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公开(公告)号：WO2021032448A1
公开(公告)日：2021-02-25
申请号：PCT/EP2020/071742
申请日：2020-08-01
Applicant: ASML NETHERLANDS B.V.
Inventor： ZHENG, Yunan , FAN, Yongfa , FENG, Mu , ZHENG, Leiwu , WANG, Jen-Shiang , LUO, Ya , ZHANG, Chenji , CHEN, Jun , HOU, Zhenyu , WANG, Jinze , CHEN, Feng , MA, Ziyang , GUO, Xin , CHENG, Jin
IPC: G03F7/20
Abstract: Described herein are method generating modified simulated contours and/or generate metrology gauges based on the modified contours. A method of generating metrology gauges for measuring a physical characteristic of a structure on a substrate includes obtaining (i) measured data associated with the physical characteristic of the structure printed on the substrate, and (ii) at least portion of a simulated contour of the structure, the portion of the simulated contour being associated with the measured data; modifying, based on the measured data, the portion of the simulated contour of the structure; and generating the metrology gauges on or adjacent to the modified portion of the simulated contour, the metrology gauges being placed to measure the physical characteristic of the simulated contour of the structure.
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2.发明申请
公开(公告)号：WO2020169303A1
公开(公告)日：2020-08-27
申请号：PCT/EP2020/051778
申请日：2020-01-24
Applicant: ASML NETHERLANDS B.V.
Inventor： TAO, Jun , BARON, Stanislas, Hugo, Louis , SU, Jing , LUO, Ya , CAO, Yu
Abstract: Described herein are training methods and a mask correction method. One of the methods is for training a machine learning model configured to predict a post optimal proximity correction (OPC) image for a mask. The method involves obtaining (i) a pre-OPC image associated with a design layout to be printed on a substrate, (ii) an image of one or more assist features for the mask associated with the design layout, and (iii) a reference post- OPC image of the design layout; and training the machine learning model using the pre-OPC image and the image of the one or more assist features as input such that a difference between the reference image and a predicted post-OPC image of the machine learning model is reduced.
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公开(公告)号：WO2019162346A1
公开(公告)日：2019-08-29
申请号：PCT/EP2019/054246
申请日：2019-02-20
Applicant: ASML NETHERLANDS B.V.
Inventor： CAO, Yu , LUO, Ya , LU, Yen-Wen , CHEN, Been-Der , HOWELL, Rafael C. , ZOU, Yi , SU, Jing , SUN, Dezheng
Abstract: Described herein are different methods of training machine learning models related to a patterning process. Described herein is a method for training a machine learning model configured to predict a mask pattern. The method including obtaining (i) a process model of a patterning process configured to predict a pattern on a substrate, wherein the process model comprises one or more trained machine learning models, and (ii) a target pattern, and training, by a hardware computer system, the machine learning model configured to predict a mask pattern based on the process model and a cost function that determines a difference between the predicted pattern and the target pattern.
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公开(公告)号：WO2019048506A1
公开(公告)日：2019-03-14
申请号：PCT/EP2018/073914
申请日：2018-09-05
Applicant: ASML NETHERLANDS B.V.
Inventor： SU, Jing , LU, Yen-Wen , LUO, Ya
Abstract: A method including: obtaining an optical proximity correction for a spatially shifted version of a training design pattern (5000); and training a machine learning model (5200) configured to predict optical proximity corrections for design patterns using data (5051; 5053) regarding the spatially shifted version of the training design pattern and data (5041; 5043) based on the optical proximity corrections for the spatially shifted version of the training design pattern.
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公开(公告)号：WO2020193095A1
公开(公告)日：2020-10-01
申请号：PCT/EP2020/055785
申请日：2020-03-05
Applicant: ASML NETHERLANDS B.V.
Inventor： MA, Ziyang , CHENG, Jin , LUO, Ya , ZHENG, Leiwu , GUO, Xin , WANG, Jen-Shiang , FAN, Yongfa , CHEN, Feng , CHEN, Yi-Yin , ZHANG, Chenji , LU, Yen-Wen
Abstract: A method for training a patterning process model, the patterning process model configured to predict a pattern that will be formed on a patterning process. The method involves obtaining an image data associated with a desired pattern, a measured pattern of the substrate, a first model comprising a first set of parameters, and a machine learning model comprising a second set of parameters; and iteratively determining values of the first set of parameters and the second set of parameters to train the patterning process model. An iteration involves executing, using the image data, the first model and the machine learning model to cooperatively predict a printed pattern of the substrate; and modifying the values of the first set of parameters and the second set of parameters such that a difference between the measured pattern and the predicted pattern of the patterning process model is reduced.
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Patent Agency Ranking
公开(公告)号：WO2018215188A1
公开(公告)日：2018-11-29
申请号：PCT/EP2018/061488
申请日：2018-05-04
Applicant: ASML NETHERLANDS B.V.
Inventor： SU, Jing , ZOU, Yi , LIN, Chenxi , CAO, Yu , LU, Yen-Wen , CHEN, Been-Der , ZHANG, Quan , BARON, Stanislas, Hugo, Louis , LUO, Ya
Abstract: A method including: obtaining a portion (505) of a design layout; determining (520) characteristics (530) of assist features based on the portion or characteristics (510) of the portion; and training (550) a machine learning model using training data (540) comprising a sample whose feature vector comprises the characteristics (510) of the portion and whose label comprises the characteristics (530) of the assist features. The machine learning model may be used to determine (560) characteristics of assist features of any portion of a design layout, even if that portion is not part of the training data.
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公开(公告)号：WO2018153866A1
公开(公告)日：2018-08-30
申请号：PCT/EP2018/054165
申请日：2018-02-20
Applicant: ASML NETHERLANDS B.V.
Inventor： LUO, Ya , CAO, Yu , WANG, Jen-Shiang , LU, Yen-Wen
Abstract: Disclosed herein are methods of determining, and using, a process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.
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公开(公告)号：WO2021037484A1
公开(公告)日：2021-03-04
申请号：PCT/EP2020/071741
申请日：2020-07-31
Applicant: ASML NETHERLANDS B.V.
Inventor： HUNSCHE, Stefan , WANG, Fuming , LUO, Ya , NIKOLSKI, Pioter
Abstract: Systems and methods for predicting substrate geometry associated with a patterning process are described. Input information including geometry information and/or process information for a pattern are received; and, using a machine learning prediction model, multi-dimensional output substrate geometry is predicted. The multi-dimensional output information comprises pattern probability images. A stochastic edge placement error band and/or a stochastic failure rate may be predicted based on the pattern probability images. The input information comprises simulated aerial images, simulated resist images, target substrate dimensions, and/or data from a scanner associated with semiconductor device manufacturing. Different aerial images may correspond to different heights in resist layers associated with the patterning process, for example.
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公开(公告)号：WO2021028228A1
公开(公告)日：2021-02-18
申请号：PCT/EP2020/071453
申请日：2020-07-30
Applicant: ASML NETHERLANDS B.V.
Inventor： MA, Ziyang , CHENG, Jin , LUO, Ya , ZHENG, Leiwu , GUO, Xin , WANG, Jen-Shiang
Abstract: Described herein is a method for training a machine learning model configured to predict values of a physical characteristic associated with a substrate for use in adjusting a patterning process. The method involves obtaining a reference image; determining a first set of model parameter values of the machine learning model such that a first cost function is reduced from an initial value of the cost function obtained using an initial set of model parameter values, where the first cost function is a difference between the reference image and an image generated via the machine learning model; and training, using the first set of model parameter values, the machine learning model such that a combination of the first cost function and a second cost function is iteratively reduced, the second cost function is a difference between measured values and predicted values.
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10.发明申请
公开(公告)号：WO2018153735A1
公开(公告)日：2018-08-30
申请号：PCT/EP2018/053589
申请日：2018-02-13
Applicant: ASML NETHERLANDS B.V.
Inventor： LIU, Peng , LUO, Ya , CAO, Yu , LU, Yen-Wen
Abstract: A method including: obtaining characteristics of a portion of a design layout; determining characteristics of M3D of a patterning device including or forming the portion; by using a computer, training a neural network using training data including a sample whose feature vector includes the characteristics of the portion and whose supervisory signal comprises the characteristics of the M3D. Also disclosed is a method including: obtaining characteristics of a portion of a design layout; obtaining characteristics of a lithographic process that uses a patterning device including or forming the portion; determining characteristics of a result of the lithographic process; by using a computer, training a neural network using training data including a sample whose feature vector comprises the characteristics of the portion and the characteristics of the lithographic process, and whose supervisory signal comprises the characteristics of the result.
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