Abstract: Described is a method for determining an endpoint of an etch process using optical emission spectroscopy (OES) data as an input. OES data is acquired by a spectrometer in a plasma etch processing chamber. The acquired time-evolving spectral data is first filtered and de-meaned, and thereafter transformed into transformed spectral data, or trends, using multivariate analysis such as principal components analysis, in which previously calculated principal component weights are used to accomplish the transform. Grouping of the principal components weights into two separate groups corresponding to positive and negative natural wavelengths, creates separate signed trends (synthetic wavelengths).

Abstract: Described is a method for determining an endpoint of an etch process using optical emission spectroscopy (OES) data as an input. OES data is acquired by a spectrometer in a plasma etch processing chamber. The acquired time-evolving spectral data is first filtered and de-meaned, and thereafter transformed into transformed spectral data, or trends, using multivariate analysis such as principal components analysis, in which previously calculated principal component weights are used to accomplish the transform. Grouping of the principal components weights into two separate groups corresponding to positive and negative natural wavelengths, creates separate signed trends (synthetic wavelengths).

Abstract: An advanced optical sensor and method for detection of optical events in a plasma processing system. The method includes detecting at least one light emission signal in a plasma processing chamber. The at least one detected light emission signal including light emissions from an optical event. The method further includes processing the at least one light emission signal and detecting a signature of the optical event from the processed light emission signal.

Abstract: An apparatus, system, and method for in-situ etching monitoring in a plasma processing chamber. The apparatus includes a continuous wave broadband light source; an illumination system configured to illuminate an area on a substrate with an incident light beam having a fixed polarization direction, the incident light beam from the broadband light source being modulated by a shutter; a collection system configured to collect a reflected light beam being reflected from the illuminated area on the substrate, and direct the reflected light beam to a detector; and processing circuitry. The processing circuitry is configured to process the reflected light beam to suppress background light, determine a property value from the processed light, and control an etch process based on the determined property value.

Abstract: Described is a method for determining an endpoint of an etch process using optical emission spectroscopy (OES) data as an input. Optical emission spectroscopy (OES) data are acquired by a spectrometer attached to a plasma etch processing tool. The acquired time-evolving spectral data are first filtered and demeaned, and thereafter transformed into transformed spectral data, or trends, using multivariate analysis such as principal components analysis, in which previously calculated principal component weights are used to accomplish the transform. A functional form incorporating multiple trends may be used to more precisely determine the endpoint of an etch process. A method for calculating principal component weights prior to actual etching, based on OES data collected from previous etch processing, is disclosed, which method facilitates rapid calculation of trends and functional forms involving multiple trends, for efficient and accurate in-line determination of etch process endpoint.

Abstract: An advanced optical sensor and method for detection of optical events in a plasma processing system. The method includes detecting at least one light emission signal in a plasma processing chamber. The at least one detected light emission signal including light emissions from an optical event. The method further includes processing the at least one light emission signal and detecting a signature of the optical event from the processed light emission signal.

Abstract: 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.

Abstract: Described is a method for determining an endpoint of an etch process using optical emission spectroscopy (OES) data as an input. Optical emission spectroscopy (OES) data are acquired by a spectrometer attached to a plasma etch processing tool. The acquired time-evolving spectral data are first filtered and demeaned, and thereafter transformed into transformed spectral data, or trends, using multivariate analysis such as principal components analysis, in which previously calculated principal component weights are used to accomplish the transform. A functional form incorporating multiple trends may be used to more precisely determine the endpoint of an etch process. A method for calculating principal component weights prior to actual etching, based on OES data collected from previous etch processing, is disclosed, which method facilitates rapid calculation of trends and functional forms involving multiple trends, for efficient and accurate in-line determination of etch process endpoint.

Abstract: Disclosed is a method for determining an endpoint of an etch process using optical emission spectroscopy (OES) data as an input. Optical emission spectroscopy (OES) data are acquired by a spectrometer attached to a plasma etch processing tool. The acquired time-evolving spectral data are first filtered and demeaned, and thereafter transformed into transformed spectral data, or trends, using multivariate analysis such as principal components analysis, in which previously calculated principal component weights are used to accomplish the transform. A functional form incorporating multiple trends may be used to more precisely determine the endpoint of an etch process. A method for calculating principal component weights prior to actual etching, based on OES data collected from previous etch processing, is disclosed, which method facilitates rapid calculation of trends and functional forms involving multiple trends, for efficient and accurate in-line determination of etch process endpoint.

Abstract: Disclosed is a method for determining an endpoint of an etch process using optical emission spectroscopy (OES) data as an input. Optical emission spectroscopy (OES) data are acquired by a spectrometer attached to a plasma etch processing tool. The acquired time-evolving spectral data are first filtered and demeaned, and thereafter transformed into transformed spectral data, or trends, using multivariate analysis such as principal components analysis, in which previously calculated principal component weights are used to accomplish the transform. A functional form incorporating multiple trends may be used to more precisely determine the endpoint of an etch process. A method for calculating principal component weights prior to actual etching, based on OES data collected from previous etch processing, is disclosed, which method facilitates rapid calculation of trends and functional forms involving multiple trends, for efficient and accurate in-line determination of etch process endpoint.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Metrology data from a semiconductor treatment system is transformed using multivariate analysis. In particular, a set of metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. One or more essential variables for the obtained set of metrology data is determined using multivariate analysis. A first metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. The first obtained metrology data is not one of the metrology data in the set of metrology data earlier obtained. The first metrology data is transformed into a second metrology data using the one or more of the determined essential variables.

Abstract: 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.

Abstract: Metrology data from a semiconductor treatment system is transformed using multivariate analysis. In particular, a set of metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. One or more essential variables for the obtained set of metrology data is determined using multivariate analysis. A first metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. The first obtained metrology data is not one of the metrology data in the set of metrology data earlier obtained. The first metrology data is transformed into a second metrology data using the one or more of the determined essential variables.

Abstract: Metrology data from a semiconductor treatment system is transformed using multivariate analysis. In particular, a set of metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. One or more essential variables for the obtained set of metrology data is determined using multivariate analysis. A first metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. The first obtained metrology data is not one of the metrology data in the set of metrology data earlier obtained. The first metrology data is transformed into a second metrology data using the one or more of the determined essential variables.

Abstract: 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.

Abstract: To manage data flow in generating profile models for use in optical metrology, a project data object is created. A first profile model data object is created. The first profile model data object corresponds to a first profile model defined using profile parameters. A version number is associated with the first profile model data object. The first profile model data object is linked with the project data object. At least a second profile model data object is created. The second profile model data object corresponds to a second profile model defined using profile parameters. The first and second profile models are different. Another version number is associated with the second profile model data object. The second profile model data object is linked with the project data object. The project data object, the first profile model data object, and the second profile model data object are stored.

Abstract: 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.