System and method for estimating measurement uncertainty for characterization systems

A characterization system, the characterization system comprising:

• a characterization sub-system configured to obtain real-time characterization data of one or more samples;
• and
• one or more controllers including one or more processors configured to execute a set of program instructions stored in memory, the set of program instructions configured to cause the one or more processors to:
• train a machine learning-based characterization library based on a set of training data, the set of training data including at least known characterization data associated with the one or more samples, theoretical characterization data, and the real-time characterization data associated with the one or more samples from the characterization sub-system;
• generate one or more characterization measurements using the trained machine learning-based characterization library based on the real-time characterization data associated with the one or more samples from the characterization sub-system;
• determine one or more additional characterization measurements based on a non-machine learning-based technique, the non-machine learning-based technique configured to determine the one or more additional characterization measurements based on the real-time characterization data from the characterization sub-system or additional real-time characterization data from an additional characterization sub-system;
• and
• compare the one or more characterization measurements based on the generated machine learning-based characterization library and the one or more additional characterization measurements based on the non-machine learning-based technique to monitor a measurement uncertainty of the machine learning-based characterization library.

A method, the method comprising:

• training a machine learning-based characterization library based on a set of training data, the set of training data including at least known characterization data associated with one or more samples, theoretical characterization data, and real-time characterization data associated with the one or more samples from a characterization sub-system;
• generating one or more characterization measurements using the trained machine learning-based characterization library based on the real-time characterization data associated with the one or more samples from the characterization sub-system;
• determining one or more additional characterization measurements based on a non-machine learning-based technique, the non-machine learning-based technique configured to determine the one or more additional characterization measurements based on the real-time characterization data from the characterization sub-system or additional real-time characterization data from an additional characterization sub-system;
• and
• comparing the one or more characterization measurements based on the generated machine learning-based characterization library and the one or more additional characterization measurements based on the non-machine learning-based technique to monitor a measurement uncertainty of the machine learning-based characterization library.