METHOD FOR PREDICTING STOCHASTIC CONTRIBUTORS

    公开(公告)号:WO2021229030A1

    公开(公告)日:2021-11-18

    申请号:PCT/EP2021/062772

    申请日:2021-05-12

    Abstract: Described herein is a method for training a machine learning model to determine a source of error contribution to multiple features of a pattern printed on a substrate. The method includes obtaining training data having multiple datasets, wherein each dataset has error contribution values representative of an error contribution from one of multiple sources to the features, and wherein each dataset is associated with an actual classification that identifies a source of the error contribution of the corresponding dataset; and training, based on the training data, a machine learning model to predict a classification of a reference dataset of the datasets such that a cost function that determines a difference between the predicted classification and the actual classification of the reference dataset is reduced.

    PROCESS WINDOW BASED ON DEFECT PROBABILITY
    4.
    发明申请

    公开(公告)号:WO2019121486A1

    公开(公告)日:2019-06-27

    申请号:PCT/EP2018/085159

    申请日:2018-12-17

    Abstract: Described herein is a method. The method includes steps for obtaining (i) measurements of a parameter of the feature, (ii) data related to a process variable of a patterning process, (iii) a functional behavior of the parameter defined as a function of the process variable based on the measurements of the parameter and the data related to the process variable, (iv) measurements of a failure rate of the feature, and (v) a probability density function of the process variable for a setting of the process variable, converting the probability density function of the process variable to a probability density function of the parameter based on a conversion function, where the conversion function is determined based on the function of the process variable, and determining a parameter limit of the parameter based on the probability density function of the parameter and the measurements of the failure rate.

    METHOD FOR DETERMINING DEFECTIVENESS OF PATTERN BASED ON AFTER DEVELOPMENT IMAGE

    公开(公告)号:EP3789826A1

    公开(公告)日:2021-03-10

    申请号:EP19195527.7

    申请日:2019-09-05

    Abstract: Described herein is a method of training a model configured to predict whether a feature associated with an imaged substrate will be defective after etching of the imaged substrate and determining etch conditions based on the trained model. The method includes obtaining, via a metrology tool, (i) an after development image of the imaged substrate at a given location, the after development image including a plurality of features, and (ii) an after etch image of the imaged substrate at the given location; and training, using the after development image and the after etch image, the model configured to determine defectiveness of a given feature of the plurality of features in the after development image. In an embodiment, the determining of defectiveness is based on comparing the given feature in the after development image with a corresponding etch feature in the after etch image.

    METHOD OF DETERMINING CONTROL PARAMETERS OF A DEVICE MANUFACTURING PROCESS

    公开(公告)号:EP3462240A1

    公开(公告)日:2019-04-03

    申请号:EP17193430.0

    申请日:2017-09-27

    Abstract: Disclosed herein is a method in the manufacturing process of a device on a substrate, wherein the manufacturing process comprises a lithographic process of imaging a portion of a design layout onto the substrate using a lithographic apparatus and one or more further processes in the manufacturing process of the device, the method comprising: obtaining an image of at least part of the substrate, wherein the image comprises at least one feature comprised by the device being manufactured on the substrate; calculating one or more image-related metrics in dependence on a contour determined from the image comprising the at least one feature; determining one or more control parameters of the lithographic apparatus and/or said one or more further processes in the manufacturing process of the device in dependence on the one or more image-related metrics. Advantageously, the determination of the control parameters is improved.

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