Abstract: To evaluate the adequacy of a profile model, one or more types of process control to be used in controlling a fabrication process are selected. Profile model parameters and acceptable ranges for the profile model parameters are selected. A first and second metrology tools are selected. Statistical metric criteria that define an acceptable amount of variation in measurements obtained using the first and second tools are set. A profile model is selected. A measurement of the profile model parameters is obtained using the first tool and the selected profile model. A measurement of the one or more profile model parameters is obtained using the second tool. Statistical metric criteria are calculated based on the measurements of the one or more profile model parameters obtained using the first and second tools. The calculated and set statistical metric criteria are compared to evaluate the adequacy of the selected profile model.

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 that characterizes the change in intensity of light when reflected on the structure and a polarization parameter that characterizes the change in polarization states of light when reflected on the structure.

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: Edge roughness and deterministic profile of a structure formed on a semiconductor wafer are measured using optical metrology by directing an incident beam on the structure using a source and receiving the diffracted beam from the structure using a detector. The received diffracted beam is processed using a processor to determine a deterministic profile of the structure and to measure an edge roughness of the structure.

Abstract: The present invention includes a method and system for identifying an underlying structure that achieves improved planarization characteristics of layers while minimizing introduction of random and/or systematic noise to the reflected metrology signal. One embodiment of the present invention is a method of designing underlying structures in a wafer with pads of varying sizes and varying loading factors, and selecting the design of pads that yield a reflected metrology signal closest to the calibration metrology signal and that meet preset standard planarization characteristics. Another embodiment is a method of designing underlying structures with random shapes of varying sizes and varying loading factors. Still another embodiment is the use of periodic structures of varying line-to-space ratios in one or more underlying layers of a wafer, the periodicity of the underlying periodic structure being positioned at an angle relative to the direction of periodicity of the target periodic structure of the wafer.

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: A library of simulated-diffraction signals and hypothetical profiles of a periodic grating can be generated by generating diffraction calculations for a plurality of blocks of hypothetical layers. A diffraction calculation for a block of hypothetical layers characterizes, in part, the behavior of a diffraction beam in the block of hypothetical layers. Each block of hypothetical layers includes two or more hypothetical layers, and each hypothetical layer characterizes a layer within a hypothetical profile. The diffraction calculations for the blocks of hypothetical layers are stored prior to generating the library. The simulated-diffraction signals to be stored in the library are then generated based on the stored diffraction calculations for the blocks of hypothetical layers.

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: In semiconductor processing, the critical dimensions of structures formed on a wafer are controlled by first developing photoresist on top of a film layer on a wafer using a developer tool, the photoresist development being a function of developer tool process variables including temperature and length of time of development. After developing the photoresist, one or more etching steps are performed on the film layer on the wafer using an etch tool. After the one or more etching steps are performed, critical dimensions of structures at a plurality of locations on the wafer are measured using an optical metrology tool. After the critical dimensions are measured, one or more of the developer tool process variables are adjusted based on the critical dimensions of structures measured at the plurality of locations on the wafer.

Abstract: To evaluate the adequacy of a profile model, one or more types of process control to be used in controlling a fabrication process are selected. Profile model parameters and acceptable ranges for the profile model parameters are selected. A first and second metrology tools are selected. Statistical metric criteria that define an acceptable amount of variation in measurements obtained using the first and second tools are set. A profile model is selected. A measurement of the profile model parameters is obtained using the first tool and the selected profile model. A measurement of the one or more profile model parameters is obtained using the second tool. Statistical metric criteria are calculated based on the measurements of the one or more profile model parameters obtained using the first and second tools. The calculated and set statistical metric criteria are compared to evaluate the adequacy of the selected profile model.

Abstract: A simulated diffraction signal to be used in measuring shape roughness of a structure formed on a wafer using optical metrology is generated by defining an initial model of the structure. A statistical function of shape roughness is defined. A statistical perturbation is derived based on the statistical function and superimposed on the initial model of the structure to define a modified model of the structure. A simulated diffraction signal is generated based on the modified model of the structure.

Abstract: A method of generating a library of simulated-differentiation signals (simulated signals of a periodic grating includes obtaining a measured-differentiation signal (measured signal). Hypothetical parameters are associated with a hypothetical profile. The hypothetical parameters are varied within a range to generate a set of hypothetical profiles. The range to vary the hypothetical parameters is adjusted based on the measured signal. A set of simulated signals is generated from the set of hypothetical profiles.

Abstract: The present invention relates to a method and system for efficiently determining grating profiles using dynamic learning in a library generation process. The present invention also relates to a method and system for searching and matching trial grating profiles to determine shape, profile, and spectrum data information associated with an actual grating profile.

Abstract: A library of simulated-diffraction signals for an integrated circuit periodic grating is generated by generating sets of intermediate layer data. Each set of intermediate layer data corresponding to a separate one of a plurality of hypothetical layers of a hypothetical profile of the periodic grating. Each separate hypothetical layer has one of a plurality of possible combinations of hypothetical values of properties for that hypothetical layer. The generated sets of intermediate layer data are stored. Simulated-diffraction signals for each of a plurality of hypothetical profiles are generated based on the stored generated sets of intermediate layer data.

Abstract: The top-view profiles of repeating structures in a wafer are characterized and parameters to represent variations in the top-view profile of the repeating structures are selected. An optical metrology model is developed that includes the selected top-view profile parameters of the repeating structures. The optimized optical metrology model is used to generate simulated diffraction signals that are compared to measured diffraction signals.

Abstract: Overlay measurements for a semiconductor wafer are obtained by forming a periodic grating on the wafer having a first set of ridges and a second set of ridges. The first and second sets of ridges are formed on the wafer using a first mask and a second mask, respectively. After forming the first and second sets of gratings, zero-order cross polarization measurements of a portion of the periodic grating are obtained. Any overlay error between the first and second masks used to form the first and second sets of gratings is determined based on the obtained zero-order cross polarization measurements.

Abstract: A method of generating a library of simulated-diffraction signals (simulated signals) of a periodic grating includes obtaining a measured-diffraction signal (measured signal). Hypothetical parameters are associated with a hypothetical profile. The hypothetical parameters are varied within a range to generate a set of hypothetical profiles. The range to vary the hypothetical parameters is adjusted based on the measured signal. A set of simulated signals is generated from the set of hypothetical profiles.

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: The present invention relates to a method and system for efficiently determining grating profiles using dynamic learning in a library generation process. The present invention also relates to a method and system for searching and matching trial grating profiles to determine shape, profile, and spectrum data information associated with an actual grating profile.

Abstract: A library of simulated-diffraction signals for an integrated circuit periodic grating is generated by generating gets of intermediate layer data. Each set of intermediate layer data corresponding to a separate one of a plurality of hypothetical layers of a hypothetical profile of the periodic grating. Each separate hypothetical layer has one of a plurality of possible combinations of hypothetical values of properties for that hypothetical layer. The generated sets of intermediate layer data are stored. Simulated-diffraction signals for each of a plurality of hypothetical profiles are generated based on the stored generated sets of intermediate layer data.