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.

Inventor： Scott Anderson MIDDLEBROOKS ,  Markus Gerardus Martinus Maria VAN KRAAIJ ,  Maxim PISARENCO

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

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.

公开(公告)号：US20170345138A1

公开(公告)日：2017-11-30

申请号：US15533614

申请日：2015-11-13

Applicant: ASML Netherlands B.V.

Inventor： Scott Anderson MIDDLEBROOKS ,  Markus Gerardus Martinus Maria VAN KRAAIJ ,  Maxim PISARENCO ,  Adrianus Cornelis Matheus KOOPMAN ,  Stefan HUNSCHE ,  Willem Marie Julia Marcel COENE

IPC: G06T7/00

CPC classification number: G06T7/001 ,  G03F7/705 ,  G03F7/70625 ,  G03F7/70633 ,  G06T2207/10061 ,  G06T2207/20068 ,  G06T2207/30148

Abstract: A method and apparatus of detection, registration and quantification of an image. The method may include obtaining an image of a lithographically created structure, and applying a level set method to an object, representing the structure, of the image to create a mathematical representation of the structure. The method may include obtaining a first dataset representative of a reference image object of a structure at a nominal condition of a parameter, and obtaining second dataset representative of a template image object of the structure at a non-nominal condition of the parameter. The method may further include obtaining a deformation field representative of changes between the first dataset and the second dataset. The deformation field may be generated by transforming the second dataset to project the template image object onto the reference image object. A dependence relationship between the deformation field and change in the parameter may be obtained.

公开(公告)号：US20250147436A1

公开(公告)日：2025-05-08

申请号：US18832408

申请日：2023-01-23

Applicant: ASML NETHERLANDS B.V.

Inventor： Chrysostomos BATISTAKIS ,  Huaichen ZHANG ,  Maxim PISARENCO ,  Vahid BASTANI ,  Konstantin Sergeevich NECHAEV ,  Roy ANUNCIADO ,  Stefan Cornelis Theodorus VAN DER SANDEN

IPC: G03F7/00

Abstract: A method for determining a parameter of interest relating to at least one structure formed on a substrate in a manufacturing process. The method includes: obtaining layout data relating to a layout of a pattern to be applied to the at least one structure, the pattern including the at least one structure; and obtaining a trained model, having been trained on metrology data and the layout data to infer a value and/or probability metric relating to a parameter of interest from at least the layout data, the metrology data relating to a plurality of measurements of the parameter of interest at a respective plurality of measurement locations on the substrate. A value and/or probability metric is determined relating to the parameter of interest at one or more locations on the substrate different from the measurement locations from at least layout data using the trained model.

公开(公告)号：US20250103964A1

公开(公告)日：2025-03-27

申请号：US18971818

申请日：2024-12-06

Applicant: ASML Netherlands B.V.

Inventor： Maxim PISARENCO ,  Chrysostomos BATISTAKIS

IPC: G06N20/00

Abstract: A method of training a generator model comprising: using the generator model to generate the predictive data based on the first measured data, wherein the first measured data and the predictive data can be used to form images of the sample; pairing subsets of the first measured data with subsets of the predictive data, the subsets corresponding to locations within the images of the sample that can be formed from the first measured data and the predictive data; using a discriminator to evaluate a likelihood that the predictive data comes from a same data distribution as second measured data measured from a sample after an etching process; and training the generator model based on: correlation for the pairs corresponding to a same location relative to correlation for pairs corresponding to different locations, the correlation being the correlation between the paired subsets of data, and the likelihood evaluated by the discriminator.

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

IPC: G06V10/24 ,  G06T11/00

CPC classification number: G06V10/24 ,  G06T11/00

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.

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

IPC: G06T7/593 ,  G06T5/50 ,  G06T7/13

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.

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.

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

IPC: G03F7/20 ,  G06N3/08 ,  G06T7/00

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.

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

IPC: G01N21/47 ,  G01N21/95 ,  G01N21/956 ,  G03F7/20 ,  G01N29/06 ,  G01N29/44

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.

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.