公开(公告)号：US20230013919A1

公开(公告)日：2023-01-19

申请号：US17950502

申请日：2022-09-22

Applicant: ASML Netherlands B.V.

Inventor： Marinus Aart Van Den Brink ,  Yu Cao ,  Yi Zou

IPC: G06F30/398 ,  G06F30/392

Abstract: A method for calibrating a process model and training an inverse process model of a patterning process. The training method includes obtaining a first patterning device pattern from simulation of an inverse lithographic process that predicts a patterning device pattern based on a wafer target layout, receiving wafer data corresponding to a wafer exposed using the first patterning device pattern, and training an inverse process model configured to predict a second patterning device pattern using the wafer data related to the exposed wafer and the first patterning device pattern.

公开(公告)号：US12055904B2

公开(公告)日：2024-08-06

申请号：US17293373

申请日：2019-10-30

Applicant: ASML NETHERLANDS B.V.

Inventor： Youping Zhang ,  Boris Menchtchikov ,  Cyrus Emil Tabery ,  Yi Zou ,  Chenxi Lin ,  Yana Cheng ,  Simon Philip Spencer Hastings ,  Maxime Philippe Frederic Genin

IPC: G06F30/10 ,  G03F7/00 ,  G05B13/02 ,  G05B13/04 ,  G06F30/27 ,  G06N3/045 ,  G06N3/08 ,  G06F119/02 ,  G06F119/22

CPC classification number: G05B13/048 ,  G03F7/705 ,  G03F7/70616 ,  G03F7/706837 ,  G05B13/027 ,  G05B13/042 ,  G06F30/10 ,  G06F30/27 ,  G06N3/045 ,  G06N3/08 ,  G06F2119/02 ,  G06F2119/22

Abstract: A method for predicting yield relating to a process of manufacturing semiconductor devices on a substrate, the method including: obtaining a trained first model which translates modeled parameters into a yield parameter, the modeled parameters including: a) a geometrical parameter associated with one or more selected from: a geometric characteristic, dimension or position of a device element manufactured by the process and b) a trained free parameter; obtaining process parameter data including data regarding a process parameter characterizing the process; converting the process parameter data into values of the geometrical parameter; and predicting the yield parameter using the trained first model and the values of the geometrical parameter.

公开(公告)号：US12038694B2

公开(公告)日：2024-07-16

申请号：US18118695

申请日：2023-03-07

Applicant: ASML NETHERLANDS B.V.

Inventor： Youping Zhang ,  Maxime Philippe Frederic Genin ,  Cong Wu ,  Jing Su ,  Weixuan Hu ,  Yi Zou

IPC: G03F7/00

CPC classification number: G03F7/705 ,  G03F7/70675

Abstract: Methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method involves obtaining a training data set including: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; and training a machine learning model for the patterning process based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model can be used for determining a ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

公开(公告)号：US11803127B2

公开(公告)日：2023-10-31

申请号：US17295193

申请日：2019-11-04

Applicant: ASML NETHERLANDS B.V.

Inventor： Chenxi Lin ,  Cyrus Emil Tabery ,  Hakki Ergün Cekli ,  Simon Philip Spencer Hastings ,  Boris Menchtchikov ,  Yi Zou ,  Yana Cheng ,  Maxime Philippe Frederic Genin ,  Tzu-Chao Chen ,  Davit Harutyunyan ,  Youping Zhang

IPC: G03F7/00 ,  G05B13/02 ,  G05B19/418 ,  H01L21/66

CPC classification number: G03F7/705 ,  G05B13/027 ,  G05B19/41875 ,  H01L22/20 ,  G05B2219/32193 ,  G05B2219/32368 ,  G05B2219/45031

Abstract: A method for determining a root cause affecting yield in a process for manufacturing devices on a substrate, the method including: obtaining yield distribution data including a distribution of a yield parameter across the substrate or part thereof; obtaining sets of metrology data, each set including a spatial variation of a process parameter over the substrate or part thereof corresponding to a different layer of the substrate; comparing the yield distribution data and metrology data based on a similarity metric describing a spatial similarity between the yield distribution data and an individual set out of the sets of the metrology data; and determining a first similar set of metrology data out of the sets of metrology data, being the first set of metrology data in terms of processing order for the corresponding layers, which is determined to be similar to the yield distribution data.

公开(公告)号：US12093632B2

公开(公告)日：2024-09-17

申请号：US17950502

申请日：2022-09-22

Applicant: ASML NETHERLANDS B.V.

Inventor： Marinus Aart Van Den Brink ,  Yu Cao ,  Yi Zou

IPC: G06F30/398 ,  G06F30/392 ,  G06F119/18

CPC classification number: G06F30/398 ,  G06F30/392 ,  G06F2119/18

Abstract: A method for calibrating a process model and training an inverse process model of a patterning process. The training method includes obtaining a first patterning device pattern from simulation of an inverse lithographic process that predicts a patterning device pattern based on a wafer target layout, receiving wafer data corresponding to a wafer exposed using the first patterning device pattern, and training an inverse process model configured to predict a second patterning device pattern using the wafer data related to the exposed wafer and the first patterning device pattern.

公开(公告)号：US12044980B2

公开(公告)日：2024-07-23

申请号：US17296316

申请日：2019-10-30

Applicant: ASML NETHERLANDS B.V.

Inventor： Abraham Slachter ,  Wim Tjibbo Tel ,  Daan Maurits Slotboom ,  Vadim Yourievich Timoshkov ,  Koen Wilhelmus Cornelis Adrianus Van Der Straten ,  Boris Menchtchikov ,  Simon Philip Spencer Hastings ,  Cyrus Emil Tabery ,  Maxime Philippe Frederic Genin ,  Youping Zhang ,  Yi Zou ,  Chenxi Lin ,  Yana Cheng

IPC: G05B19/418 ,  G03F7/00

CPC classification number: G03F7/70625 ,  G03F7/70616 ,  G05B19/4188 ,  G03F7/70525

Abstract: A method for analyzing a process, the method including obtaining a multi-dimensional probability density function representing an expected distribution of values for a plurality of process parameters; obtaining a performance function relating values of the process parameters to a performance metric of the process; and using the performance function to map the probability density function to a performance probability function having the process parameters as arguments.

公开(公告)号：US20230236512A1

公开(公告)日：2023-07-27

申请号：US18118695

申请日：2023-03-07

Applicant: ASML NETHERLANDS B.V.

Inventor： Youping ZHANG ,  Maxime Philippe Frederic Genin ,  Cong Wu ,  Jing Su ,  Weixuan Hu ,  Yi Zou

IPC: G03F7/20

CPC classification number: G03F7/705 ,  G03F7/70675

Abstract: Methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method involves obtaining a training data set including: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; and training a machine learning model for the patterning process based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model can be used for determining a ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

公开(公告)号：US20220277116A1

公开(公告)日：2022-09-01

申请号：US17744091

申请日：2022-05-13

Applicant: ASML NETHERLANDS B.V.

Inventor： Jing SU ,  Yi Zou ,  Chenxi Lin ,  Stefan Hunsche ,  Marinus Jochemsen ,  Yen-Wen Lu ,  Lin Lee Cheong

IPC: G06F30/20 ,  G03F7/20 ,  G06T7/00 ,  G06K9/62 ,  G06F30/398 ,  G06N20/00

Abstract: Methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of one or more characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective; obtaining a characteristic of each of the process conditions; obtaining a characteristic of each of the hot spots; and training a machine learning model using a training set including the characteristic of one of the process conditions, the characteristic of one of the hot spots, and whether that hot spot is defective under that process condition.

公开(公告)号：US11183434B2

公开(公告)日：2021-11-23

申请号：US16466665

申请日：2017-12-13

Applicant: ASML NETHERLANDS B.V.

Inventor： Yu Cao ,  Yi Zou ,  Chenxi Lin

IPC: H01L21/66 ,  G03F7/20 ,  G05B19/418 ,  G06T7/00

Abstract: A method where deviations of a characteristic of an image simulated by two different process models or deviations of the characteristic simulated by a process model and measured by a metrology tool, are used for various purposes such as to reduce the calibration time, improve the accuracy of the model, and improve the overall manufacturing process.

公开(公告)号：US11947266B2

公开(公告)日：2024-04-02

申请号：US17297171

申请日：2019-11-14

Applicant: ASML NETHERLANDS B.V.

Inventor： Nicolaas Petrus Marcus Brantjes ,  Matthijs Cox ,  Boris Menchtchikov ,  Cyrus Emil Tabery ,  Youping Zhang ,  Yi Zou ,  Chenxi Lin ,  Yana Cheng ,  Simon Philip Spencer Hastings ,  Maxim Philippe Frederic Genin

IPC: G03F7/00 ,  G03F9/00

CPC classification number: G03F7/70491 ,  G03F7/70633 ,  G03F9/7046

Abstract: A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.