Abstract: A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured using a metrology device. A second diffraction signal is generated using a machine learning system, where the machine learning system receives as an input one or more parameters that characterize a profile of the structure to generate the second diffraction signal. The first and second diffraction signals are compared. When the first and second diffraction signals match within a matching criterion, a feature of the structure is determined based on the one or more parameters or the profile used by the machine learning system to generate the second diffraction signal.

Abstract: To manage data flow in generating profile models for use in optical metrology, a project data object is created. A first profile model data object is created. The first profile model data object corresponds to a first profile model defined using profile parameters. A version number is associated with the first profile model data object. The first profile model data object is linked with the project data object. At least a second profile model data object is created. The second profile model data object corresponds to a second profile model defined using profile parameters. The first and second profile models are different. Another version number is associated with the second profile model data object. The second profile model data object is linked with the project data object. The project data object, the first profile model data object, and the second profile model data object are stored.

Abstract: To manage data flow in generating different signal formats for use in optical metrology, a project data object is created. A first option data object is created. The first option data object has a set of signal parameters. Different settings of the set of signal parameters correspond to different signal formats for diffraction signals. A version number is associated with the first option data object. The first option data object is linked with the project data object. At least a second option data object is created. The second option data object has a set of signal parameters. Different settings of the set of signal parameters correspond to different signal formats for diffraction signals. The set of signal parameters of the first option data object and the set of signal parameters of the second option data object are set differently. Another version number is associated with the second option data object. The second option data object is linked with the project data object.

Abstract: In allocating processing units, first and second requests for jobs are obtained. First and second numbers of processing units requested are determined. First and second numbers of available processing units are determined. When the first number of available processing units is non-zero, the first number of available number of processing units or the first number of processing units requested is assigned to a first processing cluster. A first processing unit in the first processing cluster is designated as a master node. When the second number of available processing units is non-zero, the second number of available number of processing units or the second number of processing units requested is assigned to a second processing cluster. The first processing unit in the second processing cluster is designated as a slave node. The first and second jobs are assigned to the first and second processing clusters, respectively.

Abstract: In allocating processing units of a computer system to generate simulated diffraction signals used in optical metrology, a request for a job to generate simulated diffraction signals using multiple processing units is obtained. A number of processing units requested for the job to generate simulated diffraction signals is then determined. A number of available processing units is determined. When the number of processing units requested is greater than the number of available processing units, a number of processing units is assigned to generate the simulated diffraction signals that is less than the number of processing units requested.

Abstract: A process step in fabricating a structure on a wafer in a wafer application having one or more process steps and one or more process parameters is controlled by determining a correlation between a set of profile models and one or more key profile shape variables. Each profile model is defined using a set of profile parameters to characterize the shape of the structure. Different sets of profile parameters define the profile models in the set. The one or more key profile shape variables include one or more profile parameters or one or more process parameters. One profile model is selected from the set of profile models based on the correlation and a value of at least one key profile shape variable of the process of the wafer application to be used in fabricating the structure. The structure is fabricated in a first fabrication process cluster using the process step and the value of the at least one key profile shape variable. A measured diffraction signal is obtained off the structure.

Abstract: The accuracy of a library of simulated-diffraction signals for use in optical metrology of a structure formed on a wafer is evaluated by utilizing an identity relationship inherent to simulated diffraction signals. Each simulated diffraction signal contains at least one set of four reflectivity parameters for a wavelength and/or angle of incidence. One of the four reflectivity parameters is selected. A value for the selected reflectivity parameter is determined using the identity relationship and values of the remaining three reflectivity parameters. The determined value for the selected reflectivity parameter is compared to the value in the obtained set of four reflectivity parameters to evaluate and improve the accuracy of the library. The identity relationship can also be used to reduce the data storage in a library.

Abstract: An optical metrology model for a structure to be formed on a wafer is developed by characterizing a top-view profile and a cross-sectional view profile of the structure using profile parameters. The profile parameters of the top-view profile and the cross-sectional view profile are integrated together into the optical metrology model. The profile parameters of the optical metrology model are saved.

Abstract: One or more profile parameters of a structure fabricated on a wafer in a wafer application are determined by developing a correlation between a set of profile models and one or more key profile shape variables. The wafer application has one or more process steps and one or more process parameters. Each profile model is defined using a set of profile parameters to characterize the shape of the structure. Different sets of profile parameters define the profile models in the set. The one or more key profile shape variables include one or more profile parameters or one or more process parameters. A value of at least one key profile shape variable of the process step of the wafer application to be used in fabricating the structure is determined. One profile model is selected from the set of profile models based on the determined correlation and the value of the at least one determined key profile shape variable.

Abstract: The optimization of an optical metrology model for use in measuring a wafer structure is evaluated. An optical metrology model having metrology model variables, which includes profile model parameters of a profile model, is developed. One or more goals for metrology model optimization are selected. One or more profile model parameters to be used in evaluating the one or more selected goals are selected. One or more metrology model variables to be set to fixed values are selected. One or more selected metrology model variables are set to fixed values. One or more termination criteria for the one or more selected goals are set. The optical metrology model is optimized using the fixed values for the one or more selected metrology model variables. Measurements for the one or more selected profile model parameters are obtained using the optimized optical metrology model. A determination is then made as to whether the one or more termination criteria are met by the obtained measurements.

Abstract: The profile of a single feature formed on a wafer can be determined by obtaining an optical signature of the single feature using a beam of light focused on the single feature. The obtained optical signature can then be compared to a set of simulated optical signatures, where each simulated optical signature corresponds to a hypothetical profile of the single feature and is modeled based on the hypothetical profile.

Abstract: The invention includes a method and a system for generating integrated circuit (IC) simulation information regarding the effect of design and fabrication process decisions. One embodiment includes creating and using a data store of profile-based information comprising metrology signal, structure profile data, process control parameters, and IC simulation attributes. Another embodiment includes creation and use of a simulation data store generated using test gratings that model the geometries of the IC interconnects. The interconnect simulation data store may be used in-line for monitoring electrical and thermal properties of an IC device during fabrication. Other embodiments include methods and systems for generating and using simulation data stores utilizing a metrology simulator and various combinations of a fabrication process simulator, a device simulator, and/or circuit simulator. Information from the simulation data store may be used in-line in-situ during the design or fabrication process steps.

Abstract: A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured using a metrology device. A second diffraction signal is generated using a machine learning system, where the machine learning system receives as an input one or more parameters that characterize a profile of the structure to generate the second diffraction signal. The first and second diffraction signals are compared. When the first and second diffraction signals match within a matching criterion, a feature of the structure is determined based on the one or more parameters or the profile used by the machine learning system to generate the second diffraction signal.

Abstract: A fabrication tool can be controlled using a support vector machine. A profile model of the structure is obtained. The profile model is defined by profile parameters that characterize the geometric shape of the structure. A set of values for the profile parameters is obtained. A set of simulated diffraction signals is generated using the set of values for the profile parameters, each simulated diffraction signal characterizing the behavior of light diffracted from the structure. The support vector machine is trained using the set of simulated diffraction signals as inputs to the support vector machine and the set of values for the profile parameters as expected outputs of the support vector machine. After the support vector machine has been trained, a fabrication process is performed using the fabrication tool to fabricate the structure on the wafer. A measured diffraction signal off the structure is obtained. The measured diffraction signal is inputted into the trained support vector machine.

Abstract: A system for examining a patterned structure formed on a semiconductor wafer using an optical metrology model includes a first fabrication cluster, a metrology cluster, an optical metrology model optimizer, and a real time profile estimator. The first fabrication cluster configured to process a wafer, the wafer having a first patterned and a first unpatterned structure. The first patterned structure has underlying film thicknesses, critical dimension, and profile. The metrology cluster including one or more optical metrology devices coupled to the first fabrication cluster. The metrology cluster is configured to measure diffraction signals off the first patterned and the first unpatterned structure. The metrology model optimizer is configured to optimize an optical metrology model of the first patterned structure using one or more measured diffraction signals off the first patterned structure and with floating profile parameters, material refraction parameters, and metrology device parameters.

Abstract: A system for examining a patterned structure formed on a semiconductor wafer using an optical metrology model includes a first fabrication cluster, a metrology cluster, an optical metrology model optimizer, and a real time profile estimator. The first fabrication cluster processes a wafer, the wafer having a first patterned and a first unpatterned structure. The metrology cluster measures diffraction signals off the first patterned and first unpatterned structure. The metrology model optimizer optimizes an optical metrology model of the first patterned structure. The real time profile estimator creates an output comprising underlying film thickness, critical dimension, and profile of the first patterned structure.

Abstract: Structures formed on a semiconductor wafer are consecutively measured by obtaining first and second measured diffraction signals of a first structure and a second structure formed abutting the first structure. The first and second measured diffraction signals were consecutively measured using a polarized reflectometer. The first measured diffraction signal is compared to a first simulated diffraction signal generated using a profile model of the first structure. The profile model has profile parameters that characterize geometries of the first structure. One or more features of the first structure are determined based on the comparison. The second measured diffraction signal is converted to a converted diffraction signal. The converted diffraction signal is compared to the first simulated diffraction signal or a second simulated diffraction signal generated using the same profile model as the first simulated diffraction signal.

Abstract: A profile model can be selected for use in examining a structure formed on a semiconductor wafer using optical metrology by obtaining an initial profile model having a set of profile parameters. A machine learning system is trained using the initial profile model. A simulated diffraction signal is generated for an optimized profile model using the trained machine learning system, where the optimized profile model has a set of profile parameters with the same or fewer profile parameters than the initial profile model. A determination is made as to whether the one or more termination criteria are met. If the one or more termination criteria are met, the optimized profile model is modified and another simulated diffraction signal is generated using the same trained machine learning system.

Abstract: A weighting function is obtained to enhance measured diffraction signals used in optical metrology. To obtain the weighting function, a measured diffraction signal is obtained. The measured diffraction signal was measured from a site on a wafer using a photometric device. A first weighting function is defined based on noise that exists in the measured diffraction signal. A second weighting function is defined based on accuracy of the measured diffraction signal. A third weighting function is defined based on sensitivity of the measured diffraction signal. A fourth weighting function is defined based on one or more of the first, second, and third weighting functions.

Abstract: A profile model to characterize a structure to be examined using optical metrology is evaluated by displaying a set of profile parameters that characterizes the profile model. Each profile parameter has a range of values for the profile parameter. For each profile parameter having a range of values, an adjustment tool is displayed for selecting a value for the profile parameter within the range of values. A measured diffraction signal, which was measured using an optical metrology tool, is displayed. A simulated diffraction signal, which was generated based on the values of the profile parameters selected using the adjustment tools for the profile parameters, is displayed. The simulated diffraction signal is overlaid with the measured diffraction signal.