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1.发明公开公开(公告)号：US20230267711A1
公开(公告)日：2023-08-24
申请号：US18015313
申请日：2021-07-29
Applicant: ASML NETHERLANDS B.V.
Inventor： Scott Anderson MIDDLEBROOKS , Maxim PISARENCO , Markus Gerardus Martinus Maria VAN KRAAIJ , Coen Adrianus VERSCHUREN
IPC: G06V10/774 , G06N3/0464 , G06V10/50
CPC classification number: G06V10/774 , G06N3/0464 , G06V10/50
Abstract: 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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2.发明申请公开(公告)号：US20220375063A1
公开(公告)日：2022-11-24
申请号：US17761578
申请日：2020-09-14
Applicant: ASML Netherlands B.V.
Inventor： Maxim PISARENCO , Scott Anderson MIDDLEBROOKS , Mark John MASLOW , Marie-Claire VAN LARE , Chrysostomos BATISTAKIS
Abstract: A system and method for generating predictive images for wafer inspection using machine learning are provided. Some embodiments of the system and method include acquiring the wafer after a photoresist applied to the wafer has been developed; imaging a portion of a segment of the developed wafer; acquiring the wafer after the wafer has been etched; imaging the segment of the etched wafer; training a machine learning model using the imaged portion of the developed wafer and the imaged segment of the etched wafer; and applying the trained machine learning model using the imaged segment of the etched wafer to generate predictive images of a developed wafer. Some embodiments include imaging a segment of the developed wafer; imaging a portion of the segment of the etched wafer; training a machine learning model; and applying the trained machine learning model to generate predictive after-etch images of the developed wafer.
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公开(公告)号：US20240233305A1
公开(公告)日：2024-07-11
申请号：US18415596
申请日：2024-01-17
Applicant: ASML Netherlands B.V.
Inventor： Maxim PISARENCO , Scott Anderson MIDDLEBROOKS , Markus Gerardus Martinus Maria VAN KRAAIJ , Coen Adrianus VERSCHUREN
Abstract: Disclosed herein is a non-transitory computer readable medium that has stored therein a computer program, wherein the computer program comprises code that, when executed by a computer system, instructs the computer system to perform a method for generating synthetic distorted images, the method comprising: obtaining an input set that comprises a plurality of distorted images; determining, using a model, distortion modes of the distorted images in the input set; generating a plurality of different combinations of the distortion modes; generating, for each one of the plurality of combinations of the distortion modes, a synthetic distorted image in dependence on the combination; and including each of the synthetic distorted images in an output set.
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4.发明公开公开(公告)号：US20240054669A1
公开(公告)日：2024-02-15
申请号：US18266792
申请日：2021-11-24
Applicant: ASML NETHERLANDS B.V.
Inventor： Tim HOUBEN , Thomas Jarik HUISMAN , Maxim PISARENCO , Scott Anderson MIDDLEBROOKS , Chrysostomos BATISTAKIS , Yu CAO
CPC classification number: G06T7/593 , G06T5/50 , G06T7/13 , G06T2207/10061 , G06T2207/20084 , G06T2207/10012 , G06T2207/20212 , G06T2207/20081 , G06T2207/30148
Abstract: A system, method, and apparatus for determining three-dimensional (3D) information of a structure of a patterned substrate. The 3D information can be determined using one or more models configured to generate 3D information (e.g., depth information) using only a single image of a patterned substrate. In a method, the model is trained by obtaining a pair of stereo images of a structure of a patterned substrate. The model generates, using a first image of the pair of stereo images as input, disparity data between the first image and a second image, the disparity data being indicative of depth information associated with the first image. The disparity data is combined with the second image to generate a reconstructed image corresponding to the first image. Further, one or more model parameters are adjusted based on the disparity data, the reconstructed image, and the first image.
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公开(公告)号：US20220350254A1
公开(公告)日：2022-11-03
申请号：US17621494
申请日：2020-06-04
Applicant: ASML NETHERLANDS B.V.
Inventor： Maxim PISARENCO , Maurits VAN DER SCHAAR , Huaichen ZHANG , Marie-Claire VAN LARE
IPC: G03F7/20
Abstract: A method for applying a deposition model in a semiconductor manufacturing process. The method includes predicting a deposition profile of a substrate using the deposition model; and using the predicted deposition profile to enhance a metrology target design. The deposition model can be calibrated using experimental cross-section profile information from a layer of a physical substrate. In some embodiments, the deposition model is a machine-learning model, and calibrating the deposition model includes training the machine-learning model. The metrology target design may include an alignment metrology target design or an overlay metrology target design, for example.
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Patent Agency Ranking
公开(公告)号：US20220187713A1
公开(公告)日：2022-06-16
申请号：US17441729
申请日：2020-03-26
Applicant: ASML NETHERLANDS B.V.
Inventor： Scott Anderson MIDDLEBROOKS , Adrianus Cornelis Matheus KOOPMAN , Markus Gerardus Martinus Maria VAN KRAAIJ , Maxim PISARENCO , Stefan HUNSCHE
Abstract: A method for training a machine learning model configured to predict a substrate image corresponding to a printed pattern of a substrate as measured via a metrology tool. The method involves obtaining a training data set including (i) metrology data of the metrology tool used to measure the printed pattern of the substrate, and (ii) a representation of a mask pattern employed for imaging the printed pattern on the substrate; and training, based on the training data set, a machine learning model to predict the substrate image of the substrate as measured by the metrology tool such that a cost function is improved, wherein the cost function includes a relationship between the predicted substrate image and the metrology data.
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公开(公告)号：US20210055215A1
公开(公告)日：2021-02-25
申请号：US17092397
申请日：2020-11-09
Applicant: ASML Netherlands B.V.
Inventor： Maxim PISARENCO , Nitesh PANDEY , Alessandro POLO
Abstract: An acoustic scatterometer has an acoustic source operable to project acoustic radiation onto a periodic structure and formed on a substrate. An acoustic detector is operable to detect the −1st acoustic diffraction order diffracted by the periodic structure and while discriminating from specular reflection (0th order). Another acoustic detector is operable to detect the +1st acoustic diffraction order diffracted by the periodic structure, again while discriminating from the specular reflection (0th order). The acoustic source and acoustic detector may be piezo transducers. The angle of incidence of the projected acoustic radiation and location of the detectors and are arranged with respect to the periodic structure and such that the detection of the −1st and +1st acoustic diffraction orders and discriminates from the 0th order specular reflection.
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公开(公告)号：US20190271919A1
公开(公告)日：2019-09-05
申请号：US16461044
申请日：2018-05-23
Applicant: ASML NETHERLANDS B.V.
Inventor： Davit HARUTYUNYAN , Fei JIA , Frank STAALS , Fuming WANG , Hugo Thomas LOOIJESTIJN , Cornelis Johannes RIJNIERSE , Maxim PISARENCO , Roy WERKMAN , Thomas THEEUWES , Tom VAN HEMERT , Vahid BASTANI , Jochem Sebastian WILDENBERG , Everhardus Cornelis MOS , Erik Johannes Maria WALLERBOS
IPC: G03F7/20
Abstract: A method, system and program for determining a fingerprint of a parameter. The method includes determining a contribution from a device out of a plurality of devices to a fingerprint of a parameter. The method including: obtaining parameter data and usage data, wherein the parameter data is based on measurements for multiple substrates having been processed by the plurality of devices, and the usage data indicates which of the devices out of the plurality of the devices were used in the processing of each substrate; and determining the contribution using the usage data and parameter data.
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公开(公告)号：US20240369944A1
公开(公告)日：2024-11-07
申请号：US18287166
申请日：2022-04-12
Applicant: ASML NETHERLANDS B.V.
Inventor： Chrysostomos BATISTAKIS , Maxim PISARENCO , Markus Gerardus Martinus Maria VAN KRAAIJ , Vito Daniele RUTIGLIANI , Scott Anderson MIDDLEBROOKS , Coen Adrianus VERSCHUREN , Niels GEYPEN
Abstract: A method of determining a stochastic metric, the method including: obtaining a trained model having been trained to correlate training optical metrology data to training stochastic metric data, wherein the training optical metrology data includes a plurality of measurement signals relating to distributions of an intensity related parameter across a zero or higher order of diffraction of radiation scattered from a plurality of training structures, and the training stochastic metric data includes stochastic metric values relating to the plurality of training structures, wherein the plurality of training structures have been formed with a variation in one or more dimensions on which the stochastic metric is dependent; obtaining optical metrology data including a distribution of the intensity related parameter across a zero or higher order of diffraction of radiation scattered from a structure; and using the trained model to infer a value of the stochastic metric from the optical metrology data.
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公开(公告)号：US20240020961A1
公开(公告)日：2024-01-18
申请号：US18039483
申请日：2021-12-08
Applicant: ASML NETHERLANDS B.V.
CPC classification number: G06V10/82 , G06V10/993
Abstract: A method for training a machine learning model includes obtaining a set of unpaired after-development (AD) images 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.
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