Abstract: Embodiments discussed herein refer to LiDAR systems to focus on one or more regions of interests within a field of view.

Abstract: Embodiments discussed herein refer to LiDAR systems to focus on one or more regions of interests within a field of view.

Abstract: Embodiments discussed herein refer to LiDAR systems to focus on one or more regions of interests within a field of view.

Abstract: Embodiments discussed herein refer to LiDAR systems to focus on one or more regions of interests within a field of view.

Abstract: Embodiments discussed herein refer to LiDAR systems to focus on one or more regions of interests within a field of view.

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 discussed herein refer to LiDAR systems to focus on one or more regions of interests within a field of view.

Abstract: Described herein are architectures, platforms and methods for determining endpoints of an optical emission spectroscopy (OES) data acquired from a plasma processing system. The OES data, for example, includes an absorption—step process, a desorption—step process, or a combination thereof. In this example, the OES data undergoes signal synchronization and transient signal filtering prior to endpoint determination, which may be implemented through an application of a moving average filter.

Abstract: Described herein are architectures, platforms and methods for determining endpoints of an optical emission spectroscopy (OES) data acquired from a plasma processing system. The OES data, for example, includes an absorption—step process, a desorption—step process, or a combination thereof. In this example, the OES data undergoes signal synchronization and transient signal filtering prior to endpoint determination, which may be implemented through an application of a moving average filter.

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: Provided is an automated determination of an optimized parameterization of a scatterometry model for analysis of a sample diffracting structure having unknown parameters. A preprocessor determines from a plurality of floating model parameters, a reduced set of model parameters which can be reasonably floated in the scatterometry model based on a relative precision for each parameter determined from the Jacobian of measured spectral information with respect to each parameter. The relative precision for each parameter is determined in a manner which accounts for correlation between the parameters for a combination.

Abstract: Provided is an automated determination of an optimized parameterization of a scatterometry model for analysis of a sample diffracting structure having unknown parameters. A preprocessor determines from a plurality of floating model parameters, a reduced set of model parameters which can be reasonably floated in the scatterometry model based on a relative precision for each parameter determined from the Jacobian of measured spectral information with respect to each parameter. The relative precision for each parameter is determined in a manner which accounts for correlation between the parameters for a combination.

Abstract: To evaluate the adequacy of a profile model, an initial profile model is selected. The profile model includes profile model parameters to be measured in implementing types of process control to be used in controlling a fabrication process. A measurement of profile model parameters is obtained using a first metrology tool and the profile model. A measurement of the profile model parameters is obtained using a second metrology tool and the profile model. Statistical metric criteria are calculated based on the measurements of the profile model parameters obtained using the first and second metrology tools. When the calculated statistical metric criteria are not within matching requirements, the profile model is revised. When the calculated statistical metric criteria are within matching requirements, the profile model or the revised profile model is stored.

Abstract: To evaluate the adequacy of a profile model, an initial profile model is selected. The profile model includes profile model parameters to be measured in implementing types of process control to be used in controlling a fabrication process. A measurement of profile model parameters is obtained using a first metrology tool and the profile model. A measurement of the profile model parameters is obtained using a second metrology tool and the profile model. Statistical metric criteria are calculated based on the measurements of the profile model parameters obtained using the first and second metrology tools. When the calculated statistical metric criteria are not within matching requirements, the profile model is revised. When the calculated statistical metric criteria are within matching requirements, the profile model or the revised profile model is stored.

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: 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: 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: 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.