applicant: KLA Corporation

Inventor： Stilian Pandev ,  Proteek Roy

IPC: G06N20/00

Abstract: A method for improving measurement performance of characterization systems is disclosed. The method may include training a plurality of machine learning models based on a set of training data, where each machine learning model is capable of generating an uncertainty estimator and a first machine learning model is different from one or more additional machine learning models based on one of a set of hyperparameters or a dataset. The method may further include receiving a plurality of sample measurement datasets from one or more test samples. For each of the plurality of sample measurement datasets, the method may further include applying each trained machine learning model to determine a measurement value and the uncertainty estimator for each trained machine learning model and generating a measurement output based on N trained machine learning models with the lowest uncertainty estimators.

公开(公告)号：US20240201637A1

公开(公告)日：2024-06-20

申请号：US18081191

申请日：2022-12-14

Applicant: KLA Corporation

Inventor： Stilian Pandev ,  Min-Yeong Moon

IPC: G05B13/02

CPC classification number: G05B13/0265

Abstract: A characterization system is disclosed. The system may include one or more controllers including one or more processors configured to execute a set of program instructions stored in memory. The controller may be configured to train a machine learning-based characterization library based on a set of training data. The controller may be configured to generate one or more characterization measurements using the trained machine learning-based characterization library based on the real-time characterization data associated with the one or more samples from the characterization sub-system. The controller may be configured to determine one or more additional characterization measurements based on a non-machine learning-based technique. The controller may be configured to compare the one or more characterization measurements and the one or more additional characterization measurements to monitor a measurement uncertainty of the machine learning-based characterization library.

公开(公告)号：US20220412734A1

公开(公告)日：2022-12-29

申请号：US17473742

申请日：2021-09-13

Applicant: KLA Corporation

Inventor： Stilian Pandev ,  Min-Yeong Moon ,  Andrei V. Shchegrov ,  Jonathan Madsen ,  Dimitry Sanko ,  Liran Yerushalmi ,  Alexander Kuznetsov ,  Mahendra Dubey

IPC: G01B21/24

Abstract: An overlay metrology system may receive overlay data for in-die overlay targets within various fields on a skew training sample from one or more overlay metrology tools, wherein the in-die overlay targets within the fields have a range programmed overlay offsets, wherein the fields are fabricated with a range of programmed skew offsets. The system may further generate asymmetric target signals for the in-die overlay targets using an asymmetric function providing a value of zero when physical overlay is zero and a sign indicative of a direction of physical overlay. The system may further generate corrected overlay offsets for the in-die overlay targets on the asymmetric target signals, generate self-calibrated overlay offsets for the in-die overlay targets based on the programmed overlay offsets and the corrected overlay offsets, generate a trained overlay recipe, and generate overlay measurements for in-die overlay targets on additional samples using the trained overlay recipe.

公开(公告)号：US20220349752A1

公开(公告)日：2022-11-03

申请号：US17856660

申请日：2022-07-01

Applicant: KLA Corporation

Inventor： Tianhan Wang ,  Aaron Rosenberg ,  Dawei Hu ,  Alexander Kuznetsov ,  Manh Dang Nguyen ,  Stilian Pandev ,  John Lesoine ,  Qiang Zhao ,  Liequan Lee ,  Houssam Chouaib ,  Ming Di ,  Torsten R. Kaack ,  Andrei V. Shchegrov ,  Zhengquan Tan

IPC: G01J3/18 ,  G01J3/28 ,  G01N21/55 ,  G01N21/956 ,  G01N21/21 ,  G01N21/25 ,  G01N21/88 ,  G01N21/84

Abstract: A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

公开(公告)号：US20210109453A1

公开(公告)日：2021-04-15

申请号：US16724058

申请日：2019-12-20

Applicant: KLA Corporation

Inventor： Stilian Pandev

IPC: G03F7/20 ,  H01L21/67 ,  G06N20/00 ,  G01B11/24

Abstract: First and second metrology data are used to train a machine-learning model to predict metrology data for a metrology target based on metrology data for a device area. The first metrology data are for a plurality of instances of a device area on semiconductor die fabricated using a fabrication process. The second metrology data are for a plurality of instances of a metrology target that contains structures distinct from structures in the device area. Using the trained machine-learning model, fourth metrology data are predicted for the metrology target based on third metrology data for an instance of the device area. Using a recipe for the metrology target, one or more parameters of the metrology target are determined based on the fourth metrology data. The fabrication process is monitored and controlled based at least in part on the one or more parameters.