Abstract: Provided is a method of determining one or more profile parameters of a photomask covered with a pellicle, the method comprising developing an optical metrology model of a pellicle covering a photomask, developing an optical metrology model of the photomask, the photomask separated from the pellicle by a medium and having a structure, the structure having profile parameters, the optical metrology model of the photomask taking into account the optical effects on the illumination beam transmitted through the pellicle and diffracted by the photomask structure. The optical metrology model of the pellicle and the optical metrology model of the photomask structure are integrated and optimized. At least one profile parameters of the photomask structure is determined using the optimized integrated optical metrology model.

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: A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured from the structure using an optical metrology device. A first profile is obtained from a first machine learning system using the first diffraction signal obtained as an input to the first machine learning system. The first machine learning system is configured to generate a profile as an output for a diffraction signal received as an input. A second profile is obtained from a second machine learning system using the first profile obtained from the first machine learning system as an input to the second machine learning system. The second machine learning system is configured to generate a diffraction signal as an output for a profile received as an input. The first and second profiles include one or more parameters that characterize one or more features of the structure.

Abstract: An optical metrology system includes a photometric device with a source configured to generate and direct light onto a structure, and a detector configured to detect light diffracted from the structure and to convert the detected light into a measured diffraction signal. A processing module of the optical metrology system is configured to receive the measured diffraction signal from the detector to analyze the structure. The optical metrology system also includes a generic interface disposed between the photometric device and the processing module. The generic interface is configured to provide the measured diffraction signal to the processing module using a standard set of signal parameters. The standard set of signal parameters includes a reflectance parameter, a first polarization parameter, a second polarization parameter, and a third polarization parameter.

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: 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 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, each simulated diffraction signal characterizing the behavior of light diffracted from the structure. The support vector machine is trained using the training set of values for the profile parameters as inputs to the support vector machine and the training set of simulated diffraction signals as expected outputs of the support vector machine. A measured diffraction signal off the structure is obtained. A simulated diffraction signal is generated using a set of values for the profile parameters as inputs to the trained support vector machine.

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 fabrication cluster can be controlled using optical metrology. A fabrication process is performed on a wafer using a fabrication cluster. A photonic nanojet, an optical intensity pattern induced at a shadow-side surface of a dielectric microsphere, is generated. An inspection area on the wafer is scanned with the photonic nanojet. A measurement is obtained of the retroreflected light from the dielectric microsphere as the photonic nanojet scans the inspection area. The existence of a structure in the inspection area is determined with the obtained measurement of the retroreflected light. One or more process parameters of the fabrication cluster is adjusted based on the determination of the existence of the structure in the inspection area.

Abstract: The number of diffraction orders to use in generating simulated diffraction signals for a two-dimensional structure in optical metrology is selected by generating a first simulated diffraction signal using a first number of diffraction orders and a hypothetical profile of the two-dimensional structure. A second simulated diffraction signal is generated using a second number of diffraction orders using the same hypothetical profile used to generate the first simulated diffraction signal, where the first and second numbers of diffraction orders are different. The first and second simulated diffraction signals are compared. Based on the comparison of the first and second simulated diffraction signals, a determination is made as to whether to select the first or second number of diffraction orders.

Abstract: A structure formed on a semiconductor wafer is examined by obtaining measurements of cross polarization components of diffraction beams, which were obtained from scanning an incident beam over a range of azimuth angles to obtain an azimuthal scan. A zero azimuth position is determined based on the azimuthal scan. The cross polarization components are zero at the zero azimuth position. A measured diffraction signal is obtained using an azimuth angle to be used in optical metrology of the structure. Misalignment of the azimuth angle is detected using the measured diffraction signal and the determined zero azimuth position.

Abstract: An optical metrology model for a repetitive structure is optimized by selecting one or more profile parameters using one or more selection criteria. One or more termination criteria are set, the one or more termination criteria comprising measures of stability of the optical metrology model. The profile shape features of the repetitive structure are characterized using the one or more selected profile parameters. The optical metrology model is optimized using a set of values for the one or more selected profile parameters. One or more profile parameters of the profile of the repetitive structure are determined using the optimized optical metrology model and one or more measured diffraction signals. Values of the one or more termination criteria are calculated using the one or more determined profile parameters.

Abstract: An inspection area on a semiconductor wafer can be examined using a photonic nanojet. The photonic nanojet, an optical intensity pattern induced at a shadow-side surface of a dielectric microsphere, is generated. The inspection area is scanned with the photonic nanojet. A measurement is obtained of the retroreflected light from the dielectric microsphere as the photonic nanojet scans the inspection area. The existence of a structure in the inspection area is determined with the obtained measurement of the retroreflected light.

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: 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: An optical metrology model for a repetitive structure is optimized by selecting one or more profile parameters using one or more selection criteria. One or more termination criteria are set, the one or more termination criteria comprising measures of stability of the optical metrology model. The profile shape features of the repetitive structure are characterized using the one or more selected profile parameters. The optical metrology model is optimized using a set of values for the one or more selected profile parameters. One or more profile parameters of the profile of the repetitive structure are determined using the optimized optical metrology model and one or more measured diffraction signals. Values of the one or more termination criteria are calculated using the one or more determined profile parameters.

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 system includes a photometric device with a source configured to generate and direct light onto a structure, and a detector configured to detect light diffracted from the structure and to convert the detected light into a measured diffraction signal. A processing module of the optical metrology system is configured to receive the measured diffraction signal from the detector to analyze the structure. The optical metrology system also includes a generic interface disposed between the photometric device and the processing module. The generic interface is configured to provide the measured diffraction signal to the processing module using a standard set of signal parameters. The standard set of signal parameters includes a reflectance parameter, a first polarization parameter, a second polarization parameter, and a third polarization parameter.

Abstract: A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured from the structure using an optical metrology device. A first profile is obtained from a first machine learning system using the first diffraction signal obtained as an input to the first machine learning system. The first machine learning system is configured to generate a profile as an output for a diffraction signal received as an input. A second profile is obtained from a second machine learning system using the first profile obtained from the first machine learning system as an input to the second machine learning system. The second machine learning system is configured to generate a diffraction signal as an output for a profile received as an input. The first and second profiles include one or more parameters that characterize one or more features of the structure.

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: In generating a profile model to characterize a structure to be examined using optical metrology, a view canvas is displayed, with the profile model being generated displayed in the view canvas. A profile shape palette is displayed adjacent to the view canvas. A plurality of different profile shape primitives is displayed in the profile shape palette. Each profile shape primitive in the profile shape palette is defined by a set of profile parameters. When a user selects a profile shape primitive from the profile shape palette, drags the selected profile shape primitive from the profile shape palette, and drops the selected profile shape primitive into the view canvas, the selected profile shape primitive is incorporated into the profile model being generated and displayed in the view canvas.