中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

12 records (took 0.03 seconds)

    MODELING METHOD FOR COMPUTATIONAL FINGERPRINTS
    1.
    发明申请
    MODELING METHOD FOR COMPUTATIONAL FINGERPRINTS 审中-公开

    公开(公告)号：WO2021028126A1

    公开(公告)日：2021-02-18

    申请号：PCT/EP2020/069355

    申请日：2020-07-09

    Applicant: ASML NETHERLANDS B.V.

    Inventor： SU, Jing ,  CHENG, Yana ,  LIN, Chenxi ,  ZOU, Yi ,  HARUTYUNYAN, Davit ,  SCHMITT-WEAVER, Emil, Peter ,  BHATTACHARYYA, Kaustuve ,  LAMBREGTS, Cornelis, Johannes, Henricus ,  YAGUBIZADE, Hadi

    IPC: G03F7/20 ,  H01L21/66

    Abstract: Described herein is a method for determining a model to predict overlay data associated with a current substrate being patterned. The method involves obtaining (i) a first data set associated with one or more prior layers and/or current layer of the current substrate, (ii) a second data set comprising overlay metrology data associated with one or more prior substrates, and (iii) de-corrected measured overlay data associated with the current layer of the current substrate; and determining, based on (i) the first data set, (ii) the second data set, and (iii) the de-corrected measured overlay data, values of a set of model parameters associated with the model such that the model predicts the overlay data for the current substrate, wherein the values are determined such that a cost function is minimized, the cost function comprises a difference between the predicted data and the de-corrected measured overlay data.

    METHOD FOR DETERMINING CURVILINEAR PATTERNS FOR PATTERNING DEVICE
    2.
    发明申请
    METHOD FOR DETERMINING CURVILINEAR PATTERNS FOR PATTERNING DEVICE 审中-公开

    公开(公告)号：WO2019179747A1

    公开(公告)日：2019-09-26

    申请号：PCT/EP2019/055067

    申请日：2019-02-28

    Applicant: ASML NETHERLANDS B.V.

    Inventor： ZHANG, Quan ,  CHEN, Been-Der ,  HOWELL, Rafael C. ,  SU, Jing ,  ZOU, Yi ,  LU, Yen-Wen

    IPC: G03F1/36 ,  G03F1/70 ,  G03F7/20 ,  G06F17/50

    Abstract: Described herein is a method to determine a curvilinear pattern of a patterning device that includes obtaining (i) an initial image of the patterning device corresponding to a target pattern to be printed on a substrate subjected to a patterning process, and (ii) a process model configured to predict a pattern on the substrate from the initial image, generating, by a hardware computer system, an enhanced image from the initial image, generating, by the hardware computer system, a level set image using the enhanced image, and iteratively determining, by the hardware computer system, a curvilinear pattern for the patterning device based on the level set image, the process model, and a cost function, where the cost function (e.g., EPE) determines a difference between a predicted pattern and the target pattern, where the difference is iteratively reduced.

    METHOD FOR TRAINING MACHINE LEARNING MODEL TO DETERMINE OPTICAL PROXIMITY CORRECTION FOR MASK
    4.
    发明申请
    METHOD FOR TRAINING MACHINE LEARNING MODEL TO DETERMINE OPTICAL PROXIMITY CORRECTION FOR MASK 审中-公开

    公开(公告)号：WO2020169303A1

    公开(公告)日：2020-08-27

    申请号：PCT/EP2020/051778

    申请日：2020-01-24

    Applicant: ASML NETHERLANDS B.V.

    Inventor： TAO, Jun ,  BARON, Stanislas, Hugo, Louis ,  SU, Jing ,  LUO, Ya ,  CAO, Yu

    IPC: G03F1/36 ,  G03F7/20 ,  G06N3/08 ,  G06N20/00

    Abstract: Described herein are training methods and a mask correction method. One of the methods is for training a machine learning model configured to predict a post optimal proximity correction (OPC) image for a mask. The method involves obtaining (i) a pre-OPC image associated with a design layout to be printed on a substrate, (ii) an image of one or more assist features for the mask associated with the design layout, and (iii) a reference post- OPC image of the design layout; and training the machine learning model using the pre-OPC image and the image of the one or more assist features as input such that a difference between the reference image and a predicted post-OPC image of the machine learning model is reduced.

    COLORING AWARE OPTIMIZATION
    5.
    发明申请
    COLORING AWARE OPTIMIZATION 审中-公开
    着色优化

    公开(公告)号：WO2016184664A1

    公开(公告)日：2016-11-24

    申请号：PCT/EP2016/059655

    申请日：2016-04-29

    Applicant: ASML NETHERLANDS B.V.

    Inventor： ZOU, Yi ,  SU, Jing ,  SOCHA, Robert ,  SPENCE, Christopher, Alan ,  HSU, Duan-Fu, Stephen

    IPC: G03F7/20 ,  G06F17/50

    CPC classification number: G03F7/70433 ,  G03F7/70466

    Abstract: Disclosed herein is a computer-implemented method comprising: obtaining a sub-layout comprising an area that is a performance limiting spot; adjusting colors of patterns in the area; determining whether the area is still performance limiting spot. Another method comprises: decomposing patterns in a design layout into multiple sub-layouts; determining for at least one area in one of the sub- layouts, the likelihood of that a figure of merit is beyond its allowed range; if the likelihood is above a threshold, that one sub-layout has a performance limiting spot. Yet another method disclosed comprises: obtaining a design layout comprising a first group of patterns and a second group of patterns, wherein colors of the first group of patterns are not allowed to change and colors of the second group of patterns are allowed to change; co-optimizing at least the first group of patterns, the second group of patterns and a source of a lithographic apparatus.

    Abstract translation: 本文公开了一种计算机实现的方法，包括：获得包括作为性能限制点的区域的子布局; 调整该区域的图案颜色; 确定该区域是否仍然是性能限制点。 另一种方法包括：将设计布局中的图案分解为多个子布局; 确定一个子布局中的至少一个区域，品质因数超出其允许范围的可能性; 如果可能性高于阈值，则该子布局具有性能限制点。 公开的另一种方法包括：获得包括第一组图案和第二组图案的设计布局，其中第一组图案的颜色不允许改变，并且允许第二组图案的颜色改变; 共同优化至少第一组图案，第二组图案和光刻设备的来源。

    METHODS FOR GENERATING CHARACTERISTIC PATTERN AND TRAINING MACHINE LEARNING MODEL
    6.
    发明申请
    METHODS FOR GENERATING CHARACTERISTIC PATTERN AND TRAINING MACHINE LEARNING MODEL 审中-公开

    公开(公告)号：WO2021052712A1

    公开(公告)日：2021-03-25

    申请号：PCT/EP2020/073449

    申请日：2020-08-21

    Applicant: ASML NETHERLANDS B.V.

    Inventor： CAO, Yu ,  SCRANTON, Greggory ,  SU, Jing ,  ZOU, Yi

    IPC: G03F1/36 ,  G03F1/70 ,  G03F7/20 ,  G06N20/00

    Abstract: Described herein are methods of generating a characteristic pattern for a patterning process and training a machine learning model. A method of training a machine learning model configured to generate a characteristic pattern for a mask pattern includes obtaining (i) a reference characteristic pattern (EFMs) that meets a satisfactory threshold related to manufacturing of the mask pattern, and (ii) a continuous transmission mask (CTM) for use in generating the mask pattern; and training, based on the reference characteristic pattern and the CTM, the machine learning model such that a first metric between the characteristic pattern (EFM1) and the CTM, and a second metric between the characteristic pattern (EFM1) and the reference characteristic pattern (EFMs) is reduced.

    DETERMINING PATTERN RANKING BASED ON MEASUREMENT FEEDBACK FROM PRINTED SUBSTRATE
    7.
    发明申请
    DETERMINING PATTERN RANKING BASED ON MEASUREMENT FEEDBACK FROM PRINTED SUBSTRATE 审中-公开

    公开(公告)号：WO2020135988A1

    公开(公告)日：2020-07-02

    申请号：PCT/EP2019/083585

    申请日：2019-12-04

    Applicant: ASML NETHERLANDS B.V.

    Inventor： ZHANG, Youping ,  GENIN, Maxime, Philippe, Frederic ,  WU, Cong ,  SU, Jing ,  HU, Weixuan ,  ZOU, Yi

    IPC: G03F7/20

    Abstract: Described herein are methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method of training a machine learning model of a patterning process involves obtaining a training data set comprising: (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; training the machine learning model 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 is further used for determining ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

    IDENTIFICATION OF HOT SPOTS OR DEFECTS BY MACHINE LEARNING
    8.
    发明申请
    IDENTIFICATION OF HOT SPOTS OR DEFECTS BY MACHINE LEARNING 审中-公开
    机器学习识别热点或缺陷

    公开(公告)号：WO2017194281A1

    公开(公告)日：2017-11-16

    申请号：PCT/EP2017/059328

    申请日：2017-04-20

    Applicant: ASML NETHERLANDS B.V.

    Inventor： SU, Jing ,  ZOU, Yi ,  LIN, Chenxi ,  HUNSCHE, Stefan ,  JOCHEMSEN, Marinus ,  LU, Yen-Wen ,  CHEONG, Lin, Lee

    IPC: G03F7/20 ,  G06K9/00 ,  G06F17/50

    Abstract: Disclosed herein are various 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 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 characteristics under that process condition, whether that hot spot is defective; obtaining characteristics of each of the process conditions; obtaining characteristics of each of the hot spots; and training a machine learning model using a training set comprising the characteristics of one of the process conditions, the characteristics of one of the hot spots, and whether that hot spot is defective under that process condition.

    Abstract translation: 这里公开了使用机器学习模型从设计布局识别热点或预测设计布局中的模式是否有缺陷的各种方法。 本文公开的示例方法包括分别在设备制造过程中的多个过程条件下分别获得热点性能的特征组; 基于在该处理条件下的特性，针对每个处理条件确定每个热点的热点是否有缺陷; 获得每个工艺条件的特性; 获得每个热点的特征; 并且使用训练集训练机器学习模型，所述训练集包括过程条件之一的特征，其中一个热点的特征以及在该过程条件下该热点是否有缺陷。

Patent Agency Ranking