Abstract: Complex structures, such as gate-all-around (GAA) field effect transistor or high-aspect ratio (HAR) Channel hole etch, etc., in semiconductor devices are measured using a combination of physical modeling and machine learning modeling. Metrology signals collected at different manufacturing process steps, e.g., pre-process step and post-process step of the structure of interest (SOI) may be used. The measurement signals acquired at the pre-process steps are used to determine a first parameter of the SOT, e.g., using physical modeling and machine learning, which may be fed forward and used to generate a physical model of the SOI at the post-process step. A second parameter of the SOI at the post-process step is determined using physical modeling and machine learning and may be fed back and used to generate the physical model of the SOI at the post-process step with post process signals and used to determine other parameters.

Abstract: Systems and methods for enhancing inspection sensitivity to detect defects in wafers using an inspection tool are disclosed. A plurality of light emitting diodes illuminate at least a portion of a wafer and capture a set of grayscale images. A residual signal is determined in each image of the grayscale image set and the residual signal is subtracted from each image of the grayscale image set. Defects are identified based on the subtracted grayscale image set. Models of the inspection tool and wafer may be built and refined in some embodiments of the disclosed systems and methods.

Abstract: A system determines a value, such as a thickness, surface roughness, material concentration, and/or critical dimension, of a layer on a wafer based on normalized signals and reflected total intensities. A light source directs a beam at a surface of the wafer. A sensor receives the reflected beam and provides at least a pair of polarization channels. The signals from the polarization channels are received by a controller, which normalizes a difference between a pair of the signals to generate the normalized result. The value of the wafer is determined through analyzing the signal with a modeling of the system.

Abstract: Disclosed are methods and apparatus for inspecting and processing semiconductor wafers. The system includes an edge detection system for receiving each wafer that is to undergo a photolithography process. The edge detection system comprises an illumination channel for directing one or more illumination beams towards a side, top, and bottom edge portion that are within a border region of the wafer. The edge detection system also includes a collection module for collecting and sensing output radiation that is scattered or reflected from the edge portion of the wafer and an analyzer module for locating defects in the edge portion and determining whether each wafer is within specification based on the sensed output radiation for such wafer. The photolithography system is configured for receiving from the edge detection system each wafer that has been found to be within specification. The edge detection system is coupled in-line with the photolithography system.

Abstract: Systems and methods for enhancing inspection sensitivity to detect defects in wafers using an inspection tool are disclosed. A plurality of light emitting diodes illuminate at least a portion of a wafer and capture a set of grayscale images. A residual signal is determined in each image of the grayscale image set and the residual signal is subtracted from each image of the grayscale image set. Defects are identified based on the subtracted grayscale image set. Models of the inspection tool and wafer may be built and refined in some embodiments of the disclosed systems and methods.

Abstract: A system determines a value, such as a thickness, surface roughness, material concentration, and/or critical dimension, of a layer on a wafer based on normalized signals and reflected total intensities. A light source directs a beam at a surface of the wafer. A sensor receives the reflected beam and provides at least a pair of polarization channels. The signals from the polarization channels are received by a controller, which normalizes a difference between a pair of the signals to generate the normalized result. The value of the wafer is determined through analyzing the signal with a modeling of the system.

Abstract: Disclosed are methods and apparatus for inspecting and processing semiconductor wafers. The system includes an edge detection system for receiving each wafer that is to undergo a photolithography process. The edge detection system comprises an illumination channel for directing one or more illumination beams towards a side, top, and bottom edge portion that are within a border region of the wafer. The edge detection system also includes a collection module for collecting and sensing output radiation that is scattered or reflected from the edge portion of the wafer and an analyzer module for locating defects in the edge portion and determining whether each wafer is within specification based on the sensed output radiation for such wafer. The photolithography system is configured for receiving from the edge detection system each wafer that has been found to be within specification. The edge detection system is coupled in-line with the photolithography system.

Abstract: An optical metrology includes a library, a metrology tool and a library evolution tool. The library is generated to include a series of predicted measurements. Each predicted measurement is intended to match the measurements that a metrology device would record when analyzing a corresponding physical structure. The metrology tool compares its empirical measurements to the predicted measurements in the library. If a match is found, the metrology tool extracts a description of the corresponding physical structure from the library. The library evolution tool operates to improve the efficiency of the library. To make these improvements, the library evolution tool statistically analyzes the usage pattern of the library. Based on this analysis, the library evolution tool increases the resolution of commonly used portions of the library. The library evolution tool may also optionally reduce the resolution of less used portions of the library.

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 method for modeling samples includes the use of control points to define lines profiles and other geometric shapes. Each control point used within a model influences a shape within the model. Typically, the control points are used in a connect-the-dots fashion where a set of dots defines the outline or profile of a shape. The layers within the sample are typically modeled independently of the shape defined using the control points. The overall result is to minimize the number of parameters used to model shapes while maintaining the accuracy of the resulting scatterometry models.

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 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.

Abstract: Measurement data sets for optical metrology systems can be processed in parallel using Multiple Tool and Structure Analysis (MTSA). In an MTSA procedure, at least one parameter that is common to the data sets can be coupled as a global parameter. Setting this parameter as global allows a regression on each data set to contain fewer fitting parameters, making the process is less complex, requiring less processing capacity, and providing more accurate results. MTSA can analyze multiple structures measured on a single tool, or a single structure measured on separate tools. For a multiple tool recipe, a minimized regression solution can be applied back to each tool to determine whether the recipe is optimized. If the recipe does not provide accurate results for each tool, search parameters and/or spaces can be modified in an iterative manner until an optimized solution is obtained that provides acceptable solutions on each tool.

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: 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 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.

Abstract: A method for modeling samples includes the use of control points to define lines profiles and other geometric shapes. Each control point used within a model influences a shape within the model. Typically, the control points are used in a connect-the-dots fashion where a set of dots defines the outline or profile of a shape. The layers within the sample are typically modeled independently of the shape defined using the control points. The overall result is to minimize the number of parameters used to model shapes while maintaining the accuracy of the resulting scatterometry models.

Abstract: A method for modeling samples includes the use of control points to define lines profiles and other geometric shapes. Each control point used within a model influences a shape within the model. Typically, the control points are used in a connect-the-dots fashion where a set of dots defines the outline or profile of a shape. The layers within the sample are typically modeled independently of the shape defined using the control points. The overall result is to minimize the number of parameters used to model shapes while maintaining the accuracy of the resulting scatterometry models.

Abstract: Feed forward techniques can be used to improve optical metrology measurements for microelectronic devices. Metrology tools can be used to measure parameters such as critical dimension, profile, index of refraction, and thickness, as well as various material properties. Three-dimensional feature characterizations can be performed, from which parameters can be extracted and correlations executed. Process fingerprints on a wafer can be tracked after each process step, such that correlation between profile and structure parameters can be established and deviations from specification can be detected instantaneously. A “feed forward” approach allows information relating to dimensions, profiles, and layer thicknesses to be passed on to subsequent process steps. By retaining information from previous process steps, calculations such as profile determinations can be simplified by reducing the number of variables and degrees of freedom used in the calculation.

Abstract: Measurement data sets for optical metrology systems can be processed in parallel using Multiple Tool and Structure Analysis (MTSA). In an MTSA procedure, at least one parameter that is common to the data sets can be coupled as a global parameter. Setting this parameter as global allows a regression on each data set to contain fewer fitting parameters, making the process is less complex, requiring less processing capacity, and providing more accurate results. MTSA can analyze multiple structures measured on a single tool, or a single structure measured on separate tools. For a multiple tool recipe, a minimized regression solution can be applied back to each tool to determine whether the recipe is optimized. If the recipe does not provide accurate results for each tool, search parameters and/or spaces can be modified in an iterative manner until an optimized solution is obtained that provides acceptable solutions on each tool.