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31.发明申请
公开(公告)号：WO2015078670A1
公开(公告)日：2015-06-04
申请号：PCT/EP2014/073702
申请日：2014-11-04
Applicant: ASML NETHERLANDS B.V.
Inventor： PISARENCO, Maxim , SETIJA, Irwan, Dani
IPC: G03F7/20
CPC classification number: G01B11/02 , G03F7/705 , G03F7/70625 , G03F7/70633
Abstract: In scatterometry, a merit function including a regularization parameter is used in an iterative process to find values for the scattering properties of the measured target. An optimal value for the regularization parameter is obtained for each measurement target and in each iteration of the iterative process. Various methods can be used to find the value for the regularization parameter, including the Discrepancy Principle, the chi-squared method and novel modifications of the Discrepancy Principle and the chi-squared method including a merit function.
Abstract translation: 在散射法中，在迭代过程中使用包括正则化参数的优值函数来查找测量目标的散射特性的值。 为每个测量目标和迭代过程的每次迭代获得正则化参数的最优值。 可以使用各种方法来找出正则化参数的值，包括差异原理，卡方法和差异原理的新颖修改以及包括优值函数的卡方法。
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公开(公告)号：EP3660744A1
公开(公告)日：2020-06-03
申请号：EP18209496.1
申请日：2018-11-30
Applicant: ASML Netherlands B.V.
Inventor： MIDDLEBROOKS, Scott, Anderson , VAN KRAAIJ, Markus, Gerardus, Martinus, Maria , PISARENCO, Maxim
Abstract: Described herein is a method for quantifying uncertainty in parameterized (e.g., machine learning) model predictions. The method comprising causing a machine learning model to predict multiple output realizations from the machine learning model for a given input; determining a variability of the predicted multiple output realizations for the given input, and using the determined variability in the predicted multiple output realizations to adjust the machine learning model to decrease an uncertainty of the machine learning model. The machine learning model comprises encoder-decoder architecture. The method comprises using the determined variability in the predicted multiple output realizations to adjust the machine learning model to decrease the uncertainty of the machine learning 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：EP3474074A1
公开(公告)日：2019-04-24
申请号：EP17196893.6
申请日：2017-10-17
Applicant: ASML Netherlands B.V.
Inventor： PISARENCO, Maxim , PANDEY, Nitesh , POLO, Alessandro
Abstract: An acoustic scatterometer 502 has an acoustic source 520 operable to project acoustic radiation 526 onto a periodic structure 538 and 540 formed on a substrate 536. An acoustic detector 518 is operable to detect the -1st acoustic diffraction order 528 diffracted by the periodic structure 538 and 540 while discriminating from specular reflection (0th order 532). Another acoustic detector 522 is operable to detect the +1st acoustic diffraction order 530 diffracted by the periodic structure, again while discriminating from the specular reflection (0th order 532). The acoustic source and acoustic detector may be piezo transducers. The angle of incidence of the projected acoustic radiation 526 and location of the detectors 518 and 522 are arranged with respect to the periodic structure 538 and 540 such that the detection of the -1st and +1st acoustic diffraction orders 528 and 530 discriminates from the 0th order specular reflection 532.
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公开(公告)号：EP3336608A1
公开(公告)日：2018-06-20
申请号：EP16204832.6
申请日：2016-12-16
Applicant: ASML Netherlands B.V.
Inventor： MIDDLEBROOKS, Scott, Anderson , KOOPMAN, Adrianus, Cornelis, Matheus , VAN KRAAIJ, Markus, Gerardus, Martinus, Maria , PISARENCO, Maxim
IPC: G03F7/20
CPC classification number: G03F7/705 , G03F7/70525
Abstract: A method including: obtaining a logistic mathematical model predicting the formation of a physical structure created using a patterning process; evaluating the logistic mathematical model to predict formation of a part of the physical structure and generate an output; and adapting, based on the output, an aspect of the patterning process.
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公开(公告)号：EP3910418A1
公开(公告)日：2021-11-17
申请号：EP20174556.9
申请日：2020-05-14
Applicant: ASML Netherlands B.V.
Inventor： BATISTAKIS, Chrysostomos , PISARENCO, Maxim , OYARZUN RIVERA, Bernardo Andres , SLACHTER, Abraham
Abstract: Described herein is a method for decomposing error contributions from multiple sources to multiple features of a pattern printed on a substrate. The method includes obtaining an image of the pattern on the substrate and obtaining, using the image, a plurality of measurement values (615;620;625) of a feature of the pattern. The measurement values are obtained for different sensor values. Further, the method includes correlating, using a decomposition algorithm (320), each measurement value of the plurality of measurement values to a linear mixture of the error contributions to generate a plurality of linear mixtures of the error contributions, and deriving, from the linear mixtures and using the decomposition algorithm, each of the error contributions (601;602;603).
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Patent Agency Ranking
公开(公告)号：EP3889684A1
公开(公告)日：2021-10-06
申请号：EP20167449.6
申请日：2020-04-01
Applicant: ASML Netherlands B.V.
IPC: G03F7/20 , G01N21/956 , G06T7/00
Abstract: An inspection tool comprising:
an imaging system configured to image a portion of a semiconductor substrate; and
an image analysis system configured to:
obtain an image of a structure on the semiconductor substrate from the imaging system;
encode the image of the structure into a latent space thereby forming a first encoding;
subtract an artefact vector, representative of an artefact in the image, from the encoding thereby forming a second encoding; and
decode the second encoding to obtain a decoded image.-
公开(公告)号：EP3370114A1
公开(公告)日：2018-09-05
申请号：EP17158923.7
申请日：2017-03-02
Applicant: ASML Netherlands B.V.
Inventor： PISARENCO, Maxim , SCHNEIDER, Frank Stefan , VAN KRAAIJ, Markus Gerardus Martinus Maria , VAN BEURDEN, Martijn Constant
IPC: G03F7/20
CPC classification number: G03F7/705 , G03F7/70625
Abstract: Disclosed is a method for reconstructing a parameter of a lithographic process. The method comprises the step of designing a preconditioner suitable for an input system comprising the difference of a first matrix and a second matrix, the first matrix being arranged to have a multi-level structure of at least three levels whereby at least two of said levels comprise a Toeplitz structure. One such preconditioner is a block-diagonal matrix comprising a BTTB structure generated from a matrix-valued inverse generating function. A second such preconditioner is determined from an approximate decomposition of said first matrix into one or more Kronecker products.
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公开(公告)号：EP4020085A1
公开(公告)日：2022-06-29
申请号：EP20216767.2
申请日：2020-12-22
Applicant: ASML Netherlands B.V.
Abstract: A method for training a machine learning model includes obtaining a set of unpaired after-development images (AD) and after-etch (AE) images associated with a substrate. Each AD image in the set is obtained at a location on the substrate that is different from the location at which any of the AE images is obtained. The method further includes training the machine learning model to generate a predicted AE image based on the AD images and the AE images, wherein the predicted AE image corresponds to a location from which an input AD image of the AD images is obtained, and corresponding non-transitory computer-readable medium.
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公开(公告)号：EP3994526A1
公开(公告)日：2022-05-11
申请号：EP20731031.9
申请日：2020-06-04
Applicant: ASML Netherlands B.V.
Inventor： PISARENCO, Maxim , VAN DER SCHAAR, Maurits , ZHANG, Huaichen , VAN LARE, Marie-Claire
IPC: G03F7/20
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40.发明公开
公开(公告)号：EP3951496A1
公开(公告)日：2022-02-09
申请号：EP20189955.6
申请日：2020-08-07
Applicant: ASML Netherlands B.V.
Inventor： PISARENCO, Maxim , MIDDLEBROOKS, Scott, Anderson , VAN KRAAIJ, Markus, Gerardus, Martinus, Maria , VERSCHUREN, Coen, Adrianus
Abstract: Described herein is a method and apparatus for selecting patterns from an image such as a design layout. The method includes obtaining an image (e.g., of a target layout) having a plurality of patterns; determining, based on pixel intensities within the image, a metric (e.g., entropy) indicative of an amount of information contained in one or more portions of the image; and selecting, based on the metric, a sub-set of the plurality of patterns from the one or more portions of the image having values of the metric within a specified range. The sub-set of patterns can be provided as training data for training a model associated with a patterning process.
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