摘要:
Embodiments are generally directed to neural network training for library-based critical dimension metrology. An embodiment of a method includes optimizing a threshold for a principal component analysis of a spectrum data set to provide a principal component value, estimating a training target for one or more neural networks, training the one or more neural networks based both on the training target and on the principal component value provided from optimizing the threshold for the principal component analysis, and providing a spectral library based on the one or more trained neural networks.
摘要:
Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described. In an example, a method includes optimizing a threshold for a principal component analysis (PCA) of a spectrum data set to provide a principal component (PC) value, estimating a training target for one or more neural networks, training the one or more neural networks based both on the training target and on the PC value provided from optimizing the threshold for the PCA, and providing a spectral library based on the one or more trained neural networks.
摘要:
Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described.
摘要:
Automated determination of a number of profiles for a training data set to be used in training a machine learning system for generating target function information from modeled profile parameters. In one embodiment, a first principal component analysis (PCA) is performed on a training data set, and a second PCA is performed on a combined data set which includes the training data set and a test data set. A test data set estimate is generated based on the first PCA transform and the second PCA matrix. The size of error between the test data set and the test data set estimate is used to determine whether a number of profiles associated with the training data set is sufficiently large for training a machine learning system to generate a library of spectral information.
摘要:
To manage data flow in generating different signal formats for use in optical metrology, a project data object is created. A first option data object is created. The first option data object has a set of signal parameters. Different settings of the set of signal parameters correspond to different signal formats for diffraction signals. A version number is associated with the first option data object. The first option data object is linked with the project data object. At least a second option data object is created. The second option data object has a set of signal parameters. Different settings of the set of signal parameters correspond to different signal formats for diffraction signals. The set of signal parameters of the first option data object and the set of signal parameters of the second option data object are set differently. Another version number is associated with the second option data object. The second option data object is linked with the project data object. The project data object, the first option data object, and the second option data object are stored. The version numbers associated with the first option data object and the second option data object are stored. The link between the first option data object and the project data object is stored. The link between the second option data object and the project data object is stored.
摘要:
In allocating processing units of a computer system to generate simulated diffraction signals used in optical metrology, a request for a job to generate simulated diffraction signals using multiple processing units is obtained. A number of processing units requested for the job to generate simulated diffraction signals is then determined. A number of available processing units is determined. When the number of processing units requested is greater than the number of available processing units, a number of processing units is assigned to generate the simulated diffraction signals that is less than the number of processing units requested.
摘要:
A system for examining a patterned structure formed on a semiconductor wafer using an optical metrology model includes a first fabrication cluster, a metrology cluster, an optical metrology model optimizer, and a real time profile estimator. The first fabrication cluster configured to process a wafer, the wafer having a first patterned and a first unpatterned structure. The first patterned structure has underlying film thicknesses, critical dimension, and profile. The metrology cluster including one or more optical metrology devices coupled to the first fabrication cluster. The metrology cluster is configured to measure diffraction signals off the first patterned and the first unpatterned structure. The metrology model optimizer is configured to optimize an optical metrology model of the first patterned structure using one or more measured diffraction signals off the first patterned structure and with floating profile parameters, material refraction parameters, and metrology device parameters.
摘要:
A hypothetical profile is used to model the profile of a structure formed on a semiconductor wafer to use in determining the profile of the structure using optical metrology.To select a hypothetical profile, sample diffraction signals are obtained from measured diffraction signals of structures formed on the wafer, where the sample diffraction signals are a representative sampling of the measured diffraction signals. A hypothetical profile is defined and evaluated using a sample diffraction signal from the obtained sample diffraction signals.
摘要:
To manage data flow in generating different signal formats for use in optical metrology, a project data object is created. A first option data object is created. The first option data object has a set of signal parameters. Different settings of the set of signal parameters correspond to different signal formats for diffraction signals. A version number is associated with the first option data object. The first option data object is linked with the project data object. At least a second option data object is created. The second option data object has a set of signal parameters. Different settings of the set of signal parameters correspond to different signal formats for diffraction signals. The set of signal parameters of the first option data object and the set of signal parameters of the second option data object are set differently. Another version number is associated with the second option data object. The second option data object is linked with the project data object. The project data object, the first option data object, and the second option data object are stored. The version numbers associated with the first option data object and the second option data object are stored. The link between the first option data object and the project data object is stored. The link between the second option data object and the project data object is stored.
摘要:
To examine a patterned structure formed on a semiconductor wafer using an optical metrology model, an optical metrology model is created for the patterned structure. The optical metrology model has profile parameters, material refraction parameters, and metrology device parameters. Ranges of values for the profile parameters, material refraction parameters, and metrology device parameters are defined. One or more measured diffraction signals of the patterned structure are obtained. The optical metrology model is optimized to obtain an optimized optical metrology model using the defined ranges of values defined and the one or more obtained measured diffraction signals of the patterned structure. For at least one parameter from amongst the material refraction parameters and the metrology device parameters, the at least one parameter is set to a fixed value within the range of values for the at least one parameter. At least one profile parameter of the patterned structure is determined using the optimized optical metrology model and the fixed value for the at least one parameter.