Abstract: Transmittance of a photomask is determined using optical metrology. In particular, reflectance of a portion of the photomask is determined by directing an incident beam of light at the portion of the photomask. The reflectance is determined by measuring light diffracted from the portion of the photomask. One or more geometric features of the portion of the photomask are determined using the measured light diffracted from the portion of the photomask. A wave coupling is determined using the determined one or more geometric features of the portion of the photomask. The transmittance of the photomask is determined using the determined wave coupling and the determined reflectance of the portion of the photomask.

Abstract: An optical metrology model for the structure is obtained. The optical metrology model comprising one or more profile parameters, one or more process parameters, and a dispersion. A dispersion function that relates the dispersion to at least one of the one or more process parameters is obtained. A simulated diffraction signal is generated using the optical metrology model and a value for the at least one of the process parameters and a value for the dispersion. The value for the dispersion is calculated using the value for the at least one of the process parameter and the dispersion function. A measured diffraction signal of the structure is obtained using an optical metrology tool. The measured diffraction signal is compared to the simulated diffraction signal to determine one or more profile parameters of the structure. The fabrication tool is controlled based on the determined one or more profile parameters of the structure.

Abstract: Provided is a method of controlling a fabrication cluster using a machine learning system, the machine learning system trained developed using an optical metrology model. A simulated approximation diffraction signal is generated based on an approximation diffraction model of the structure. A set of difference diffraction signal is obtained by subtracting the simulated approximation diffraction signal from each of simulated fine diffraction signals and paired with the corresponding profile parameters. A first machine learning system is trained using the pairs of difference diffraction signal and corresponding profile parameters. A library of simulated fine diffraction signals and profile parameters is generated using the trained first machine learning system and using ranges and corresponding resolutions of the profile parameters. A measured diffraction signal is input into the trained second machine learning system to determine at least one profile parameter.

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: 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: 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: The invention can provide apparatus and methods for processing wafers using Noise-Reduction (N-R) metrology models that can be used in Double-Patterning (D-P) processing sequences, Double-Exposure (D-E) processing sequences, or other processing sequences.

Abstract: In determining position accuracy of double exposure lithography using optical metrology, a mask is exposed to form a first set of repeating patterns on a wafer, where the repeating patterns of the first set have a first pitch. The mask is then exposed again to form a second set of repeating patterns on the wafer. The repeating patterns of the second set of repeating patterns interleave with the repeating patterns of the first set of repeating patterns. The wafer is then developed to form a first set of repeating structures from the first set of repeating patterns and a second set of repeating structures from the second set of repeating patterns. A first diffraction signal is measured of a first repeating structure from the first set of repeating structures and a second repeating structure from the second set of repeating structures, where the first repeating structure is adjacent to the second repeating structure.

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: The profile of a structure having a region with a spatially varying property is modeled using an optical metrology model. A set of profile parameters is defined for the optical metrology model to characterize the profile of the structure. A set of layers is defined for a portion the optical metrology model that corresponds to the region of the structure with the spatially varying property, each layer having a defined height and width. For each layer, a mathematic function that varies across at least a portion of the width of the layer is defined to characterize the spatially varying property within a corresponding layer in the region of the structure.

Abstract: A structure formed on a semiconductor wafer can be examined 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. A measured diffraction signal off the structure is obtained. The measured diffraction signal is inputted into the trained support vector machine. Values of profile parameters of the structure are obtained as an output from the trained support vector machine.

Abstract: An optical metrology model is created for a structure formed on a semiconductor wafer. The optical metrology model comprises one or more profile parameters, one or more process parameters, and dispersion. A dispersion function is obtained that relates the dispersion to at least one of the one or more process parameters. A simulated diffraction signal is generated using the optical metrology model and a value for the at least one of the process parameters and a value for the dispersion. The value for the dispersion is calculated using the value for the at least one of the process parameter and the dispersion function. A measured diffraction signal of the structure is obtained. The measured diffraction signal is compared to the simulated diffraction signal. One or more profile parameters of the structure and one or more process parameters are determined based on the comparison of the measured diffraction signal to the simulated diffraction signal.

Abstract: An optical metrology model for the structure is obtained. The optical metrology model comprising one or more profile parameters, one or more process parameters, and a dispersion. A dispersion function that relates the dispersion to at least one of the one or more process parameters is obtained. A simulated diffraction signal is generated using the optical metrology model and a value for the at least one of the process parameters and a value for the dispersion. The value for the dispersion is calculated using the value for the at least one of the process parameter and the dispersion function. A measured diffraction signal of the structure is obtained using an optical metrology tool. The measured diffraction signal is compared to the simulated diffraction signal to determine one or more profile parameters of the structure. The fabrication tool is controlled based on the determined one or more profile parameters of the structure.

Abstract: A first wafer is fabricated using a first value for a process parameter specifying a process condition in fabricating the structure. A first value of a dispersion is measured from the first wafer. A second wafer is fabricated using a second value for the process parameter. A second value of the dispersion is measured from the second wafer. A third wafer is fabricated using a third value for the process parameter. The first, second, and third values for the process parameter are different from each other. A third value of the dispersion is measured from the third wafer. A dispersion function is defined to relate the process parameter to the dispersion using the first, second, and third values for the process parameter and the measured first, second, and third values of the dispersion. The simulated diffraction signal is generated using the defined dispersion function. The simulated diffraction signal is stored.

Abstract: Provided is a method for determining one or more profile parameters of a structure using an optical metrology model, the optical metrology model comprising a profile model, an approximation diffraction model, and a fine diffraction model. A simulated approximation diffraction signal is generated based on an approximation diffraction model of the structure. A set of difference diffraction signals is obtained by subtracting the simulated approximation diffraction signal from each of simulated fine diffraction signals and paired with the corresponding profile parameters and used to generate a library of difference diffraction signals. A measured diffraction signal adjusted by the simulated approximation diffraction signal is matched against the library to determine at least one profile parameter of the structure.

Abstract: Provided is a method of controlling a fabrication cluster using a machine learning system, the machine learning system trained developed using an optical metrology model, the optical metrology model comprising a profile model, an approximation diffraction model, and a fine diffraction model. A simulated approximation diffraction signal is generated based on an approximation diffraction model of the structure. A set of difference diffraction signal is obtained by subtracting the simulated approximation diffraction signal from each of simulated fine diffraction signals and paired with the corresponding profile parameters. A first machine learning system is trained using the pairs of difference diffraction signal and corresponding profile parameters. A library of simulated fine diffraction signals and profile parameters is generated using the trained first machine learning system and using ranges and corresponding resolutions of the profile parameters.

Abstract: Provided is a method for determining one or more profile parameters of a structure using an optical metrology model, the optical metrology model comprising a profile model, an approximation diffraction model, and a fine diffraction model. A simulated approximation diffraction signal is generated based on an approximation diffraction model of the structure. A set of difference diffraction signals is obtained by subtracting the simulated approximation diffraction signal from each of simulated fine diffraction signals and paired with the corresponding profile parameters. A machine learning system is trained using the pairs of difference diffraction signal and corresponding profile parameters. A measured diffraction signal adjusted by the simulated approximation diffraction signal is input into the trained machine learning system and generates the corresponding profile parameters.

Abstract: A structure formed on a semiconductor wafer can be examined using a support vector machine. A profile model is defined by profile parameters that characterize the geometric shape of the structure. A training set of values for the profile parameters is obtained. A training set of simulated diffraction signals is generated using the training set of values for the profile parameters. The support vector machine is trained using the training set of values for the profile parameters. A simulated diffraction signal is generated using a set of values for the profile parameters as inputs to the trained support vector machine. A measured diffraction signal is compared to the simulated diffraction signal. When the signals match within one or more matching criteria, values of profile parameters of the structure are determined to be the set of values for the profile parameters used to generate the simulated diffraction signal.

Abstract: Provided is a method of controlling a photolithography cluster or a subsequent fabrication cluster using optical metrology to determine profile parameters of a photomask structure covered with a pellicle. An optical metrology model of the pellicle is developed and integrated with the optical metrology model of the photomask structure. The optical metrology model of the photomask taking into account the optical effects on the illumination and detection beams transmitted through the pellicle and diffracted by the photomask structure. One or more profile parameters of the photomask structure is determined and used to adjust one or more process parameters or equipment settings of a photolithography cluster using the photomask or a subsequent fabrication cluster.

Abstract: To determine one or more features of an in-die structure on a semiconductor wafer, a correlation is determined between one or more features of a test structure to be formed on a test pad and one or more features of a corresponding in-die structure. A measured diffraction signal measured off the test structure is obtained. One or more features of the test structure are determined using the measured diffraction signal. The one or more features of the in-die structure are determined based on the one or more determined features of the test structure and the determined correlation.