Asml Netherlands B.v.

Marleen Kooiman

Sander Frederik Wuister

Abraham Slachter

Wim Tjibbo Tel

Stefan Hunsche

Maxim Pisarenco

Mark John Maslow

Hermanus Adrianus Dillen

Koenraad Van Ingen Schenau

Fuming Wang

Ruben Cornelis Maas

Marie-Claire Van Lare

Sudharshanan Raghunathan

Aiqin Jiang

Vadim Yourievich Timoshkov

Bernardo Andres Oyarzun Rivera

David Marie Rio

Luis Alberto Colina Santamaría Colina

Methods related to improving a simulation processes and solutions (e.g., retargeted patterns) associated with manufacturing of a chip. A method includes obtaining a plurality of dose-focus settings, and a reference distribution based on measured values of a characteristic of a printed pattern associated with each setting of the plurality of dose-focus settings. The method further includes, based on an adjustment model and the plurality of dose-focus settings, determining a probability density function (PDF) of the characteristic such that an error between the PDF and the reference distribution is reduced. The PDF can be a function of the adjustment model and variance associated with dose, the adjustment model being configured to change a proportion of non-linear dose sensitivity contribution to the PDF. A process window can be adjusted based on the determined PDF of the characteristic.

Method for calibrating simulation process based on defect-based process window

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 for determining defectiveness of pattern based on after development image

A method for calibrating a resist model. The method includes: generating a modeled resist contour of a resist structure based on a simulated aerial image of the resist structure and parameters of the resist model, and predicting a metrology contour of the resist structure from the modeled resist contour based on information of an actual resist structure obtained by a metrology device. The method includes adjusting one or more of the parameters of the resist model based on a comparison of the predicted metrology contour and an actual metrology contour of the actual resist structure obtained by the metrology device.

Systems and methods for reducing resist model prediction errors

A method of reducing variability of an error associated with a structure on a substrate in a lithography process is disclosed. The method includes determining, based on one or more images obtained based on a scan of the substrate by a scanning electron microscope (SEM), a first error due to a SEM distortion in the image. The method also includes determining, based on the image, a second error associated with a real error of the structure, where the error associated with the structure includes the first error and the second error. A command is generated by a data processor that enables a modification of the lithography process and an associated reduction of the variability of the error based on reducing any of the first error or the second error.

SEM FOV fingerprint in stochastic EPE and placement measurements in large FOV SEM devices

A method of predicting the dominant failure mode and/or the failure rate of a plurality of features formed on a substrate, and an associated inspection apparatus. The method may include determining a placement metric for each feature, the placement metric including a measure of whether the feature is in an expected position, and comparing a distribution of the placement metric to a reference (e.g., Gaussian) distribution. The placement metric may include a boundary metric for a plurality of boundary points on a boundary defining each feature, the boundary metric including a measure of whether a boundary point is in an expected position. The dominant failure mode and/or the failure rate of the plurality of features is predicted from the comparison.

Method for controlling a manufacturing apparatus and associated apparatuses

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Eindhoven, Netherlands

Marleen Kooiman