Asml Netherlands B.v.

Asml Holding N.v.

Bernardo Kastrup

Johannes Catharinus Hubertus Mulkens

Arie Jeffrey Den Boef

Neal Patrick Callan

James Patrick Koonmen

Christophe David Fouquet

James Benedict Kavanagh

Coen Adrianus Verschuren

Jozef Petrus Henricus Benschop

Marinus Aart Van Den Brink

Erwin Paul Smakman

Tamara Druzhinina

Patricius Aloysius Jacobus Tinnemans

Richard Johannes Franciscus Van Haren

Everhardus Cornelis Mos

Jozef Maria Finders

Kars Zeger Troost

Michael Kubis

Peter Ten Berge

Jason Douglas Hintersteiner

Wenceslao A Cebuhar

Peter Hanzen Wardenier

David Frans Simon Deckers

Wolfgang Helmut Henke

Erik Jensen

Ronald Peter Albright

Sander De Putter

Joungchel Lee

A defect prediction method for a device manufacturing process involving processing a portion of a design layout onto a substrate, the method including: identifying a hot spot from the portion of the design layout; determining a range of values of a processing parameter of the device manufacturing process for the hot spot, wherein when the processing parameter has a value outside the range, a defect is produced from the hot spot with the device manufacturing process; determining an actual value of the processing parameter; determining or predicting, using the actual value, existence, probability of existence, a characteristic, or a combination thereof, of a defect produced from the hot spot with the device manufacturing process.

Computational wafer inspection

An electron beam inspection apparatus, the apparatus including a plurality of electron beam columns, each electron beam column configured to provide an electron beam and detect scattered or secondary electrons from an object, and an actuator system configured to move one or more of the electron beam columns relative to another one or more of the electron beam columns, the actuator system including a plurality of first movable structures at least partly overlapping a plurality of second movable structures, the first and second movable structures supporting the plurality of electron beam columns.

Method and apparatus for inspection

An electron beam inspection apparatus, the apparatus including a plurality of electron beam columns, each electron beam column configured to provide an electron beam and detect scattered or secondary electrons from an object, and an actuator system configured to move one or more of the electron beam columns relative to another one or more of the electron beam columns. The actuator system may include a plurality of first movable structures at least partly overlapping a plurality of second movable structures, the first and second movable structures supporting the plurality of electron beam columns.

A method including obtaining a measurement and/or simulation result of a pattern after being processed by an etch tool of a patterning system, determining a patterning error due to an etch loading effect based on the measurement and/or simulation result, and creating, by a computer system, modification information for modifying a patterning device and/or for adjusting a modification apparatus upstream in the patterning system from the etch tool based on the patterning error, wherein the patterning error is converted to a correctable error and/or reduced to a certain range, when the patterning device is modified according to the modification information and/or the modification apparatus is adjusted according to the modification information.

Method and apparatus to correct for patterning process error

A computer-implemented defect prediction method for a device manufacturing process involving processing a portion of a design layout onto a substrate, the method including: identifying a hot spot from the portion of the design layout; determining a range of values of a processing parameter of the device manufacturing process for the hot spot, wherein when the processing parameter has a value outside the range, a defect is produced from the hot spot with the device manufacturing process; determining an actual value of the processing parameter; determining or predicting, using the actual value, existence, probability of existence, a characteristic, or a combination thereof, of a defect produced from the hot spot with the device manufacturing process.

Disclosed herein is a computer-implemented defect prediction method for a device manufacturing process involving processing a portion of a design layout onto a substrate, the method comprising: identifying a hot spot from the portion of the design layout; determining a range of values of a processing parameter of the device manufacturing process for the hot spot, wherein when the processing parameter has a value outside the range, a defect is produced from the hot spot with the device manufacturing process; determining an actual value of the processing parameter; determining or predicting, using the actual value, existence, probability of existence, a characteristic, or a combination thereof, of a defect produced from the hot spot with the device manufacturing process.

Disclosed herein is a computer-implemented defect prediction method for a device manufacturing process involving processing a portion of a design layout onto a substrate, the method comprising: identifying a hot spot from the portion of the design layout; determining a range of values of a processing parameter of the device manufacturing process for the hot spot, wherein when the processing parameter has a value outside the range, a defect is produced from the hot spot with the device manufacturing process; determining an actual value of the processing parameter; and determining or predicting, using the actual value, an existence, a probability of existence, a characteristic, or a combination selected therefrom, of a defect produced from the hot spot with the device manufacturing process.

A method to determine an improved configuration for a lithography apparatus, a computer-readable medium for use in carrying out the method, and a lithography apparatus are disclosed. In an example, the method involves intelligent selection of one or more device features to measure and use in a routine to optimize the configuration of the lithography apparatus. According to an example, the method comprises imposing a target error profile to one or more device features for which measurement data is not sufficient, for example in a regions where a selected device feature is sparsely distributed.

Lithographic apparatus and methods for determining an improved configuration of a lithographic apparatus

An iterative method of producing optimized setpoint data for controlling the actuation of elements of an array of individually controllable elements in a maskless system and systems therefore. The optimization is based on estimation of a device structure that can utilizes one or more of the following factors: the low-pass characteristics of the projection system, the configuration of the illumination system, and the process window properties.

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Veldhoven, Netherlands

Bernardo Kastrup