Abstract: Methods and systems for evaluating the performance of multiple patterning processes are presented. Patterned structures are measured and one or more parameter values characterizing geometric errors induced by the multiple patterning process are determined. In some examples, a single patterned target and a multiple patterned target are measured, the collected data fit to a combined measurement model, and the value of a structural parameter indicative of a geometric error induced by the multiple patterning process is determined based on the fit. In some other examples, light having a diffraction order different from zero is collected and analyzed to determine the value of a structural parameter that is indicative of a geometric error induced by a multiple patterning process. In some embodiments, a single diffraction order different from zero is collected. In some examples, a metrology target is designed to enhance light diffracted at an order different from zero.

Abstract: Disclosed are apparatus and methods for determining optimal focus for a photolithography system. A plurality of optical signals are acquired from a particular target located in a plurality of fields on a semiconductor wafer, and the fields were formed using different process parameters, including different focus values. A feature is extracted from the optical signals related to changes in focus. A symmetric curve is fitted to the extracted feature of the optical signals as a function of focus. An extreme point in the symmetric curve is determined and reported as an optimal focus for use in the photolithography system.

Abstract: Disclosed are apparatus and methods for characterizing a plurality of structures of interest on a semiconductor wafer. A plurality of spectra signals are measured from a particular structure of interest at a plurality of azimuth angles from one or more sensors of a metrology system. A difference spectrum is determined based on the spectra signals obtained for the azimuth angles. A quality indication of the particular structure of interest is determined and reported based on analyzing the difference spectrum.

Abstract: Disclosed are apparatus and methods for determining process or structure parameters for semiconductor structures. A plurality of optical signals is acquired from one or more targets located in a plurality of fields on a semiconductor wafer. The fields are associated with different process parameters for fabricating the one or more targets, and the acquired optical signals contain information regarding a parameter of interest (POI) for a top structure and information regarding one or more underlayer parameters for one or more underlayers formed below such top structure. A feature extraction model is generated to extract a plurality of feature signals from such acquired optical signals so that the feature signals contain information for the POI and exclude information for the underlayer parameters. A POI value for each top structure of each field is determined based on the feature signals extracted by the feature extraction model.

Abstract: An optimized measurement model is determined based a model of parameter variations across a semiconductor wafer. A global, cross-wafer model characterizes a structural parameter as a function of location on the wafer. A measurement model is optimized by constraining the measurement model with the cross-wafer model of process variations. In some examples, the cross-wafer model is itself a parameterized model. However, the cross-wafer model characterizes the values of a structural parameter at any location on the wafer with far fewer parameters than a measurement model that treats the structural parameter as unknown at every location. In some examples, the cross-wafer model gives rise to constraints among unknown structural parameter values based on location on the wafer. In one example, the cross-wafer model relates the values of structural parameters associated with groups of measurement sites based on their location on the wafer.

Abstract: Dynamic removal of correlation of highly-correlated parameters for optical metrology is described. An embodiment of a method includes determining a model of a structure, the model including a set of parameters; performing optical metrology measurement of the structure, including collecting spectra data on a hardware element; during the measurement of the structure, dynamically removing correlation of two or more parameters of the set of parameters, an iteration of the dynamic removal of correlation including: generating a Jacobian matrix of the set of parameters, applying a singular value decomposition of the Jacobian matrix, selecting a subset of the set of parameters, and computing a direction of the parameter search based on the subset of parameters. If the model does not converge, performing one or more additional iterations of the dynamic removal of correlation until the model converges; and if the model does converge, reporting the results of the measurement.

Abstract: Methods and systems for evaluating the capability of a measurement system to track measurement parameters through a given process window are presented herein. Performance evaluations include random perturbations, systematic perturbations, or both to effectively characterize the impact of model errors, metrology system imperfections, and calibration errors, among others. In some examples, metrology target parameters are predetermined as part of a Design of Experiments (DOE). Estimated values of the metrology target parameters are compared to the known DOE parameter values to determine the tracking capability of the particular measurement. In some examples, the measurement model is parameterized by principal components to reduce the number of degrees of freedom of the measurement model. In addition, exemplary methods and systems for optimizing the measurement capability of semiconductor metrology systems for metrology applications subject to process variations are presented.

Abstract: Methods and systems for performing semiconductor metrology directly on device structures are presented. A measurement model is created based on measured training data collected from at least one device structure. The trained measurement model is used to calculate process parameter values, structure parameter values, or both, directly from measurement data collected from device structures of other wafers. In some examples, measurement data from multiple targets is collected for model building, training, and measurement. In some examples, the use of measurement data associated with multiple targets eliminates, or significantly reduces, the effect of under layers in the measurement result, and enables more accurate measurements. Measurement data collected for model building, training, and measurement may be derived from measurements performed by a combination of multiple, different measurement techniques.

Abstract: Methods and systems for creating a measurement model based on measured training data are presented. The trained measurement model is used to calculate process parameter values, structure parameter values, or both, directly from measured data collected from other wafers. The measurement models receive measurement data directly as input and provide process parameter values, structure parameter values, or both, as output. The measurement model enables the direct measurement of process parameters. Measurement data from multiple targets is collected for model building, training, and measurement. In some examples, the use of measurement data associated with multiple targets eliminates, or significantly reduces, the effect of under layers in the measurement result, and enables more accurate measurements. Measurement data collected for model building, training, and measurement, may be derived from measurements performed by a combination of multiple, different measurement techniques.

Abstract: An optimized measurement model is determined based a model of parameter variations across a semiconductor wafer. A global, cross-wafer model characterizes a structural parameter as a function of location on the wafer. A measurement model is optimized by constraining the measurement model with the cross-wafer model of process variations. In some examples, the cross-wafer model is itself a parameterized model. However, the cross-wafer model characterizes the values of a structural parameter at any location on the wafer with far fewer parameters than a measurement model that treats the structural parameter as unknown at every location. In some examples, the cross-wafer model gives rise to constraints among unknown structural parameter values based on location on the wafer. In one example, the cross-wafer model relates the values of structural parameters associated with groups of measurement sites based on their location on the wafer.

Abstract: Methods and systems for performing measurements based on a measurement model integrating a metrology-based target model with a process-based target model. Systems employing integrated measurement models may be used to measure structural and material characteristics of one or more targets and may also be used to measure process parameter values. A process-based target model may be integrated with a metrology-based target model in a number of different ways. In some examples, constraints on ranges of values of metrology model parameters are determined based on the process-based target model. In some other examples, the integrated measurement model includes the metrology-based target model constrained by the process-based target model. In some other examples, one or more metrology model parameters are expressed in terms of other metrology model parameters based on the process model. In some other examples, process parameters are substituted into the metrology model.

Abstract: An optimized measurement model is determined based a model of parameter variations across a semiconductor wafer. A global, cross-wafer model characterizes a structural parameter as a function of location on the wafer. A measurement model is optimized by constraining the measurement model with the cross-wafer model of process variations. In some examples, the cross-wafer model is itself a parameterized model. However, the cross-wafer model characterizes the values of a structural parameter at any location on the wafer with far fewer parameters than a measurement model that treats the structural parameter as unknown at every location. In some examples, the cross-wafer model gives rise to constraints among unknown structural parameter values based on location on the wafer. In one example, the cross-wafer model relates the values of structural parameters associated with groups of measurement sites based on their location on the wafer.

Abstract: An optimized measurement recipe is determined by reducing the set of measurement technologies and ranges of machine parameters required to achieve a satisfactory measurement result. The reduction in the set of measurement technologies and ranges of machine parameters is based on available process variation information and spectral sensitivity information associated with an initial measurement model. The process variation information and spectral sensitivity information are used to determine a second measurement model having fewer floating parameters and less correlation among parameters. Subsequent measurement analysis is performed using the second, constrained model and a set of measurement data corresponding to a reduced set of measurement technologies and ranges of machine parameters.

Abstract: Model optimization approaches based on spectral sensitivity is described. For example, a method includes determining a first model of a structure. The first model is based on a first set of parameters. A set of spectral sensitivity variations data is determined for the structure. Spectral sensitivity is determined by derivatives of the spectra with respect to the first set of parameters. The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data. The second model of the structure is based on a second set of parameters different from the first set of parameters. A simulated spectrum derived from the second model of the structure is then provided.