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公开(公告)号：US20200257208A1
公开(公告)日：2020-08-13
申请号：US16851477
申请日：2020-04-17
Applicant: ASML Netherlands B.V.
Inventor： Scott Anderson MIDDLEBROOKS , Willem Maria Julia Marcel COENE , Frank Arnoldus Johannes Maria DRIESSEN , Adrianus Cornelis Matheus KOOPMAN , Markus Gerardus Martinus Maria VAN KRAAIJ
IPC: G03F7/20 , G06N7/00 , G06N20/00 , G06F30/367
Abstract: A defect prediction method for a device manufacturing process involving production substrates processed by a lithographic apparatus, the method including training a classification model using a training set including measured or determined values of a process parameter associated with the production substrates processed by the device manufacturing process and an indication regarding existence of defects associated with the production substrates processed in the device manufacturing process under the values of the process parameter, and producing an output from the classification model that indicates a prediction of a defect for a substrate.
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公开(公告)号：US20200072761A1
公开(公告)日：2020-03-05
申请号：US16490091
申请日：2018-03-02
Applicant: ASML NETHERLANDS B.V.
Inventor： Adrianus Cornelis Matheus KOOPMAN , Scott Anderson MIDDLEBROOKS , Willem Marie Julia Marcel COENE
IPC: G01N21/956 , G03F7/20 , G06F17/50
Abstract: A method including selecting a shaped feature from a set of shaped features, each shaped feature of the set of shaped features having a set of points on a perimeter of the shape of the shaped feature, creating a plurality of shape context descriptors for the selected shaped feature, wherein each shape context descriptor provides an indication of a location in a shape context descriptor framework of a first focus point of the set of points in relation to a second point of the set of points, and identifying a shaped feature from the set of shaped features having a same or similar shape as the selected shaped feature based on data from the plurality of shape context descriptors.
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公开(公告)号：US20160313651A1
公开(公告)日：2016-10-27
申请号：US15104517
申请日：2014-11-14
Applicant: ASML NETHERLANDS B.V.
Inventor： Scott Anderson MIDDLEBROOKS , Willem Maria Julia Marcel COENE , Frank Arnoldus Johannes Maria DRIESSEN , Adrianus Cornelis Matheus KOOPMAN , Markus Gerardus Martinus Maria VAN KRAAIJ
Abstract: A defect prediction method for a device manufacturing process involving production substrates processed by a lithographic apparatus, the method including training a classification model using a training set including measured or determined values of a process parameter associated with the production substrates processed by the device manufacturing process and an indication regarding existence of defects associated with the production substrates processed in the device manufacturing process under the values of the process parameter, and producing an output from the classification model that indicates a prediction of a defect for a substrate.
Abstract translation: 一种涉及由光刻设备处理的生产基板的装置制造过程的缺陷预测方法，该方法包括使用包括与通过装置制造过程处理的生产基板相关联的处理参数的测量值或确定值的训练集来训练分类模型;以及 关于在处理参数的值下在器件制造过程中处理的生产基板的相关缺陷的存在的指示，以及产生指示预测基板的缺陷的分类模型的输出。
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14.发明申请Metrology Method and Apparatus, Lithographic System and Device Manufacturing Method 有权计量方法与仪器，平版印刷系统和器件制造方法公开(公告)号：US20160161864A1
公开(公告)日：2016-06-09
申请号：US14906896
申请日：2014-07-18
Applicant: ASML NETHERLANDS B.V.
Inventor： Scott Anderson MIDDLEBROOKS , Niels GEYPEN , Hendrik Jan Hidde SMILDE , Alexander STRAAIJER , Maurits VAN DER SCHAAR , Markus Gerardus Martinus Maria VAN KRAAIJ
IPC: G03F7/20
CPC classification number: G03F7/70633
Abstract: Disclosed is a method of measuring a parameter of a litho-graphic process, and associated inspection apparatus. The method comprises measuring at least two target structures on a substrate using a plurality of different illumination conditions, the target structures having deliberate overlay biases; to obtain for each target structure an asymmetry measurement representing an overall asymmetry that includes contributions due to (i) the deliberate overlay biases, (ii) an overlay error during forming of the target structure and (iii) any feature asymmetry. A regression analysis is performed on the asymmetry measurement data by fitting a linear regression model to a planar representation of asymmetry measurements for one target structure against asymmetry measurements for another target structure, the linear regression model not necessarily being fitted through an origin of the planar representation. The overlay error can then be determined from a gradient described by the linear regression model.
Abstract translation: 公开了一种测量光刻图形处理参数的方法和相关联的检查装置。 该方法包括使用多个不同的照明条件测量衬底上的至少两个目标结构，所述目标结构具有有意的覆盖偏移; 为每个目标结构获得表示总体不对称性的不对称测量，其包括由于（i）故意重叠偏差引起的贡献，（ii）在形成目标结构期间的覆盖误差，以及（iii）任何特征不对称性。 对不对称测量数据进行回归分析，通过将线性回归模型拟合到针对另一目标结构的不对称测量的一个目标结构的不对称测量的平面表示，线性回归模型不一定通过平面表示的原点拟合 。 然后可以从线性回归模型描述的梯度确定覆盖误差。
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公开(公告)号：US20240320528A1
公开(公告)日：2024-09-26
申请号：US18579560
申请日：2022-08-17
Applicant: ASML NETHERLANDS B.V.
Inventor： Patrick Philipp HELFENSTEIN , Scott Anderson MIDDLEBROOKS , Markus Gerardus Martinus Maria VAN KRAAIJ , Maxim PISARENCO
IPC: G06N7/01
CPC classification number: G06N7/01
Abstract: A method of designing a target includes obtaining a model of an initial dataset, performing a Bayesian optimization using the model which provides an improved model, and performing an optimization of the target design using the improved model.
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Patent Agency Ranking
公开(公告)号：US20240152060A1
公开(公告)日：2024-05-09
申请号：US18282305
申请日：2022-02-17
Applicant: ASML Netherlands B.V.
Inventor： Patrick Philipp HELFENSTEIN , Scott Anderson MIDDLEBROOKS , Maxim PISARENCO , Markus Gerardus Martinus Maria VAN KRAAIJ , Alexander Prasetya KONIJNENBERG
IPC: G03F7/00
CPC classification number: G03F7/705 , G03F7/70575 , G03F7/70675
Abstract: A method and system for predicting process information (e.g., phase data) using a given input (e.g., intensity) to a parameterized model are described. A latent space of a given input is determined based on dimensional data in a latent space of the parameterized model for a given input to the parameterized model. Further, an optimum latent space is determined by constraining the latent space with prior information (e.g., wavelength) that enables converging to a solution that causes more accurate predictions of the process information. The optimum latent space is used to predict the process information. The given input may be a measured amplitude (e.g., intensity) associated with the complex electric field image. The predicted process information can be complex electric field image having amplitude data and phase data. The parameterized model comprises variational encoder-decoder architecture.
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公开(公告)号：US20210286270A1
公开(公告)日：2021-09-16
申请号：US17334574
申请日：2021-05-28
Applicant: ASML Netherlands B.V.
Abstract: Described herein is a method for quantifying uncertainty in parameterized (e.g., machine learning) model predictions. The method comprises causing a parameterized model to predict multiple posterior distributions from the parameterized model for a given input. The multiple posterior distributions comprise a distribution of distributions. The method comprises determining a variability of the predicted multiple posterior distributions for the given input by sampling from the distribution of distributions; and using the determined variability in the predicted multiple posterior distributions to quantify uncertainty in the parameterized model predictions. The parameterized model comprises encoder-decoder architecture. The method comprises using the determined variability in the predicted multiple posterior distributions to adjust the parameterized model to decrease the uncertainty of the parameterized model for predicting wafer geometry, overlay, and/or other information as part of a semiconductor manufacturing process.
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公开(公告)号：US20200320238A1
公开(公告)日：2020-10-08
申请号：US16763376
申请日：2018-11-29
Applicant: ASML NETHERLANDS B.V.
Abstract: A method involving obtaining a resist deformation model for simulating a deformation process of a pattern in resist, the resist deformation model being a fluid dynamics model configured to simulate an intrafluid force acting on the resist, performing, using the resist deformation model, a computer simulation of the deformation process to obtain a deformation of the developed resist pattern for an input pattern to the resist deformation model, and producing electronic data representing the deformation of the developed resist pattern for the input pattern.
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19.发明申请公开(公告)号：US20190278188A1
公开(公告)日：2019-09-12
申请号：US16351873
申请日：2019-03-13
Applicant: ASML Netherlands B.V.
Inventor： Alexander YPMA , Jasper MENGER , David DECKERS , David HAN , Adrianus Cornelis Matheus KOOPMAN , Irina LYULINA , Scott Anderson MIDDLEBROOKS , Richard Johannes Franciscus VAN HAREN , Jochem Sebastiaan WILDENBERG
Abstract: In a lithographic process, product units such as semiconductor wafers are subjected to lithographic patterning operations and chemical and physical processing operations. Alignment data or other measurements are made at stages during the performance of the process to obtain object data representing positional deviation or other parameters measured at points spatially distributed across each unit. This object data is used to obtain diagnostic information by performing a multivariate analysis to decompose a set of vectors representing the units in the multidimensional space into one or more component vectors. Diagnostic information about the industrial process is extracted using the component vectors. The performance of the industrial process for subsequent product units can be controlled based on the extracted diagnostic information.
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20.发明申请公开(公告)号：US20190187569A1
公开(公告)日：2019-06-20
申请号：US16322148
申请日：2017-07-14
Applicant: ASML NETHERLANDS B.V.
CPC classification number: G03F7/70525 , G03F7/70508 , G03F7/70616 , G03F7/70625 , G03F7/70633 , G03F7/70641 , G05B19/41875 , G06K9/6296 , G06T7/001 , G06T2207/30148 , H01L22/20 , Y02P90/86
Abstract: A diagnostic system implements a network including two or more sub-domains. Each sub-domain has diagnostic information extracted by analysis of object data, the object data representing one or more parameters measured in relation to a set of product units that have been subjected nominally to the same industrial process as one another. The network further has at least one probabilistic connection from a first variable in a first diagnostic sub-domain to a second variable in a second diagnostic sub-domain. Part of the second diagnostic information is thereby influenced probabilistically by knowledge within the first diagnostic information. Diagnostic information may include, for example, a spatial fingerprint observed in the object data, or inferred. The network may include connections within sub-domains. The network may form a directed acyclic graph, and used for Bayesian inference operations.
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