Asml Netherlands B.v.

Jasper Menger

Richard Johannes Franciscus Van Haren

Scott Anderson Middlebrooks

Alexander Ypma

Jochem Sebastiaan Wildenberg

Adrianus Cornelis Matheus Koopman

Irina Lyulina

David Han

Everhardus Cornelis Mos

David Deckers

Paul Cornelis Hubertus Aben

Birgitt Noëlle Cornelia Liduine Hepp

Paul Cornelius Hubertus Aben

In a lithographic process, product units such as semiconductor wafers are subjected to lithographic patterning operations and chemical and physical processing operations. Alignment data or other measurements are made at stages during the performance of the process to obtain object data representing positional deviation or other parameters measured at points spatially distributed across each unit. This object data is used to obtain diagnostic information by performing a multivariate analysis to decompose a set of vectors representing the units in the multidimensional space into one or more component vectors. Diagnostic information about the industrial process is extracted using the component vectors. The performance of the industrial process for subsequent product units can be controlled based on the extracted diagnostic information.

Methods and apparatus for obtaining diagnostic information relating to an industrial process

Offline metrology measurements are performed on substrates that have been subjected to lithographic processing. Model parameters are calculated by fitting the measurements to an extended high-order substrate model defined using a combination of basis functions that include an edge basis function related to a substrate edge. A radial edge basis function may be expressed in terms of distance from a substrate edge. The edge basis function may, for example, be an exponential decay function or a rational function. Lithographic processing of a subsequent substrate is controlled using the calculated high-order substrate model parameters, in combination with low-order substrate model parameters obtained by fitting inline measurements to a low order model.

Methods and apparatus for calculating substrate model parameters and controlling lithographic processing

Metrology measurements are performed on substrates that have been subjected to lithographic processing. Model parameters are calculated by fitting the measurements to an extended high-order substrate model defined using a combination of basis functions that include an edge basis function related to a substrate edge. A radial edge basis function may be expressed in terms of distance from a substrate edge. The edge basis function may, for example, be an exponential decay function or a rational function. Lithographic processing of a subsequent substrate is controlled using the calculated high-order substrate model parameters, in combination with low-order substrate model parameters obtained by fitting inline measurements to a low order model.

In a lithographic process, product units such as semiconductor wafers are subjected to lithographic patterning operations and chemical and physical processing operations. Alignment data or other measurements are made at stages during the performance of the process to obtain object data representing positional deviation or other parameters measured at points spatially distributed across each unit. This object data is used to obtain diagnostic information by performing a multivariate analysis to decompose a set of vectors representing the units in said multidimensional space into one or more component vectors. Diagnostic information about the industrial process is extracted using the component vectors. The performance of the industrial process for subsequent product units can be controlled based on the extracted diagnostic information.

A higher-level controller can correct measured metrology data with residual error values as reported by a lower-level controller. This results in a more accurate process disturbance estimate. A method of control obtains, based on measurement sample definition, a first process variable of a system under control, determines a residual error using the first process variable and a first set point, controls the system using the residual error, obtains, based on the same sample definition, a second process variable, and adjusts the second process variable using the residual error. The method may also include determining, using the adjusted second process variable, one or more first set points for controlling the system by the low-level controller that may vary in correspondence with the sample definition.

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Eindhoven, Netherlands

Jasper Menger