Asml Netherlands B.v.

Christophe David Fouquet

Arie Jeffrey Den Boef

Johannes Catharinus Hubertus Mulkens

Maurits Van Der Schaar

Richard Johannes Franciscus Van Haren

Kaustuve Bhattacharyya

Martin Jacobus Johan Jak

Hendrik Jan Hidde Smilde

Henricus Wilhelmus Maria Van Buel

Neal Patrick Callan

Xing Lan Liu

Andreas Fuchs

James Patrick Koonmen

Omer Abubaker Omer Adam

Bernardo Kastrup

Johannes Marcus Maria Beltman

James Benedict Kavanagh

Michael Kubis

MIchael Kubis

A diffraction measurement target that has at least a first sub-target and at least a second sub-target, and wherein (1) the first and second sub-targets each include a pair of periodic structures and the first sub-target has a different design than the second sub-target, the different design including the first sub-target periodic structures having a different pitch, feature width, space width, and/or segmentation than the second sub-target periodic structure or (2) the first and second sub-targets respectively include a first and second periodic structure in a first layer, and a third periodic structure is located at least partly underneath the first periodic structure in a second layer under the first layer and there being no periodic structure underneath the second periodic structure in the second layer, and a fourth periodic structure is located at least partly underneath the second periodic structure in a third layer under the second layer.

Metrology method, target and substrate

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

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.

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Retie, Belgium

Christophe David Fouquet