Abstract:
An improved profileometry data collection and analysis system employing software that performs clustering analysis on library data stored in memory that represent semiconductor chip wafer profiles, for use in matching real-time data signals from data collected by profileometry instruments. To better perform a match in real-time between the incoming real-time data signals and the profile library data into clusters, and to extract representative cluster data points of the clusters. The representative of the clusters are stored in primary memory (e.g. RAM), while the data forming the clusters are stored in secondary memory (e.g., a hard drive). A real-time data signal is then first compared to the representative cluster data points, and when a match is made with a particular representative cluster data point, the cluster associated with the representative cluster data point is loaded from secondary memory into primary memory. Next a further search is made wiht the incoming real-time data signal to find the closest match to the data in the cluster.
Abstract:
To generate sets of coefficients for use in optical metrology of semiconductor structures, at least three optical metrology signals for a set of parameters are obtained. The optical metrology signals are indicative of light diffracted from a semiconductor structure, and a value of at least one parameter of the set of parameters is varied to produce each signal. Functional relationships between the at least three optical metrology signals are obtained, the functional relationships including at least three coefficient values. At least three sets of coefficients from the at least three coefficient values of the functional relationships are determined.
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:
The present invention includes a method and system (53) for determining the profile of a structure (59) in an integrated circuit from a measured signal, the signal measured off the structure with a metrology device (40), selecting a best match of the measured signal in a profile data space, the profile data space having data points with a specified extent of non-linearity, and performing a refinement procedure to determine refined profile parameters. One embodiment includes a refinement procedure comprising finding a polyhedron in a function domain of cost functions of the profile library signals and profile parameters and minimizing the total cost function using the weighted average method. Other embodiments include profile parameter refinement procedures using sensitivity analysis, a clustering approach, regression-based methods, localized fine-resolution refinement library method, iterative library refinement method, and other cost optimization or refinement algorithms, procedures, and methods. Refinement of profile parameters may be invoked automatically or invoked based on predetermined criteria such as exceeding an error metric between the measured signal versus the best match profile library.
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 generate sets of coefficients for use in optical metrology of semiconductor structures, at least three optical metrology signals for a set of parameters are obtained. The optical metrology signals are indicative of light diffracted from a semiconductor structure, and a value of at least one parameter of the set of parameters is varied to produce each signal. Functional relationships between the at least three optical metrology signals are obtained, the functional relationships including at least three coefficient values. At least three sets of coefficients from the at least three coefficient values of the functional relationships are determined.
Abstract:
A profile model for use in optical metrology of structures in a wafer is selected, the profile model having a set of geometric parameters associated with the dimensions of the structure. A set of optimization parameters is selected for the profile model using one or more input diffraction signals and one or more parameter selection criteria. The selected profile model and the set of optimization parameters are tested against one or more termination criteria. The process of selecting a profile model, selecting a set of optimization parameters, and testing the selected profile model and set of optimization parameters is performed until the one or more termination criteria are met.
Abstract:
Invention teaches a method and system for characterizing grating profile data. One embodiment is a method for characterizing grating profile data by clustering the grating profile data space and associating profile space data to each cluster. Another aspect of the present invention is a system for classifying grating profile comprising a cluster generator for generating clusters of grating profiles, for associating profile shape data to each cluster, and for linking the associated profile shape data to the grating profiles belonging to each cluster. The present invention includes a method and a system for evaluating input grating profile by comparing the profile data from the closest matching grating profile by comparing data from the closest matching grating profile library instance with a set of acceptable ranges of profile data for the application; flagging the input grating profile if the profile data is outside the set of acceptable ranges; and presenting the profile data and flags associated with the input grating profile. The system is scalable and may operate in a distributed manner utilizing networks.