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: A system, method and computer program product are provided for selecting signals to be measured utilizing a metrology tool that optimizes the precision of the measurement. The technique includes the steps of simulating a set of signals for measuring one or more parameters of a metrology target. A normalized Jacobian matrix corresponding to the set of signals is generated, a subset of signals in the simulated set of signals is selected that optimizes a performance metric associated with measuring the one or more parameters of the metrology target based on the normalized Jacobian matrix, and a metrology tool is utilized to collect a measurement for each signal in the subset of signals for the metrology target. For a given number of signals collected by the metrology tool, this technique optimizes the precision of such measurements over conventional techniques that collect signals uniformly distributed over a range of process parameters.

Abstract: Methods and systems for building and using a parameter isolation model to isolate measurement signal information associated with a parameter of interest from measurement signal information associated with incidental model parameters are presented herein. The parameter isolation model is trained by mapping measurement signals associated with a first set of instances of a metrology target having known values of a plurality of incidental model parameters and known values of a parameter of interest to measurement signals associated with a second set of instances of the metrology target having nominal values of the plurality of incidental model parameters and the known values of the parameter of interest. The trained parameter isolation model receives raw measurement signals and isolates measurement signal information associated with a specific parameter of interest for model-based parameter estimation.

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: Methods and systems for estimating values of parameters of interest based on repeated measurements of a wafer during a process interval are presented herein. In one aspect, one or more optical metrology subsystems are integrated with a process tool, such as an etch tool or a deposition tool. Values of one or more parameters of interest measured while the wafer is being processed are used to control the process itself. The measurements are performed quickly and with sufficient accuracy to enable yield improvement of a semiconductor fabrication process flow. In one aspect, values of one or more parameters of interest are estimated based on spectral measurements of wafers under process using a trained signal response metrology (SRM) measurement model. In another aspect, a trained signal decontamination model is employed to generate decontaminated optical spectra from measured optical spectra while the wafer is being processed.

Abstract: Methods and systems for combining information present in measured images of semiconductor wafers with additional measurements of particular structures within the measured images are presented herein. In one aspect, an image-based signal response metrology (SRM) model is trained based on measured images and corresponding reference measurements of particular structures within each image. The trained, image-based SRM model is then used to calculate values of one or more parameters of interest directly from measured image data collected from other wafers. In another aspect, a measurement signal synthesis model is trained based on measured images and corresponding measurement signals generated by measurements of particular structures within each image by a non-imaging measurement technique.

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 combining information present in measured images of semiconductor wafers with additional measurements of particular structures within the measured images are presented herein. In one aspect, an image-based signal response metrology (SRM) model is trained based on measured images and corresponding reference measurements of particular structures within each image. The trained, image-based SRM model is then used to calculate values of one or more parameters of interest directly from measured image data collected from other wafers. In another aspect, a measurement signal synthesis model is trained based on measured images and corresponding measurement signals generated by measurements of particular structures within each image by a non-imaging measurement technique.

Abstract: Methods and systems for estimating values of parameters of interest of structures fabricated on a wafer with a signal response metrology (SRM) model trained based on reference measurement data collected from the same wafer are presented herein. In one aspect, the SRM model is an input-output model trained to establish a functional relationship between reference measurements of structures fabricated on the wafer to raw measurement data collected from the same wafer. The raw measurement data collected from the wafer is employed for training the SRM model and for performing measurements using the trained SRM model. In another aspect, the SRM model uses the entire set of raw measurement data collected from a number of measurement sites across the wafer for both training and subsequent measurement at each individual site. In a further aspect, the SRM model is trained and utilized to measure each parameter of interest individually.

Abstract: Methods and systems for creating a measurement model based only on measured training data are presented. The trained measurement model is then used to calculate overlay values directly from measured scatterometry data. The measurement models receive scatterometry signals directly as input and provide overlay values as output. In some embodiments, overlay error is determined from measurements of design rule structures. In some other embodiments, overlay error is determined from measurements of specialized target structures. In a further aspect, the measurement model is trained and employed to measure additional parameters of interest, in addition to overlay, based on the same or different metrology targets. In some embodiments, measurement data from multiple targets, measurement data collected by multiple metrologies, or both, is used for model building, training, and measurement. In some embodiments, an optimization algorithm automates the measurement model building and training process.

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: Methods and systems for solving measurement models of complex device structures with reduced computational effort are presented. In some embodiments, a measurement signal transformation model is employed to compute transformed measurement signals from coarse measurement signals. The transformed measurement signals more closely approximate a set of measured signals than the coarse measurement signals. However, the coarse set of measured signals are computed with less computational effort than would be required to directly compute measurement signals that closely approximate the set of measured signals. In other embodiments, a measurement signal transformation model is employed to compute transformed measurement signals from actual measured signals. The transformed measurement signals more closely approximate the coarse measurement signals than the actual measured signals.

Abstract: A system, method and computer program product are provided for calibrating metrology tools. One or more design-of-experiments wafers is received for calibrating a metrology tool. A set of signals is collected by measuring the one or more wafers utilizing the metrology tool. A first transformation is determined to convert the set of signals to components, and a second transformation is determined to convert a set of reference signals to reference components. The set of reference signals is collected by measuring the one or more wafers utilizing a well-calibrated reference tool. A model is trained based on the reference components that maps the components to converted components, and the model, first transformation, and second transformation are stored in a memory associated with the metrology tool.

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.

Abstract: A spectroscopic beam profile metrology system simultaneously detects measurement signals over a large wavelength range and a large range of angles of incidence (AOI). In one aspect, a multiple wavelength illumination beam is reshaped to a narrow line shaped beam of light before projection onto a specimen by a high numerical aperture objective. After interaction with the specimen, the collected light is passes through a wavelength dispersive element that projects the range of AOIs along one direction and wavelength components along another direction of a two-dimensional detector. Thus, the measurement signals detected at each pixel of the detector each represent a scatterometry signal for a particular AOI and a particular wavelength. In another aspect, a hyperspectral detector is employed to simultaneously detect measurement signals over a large wavelength range, range of AOIs, and range of azimuth angles.

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 primary, multiple patterned target is measured and a value of a parameter of interest is directly determined from the measured data by a Signal Response Metrology (SRM) measurement model. In some other examples, a primary, multiple patterned target and an assist target are measured and a value of a parameter of interest is directly determined from the measured data by a Signal Response Metrology (SRM) measurement model. In some other examples, a primary, multiple patterned target is measured at different process steps and a value of a parameter of interest is directly determined from the measured data by a Signal Response Metrology (SRM) measurement model.

Abstract: Methods and systems for measuring overlay error between structures formed on a substrate by successive lithographic processes are presented herein. Two overlay targets, each having programmed offsets in opposite directions are employed to perform an overlay measurement. Overlay error is measured based on zero order scatterometry signals and scatterometry data is collected from each target at two different azimuth angles. In addition, methods and systems for creating an image-based measurement model based on measured, image-based training data are presented. The trained, image-based measurement model is then used to calculate values of one or more parameters of interest directly from measured image data collected from other wafers. The methods and systems for image based measurement described herein are applicable to both metrology and inspection applications.

Abstract: Methods and systems for creating an image-based measurement model based only on measured, image-based training data are presented. The trained, image-based measurement model is then used to calculate values of one or more parameters of interest directly from measured image data collected from other wafers. The image-based measurement models receive image data directly as input and provide values of parameters of interest as output. In some embodiments, the image-based measurement model enables the direct measurement of overlay error. In some embodiments, overlay error is determined from images of on-device structures. In some other embodiments, overlay error is determined from images of specialized target structures. In some embodiments, image data from multiple targets, image data collected by multiple metrologies, or both, is used for model building, training, and measurement. In some embodiments, an optimization algorithm automates the image-based measurement model building and training process.

Abstract: A system, method and computer program product are provided for combining raw data from multiple metrology tools. Reference values are obtained for at least one parameter of a training component. Signals are collected for the at least one parameter of the training component, utilizing a first metrology tool and a different second metrology tool. Further, at least a portion the signals are transformed into a set of signals, and for each of the at least one parameter of the training component, a corresponding relationship between the set of signals and the reference values is determined and a corresponding training model is created therefrom. Signals from a target component are collected utilizing at least the first metrology tool and the second metrology tool, and each created training model is applied to the signals collected from the target component to measure parametric values for the target component.

Abstract: Methods and systems for estimating values of parameters of interest of actual device structures based on optical measurements of nearby metrology targets are presented herein. High throughput, inline metrology techniques are employed to measure metrology targets located near actual device structures. Measurement data collected from the metrology targets is provided to a trained signal response metrology (SRM) model. The trained SRM model estimates the value of one or more parameters of interest of the actual device structure based on the measurements of the metrology target. The SRM model is trained to establish a functional relationship between actual device parameters measured by a reference metrology system and corresponding optical measurements of at least one nearby metrology target. In a further aspect, the trained SRM is employed to determine corrections of process parameters to bring measured device parameter values within specification.