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    METHOD FOR GENERATING ASSIST FEATURES USING MACHINE LEARNING MODEL
    1.
    发明公开
    METHOD FOR GENERATING ASSIST FEATURES USING MACHINE LEARNING MODEL 审中-公开

    公开(公告)号：US20240256976A1

    公开(公告)日：2024-08-01

    申请号：US18565759

    申请日：2022-06-10

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Jun TAO ,  Yu CAO ,  Christopher Alan SPENCE

    IPC: G06N20/00 ,  G03F1/36

    CPC classification number: G06N20/00 ,  G03F1/36

    Abstract: Described herein is a method of determining assist features for a mask pattern. The method includes obtaining (i) a target pattern comprising a plurality of target features, wherein each of the plurality of target features comprises a plurality of target edges, and (ii) a trained sequence-to-sequence machine leaning (ML) model (e.g., long short term memory, Gated Recurrent Units, etc.) configured to determine sub-resolution assist features (SRAFs) for the target pattern. For a target edge of the plurality of target edges, geometric information (e.g., length, width, distances between features, etc.) of a subset of target features surrounding the target edge is determined. Using the geometric information as input, the ML model generates SRAFs to be placed around the target edge.

    METHODS OF DETERMINING SCATTERING OF RADIATION BY STRUCTURES OF FINITE THICKNESSES ON A PATTERNING DEVICE
    2.
    发明申请
    METHODS OF DETERMINING SCATTERING OF RADIATION BY STRUCTURES OF FINITE THICKNESSES ON A PATTERNING DEVICE 审中-公开

    公开(公告)号：US20200073260A1

    公开(公告)日：2020-03-05

    申请号：US16467124

    申请日：2017-12-06

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Yu CAO ,  Yen-Wen LU ,  Peng LIU ,  Rafael C. HOWELL

    IPC: G03F7/20

    Abstract: A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function represents a continuous transmission mask and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.

    MACHINE LEARNING BASED INVERSE OPTICAL PROXIMITY CORRECTION AND PROCESS MODEL CALIBRATION
    4.
    发明申请
    MACHINE LEARNING BASED INVERSE OPTICAL PROXIMITY CORRECTION AND PROCESS MODEL CALIBRATION 有权

    公开(公告)号：US20210216697A1

    公开(公告)日：2021-07-15

    申请号：US15734141

    申请日：2019-05-23

    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.

    COMPUTATIONAL PROCESS CONTROL
    5.
    发明申请
    COMPUTATIONAL PROCESS CONTROL 有权
    计算过程控制

    公开(公告)号：US20150025668A1

    公开(公告)日：2015-01-22

    申请号：US14507553

    申请日：2014-10-06

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Jun YE ,  Yu CAO ,  James Patrick KOONMEN

    IPC: G03F9/00 ,  B29C67/00

    CPC classification number: G03F7/70525 ,  B29C64/386 ,  G03F9/7096 ,  G05B13/04

    Abstract: The present invention provides a number of innovations in the area of computational process control (CPC). CPC offers unique diagnostic capability during chip manufacturing cycle by analyzing temporal drift of a lithography apparatus/ process, and provides a solution towards achieving performance stability of the lithography apparatus/process. Embodiments of the present invention enable optimized process windows and higher yields by keeping performance of a lithography apparatus and/or parameters of a lithography process substantially close to a pre-defined baseline condition. This is done by comparing the measured temporal drift to a baseline performance using a lithography process simulation model. Once in manufacturing, CPC optimizes a scanner for specific patterns or reticles by leveraging wafer metrology techniques and feedback loop, and monitors and controls, among other things, overlay and/or CD uniformity (CDU) performance over time to continuously maintain the system close to the baseline condition.

    Abstract translation: 本发明提供了计算过程控制（CPC）领域的许多创新。 CPC通过分析光刻设备/工艺的时间漂移​​，在芯片制造周期中提供独特的诊断功能，并为实现光刻设备/工艺的性能稳定性提供了解决方案。 本发明的实施例通过保持光刻设备的性能和/或基本上接近预定义基线条件的光刻工艺的参数来实现优化的工艺窗口和更高的产量。 这通过使用光刻过程模拟模型将测量的时间漂移​​与基线性能进行比较来完成。 一旦制造，CPC通过利用晶片计量技术和反馈回路来优化扫描仪的特定图案或掩模版，并监控和控制其他方面的重叠和/或CD均匀性（CDU）性能，以持续保持系统接近 基线条件。

    MODEL-BASED PROCESS SIMULATION SYSTEMS AND METHODS
    6.
    发明申请
    MODEL-BASED PROCESS SIMULATION SYSTEMS AND METHODS 审中-公开
    基于模型的过程模拟系统和方法

    公开(公告)号：US20140351773A1

    公开(公告)日：2014-11-27

    申请号：US14456586

    申请日：2014-08-11

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Yu CAO ,  Wenjin SHAO ,  Ronaldus Johannes Gijsbertus GOOSSENS ,  Jun YE ,  James Patrick KOONMEN

    IPC: G06F17/50

    CPC classification number: B29C67/0088 ,  B29C64/386 ,  G03F7/70091 ,  G03F7/70458 ,  G03F7/705 ,  G03F7/70516 ,  G03F7/70525 ,  G06F17/50 ,  G06F17/5009 ,  G06F17/5081

    Abstract: Systems and methods for process simulation are described. The methods may use a reference model identifying sensitivity of a reference scanner to a set of tunable parameters. Chip fabrication from a chip design may be simulated using the reference model, wherein the chip design is expressed as one or more masks. An iterative retuning and simulation process may be used to optimize critical dimension in the simulated chip and to obtain convergence of the simulated chip with an expected chip. Additionally, a designer may be provided with a set of results from which an updated chip design is created.

    Abstract translation: 描述了过程仿真的系统和方法。 这些方法可以使用标识参考扫描仪对一组可调谐参数的灵敏度的参考模型。 可以使用参考模型来模拟来自芯片设计的芯片制造，其中芯片设计被表示为一个或多个掩模。 可以使用迭代重调和仿真过程来优化模拟芯片中的关键尺寸，并获得模拟芯片与预期芯片的收敛。 此外，可以向设计者提供一组结果，从中创建更新的芯片设计。

    PATTERN SELECTION FOR LITHOGRAPHIC MODEL CALIBRATION
    7.
    发明申请
    PATTERN SELECTION FOR LITHOGRAPHIC MODEL CALIBRATION 有权
    图形模型校准的图案选择

    公开(公告)号：US20140208278A1

    公开(公告)日：2014-07-24

    申请号：US14246961

    申请日：2014-04-07

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Yu CAO ,  Wenjin SHAO ,  Jun YE ,  Ronaldus Johannes Gljsbertus GOOSSENS

    IPC: G06F17/50 ,  G03F7/20

    CPC classification number: G06F17/50 ,  G03F1/14 ,  G03F1/44 ,  G03F1/68 ,  G03F7/70433 ,  G03F7/705 ,  G06F17/10 ,  G06F17/5009

    Abstract: The present invention relates generally to methods and apparatuses for test pattern selection for computational lithography model calibration. According to some aspects, the pattern selection algorithms of the present invention can be applied to any existing pool of candidate test patterns. According to some aspects, the present invention automatically selects those test patterns that are most effective in determining the optimal model parameter values from an existing pool of candidate test patterns, as opposed to designing optimal patterns. According to additional aspects, the selected set of test patterns according to the invention is able to excite all the known physics and chemistry in the model formulation, making sure that the wafer data for the test patterns can drive the model calibration to the optimal parameter values that realize the upper bound of prediction accuracy imposed by the model formulation.

    Abstract translation: 本发明一般涉及用于计算光刻模型校准的测试图案选择的方法和装置。 根据一些方面，本发明的模式选择算法可以应用于任何现有的候选测试模式池。 根据一些方面，与设计最佳图案相反，本发明自动选择从现有的候选测试图案池中确定最佳模型参数值最有效的测试图案。 根据另外的方面，根据本发明的所选择的一组测试图案能够激发模型配方中的所有已知物理和化学，确保用于测试图案的晶片数据可以将模型校准驱动到最佳参数值 实现了模型公式对预测精度的上限。

    METHODS FOR GENERATING CHARACTERISTIC PATTERN AND TRAINING MACHINE LEARNING MODEL
    9.
    发明申请
    METHODS FOR GENERATING CHARACTERISTIC PATTERN AND TRAINING MACHINE LEARNING MODEL 有权

    公开(公告)号：US20220335333A1

    公开(公告)日：2022-10-20

    申请号：US17641159

    申请日：2020-08-21

    Applicant: ASML NETHERLANDS B.V.

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

    IPC: G06N20/00 ,  G03F1/36 ,  G03F7/20 ,  G06N3/04

    Abstract: 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 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 and the CTM, and a second metric between the characteristic pattern and the reference characteristic pattern is reduced.

    FAST FREEFORM SOURCE AND MASK CO-OPTIMIZATION METHOD
    10.
    发明申请
    FAST FREEFORM SOURCE AND MASK CO-OPTIMIZATION METHOD 审中-公开

    公开(公告)号：US20200218850A1

    公开(公告)日：2020-07-09

    申请号：US16821048

    申请日：2020-03-17

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Luoqi CHEN ,  Jun YE ,  Yu CAO

    IPC: G06F30/398 ,  G03F7/20 ,  G03F1/36 ,  G06F30/39 ,  G03F1/00

    Abstract: The present disclosure relates to lithographic apparatuses and processes, and more particularly to tools for optimizing illumination sources and masks for use in lithographic apparatuses and processes. According to certain aspects, the present disclosure significantly speeds up the convergence of the optimization by allowing direct computation of gradient of the cost function. According to other aspects, the present disclosure allows for simultaneous optimization of both source and mask, thereby significantly speeding the overall convergence. According to still further aspects, the present disclosure allows for free-form optimization, without the constraints required by conventional optimization techniques.

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