Multiple tool and structure analysis

A method for analyzing a sample, comprising the steps of:

• optically measuring a first structure on a metrology tool to obtain a first data set corresponding to a number of first parameters;
• optically measuring a second structure on the metrology tool to obtain a second data set corresponding to a number of second parameters, the first and second structures including at least one common structural component;
• selecting at least one common parameter for each of the first and second data sets, the common parameters having a substantially identical value for both the first and second data sets;
• selecting a mathematical model for the first and second structures;
• performing in parallel a regression analysis on each of the first and second data sets using the mathematical model to obtain a first solution, wherein during the regression, the values of the first and second parameters are varied with the value selected for the common parameter being shared in order to minimize the number of fitting parameters in each regression analysis;
• and
• storing the first solution.

A system for analyzing a sample, comprising:

• a source of radiation for illuminating the sample;
• a detection device for receiving radiation reflected from the sample and providing an output signal in response thereto;
• and
• a processor for receiving the output signal, the processor having instructions for:
• obtaining a first data set corresponding to a number of first parameters for a first structure;
• obtaining a second data set corresponding to a number of second parameters for a second structure, the first and second structures including at least one common structural component;
• selecting at least one common parameter for each of the first and second data sets, the common parameter having a substantially identical value for both the first and second data sets;
• performing in parallel a regression analysis on each of the first and second data sets using a mathematical model to obtain a first solution, wherein during the regression, the values of the first and second parameters are varied with the value selected for the common parameter being shared in order to minimize the number of fitting parameters in each regression analysis.

A system for analyzing a sample, comprising:

• a first measurement tool including a first source of radiation for illuminating the sample and a first detection device for receiving radiation reflected from the sample and providing a first output signal in response thereto;
• a second measurement tool including a second source of radiation for illuminating the sample and a second detection device for receiving radiation reflected from the sample and providing a second output signal in response thereto;
• and
• a processor for receiving the first and second output signals, the processor having instructions for:
• obtaining a first data set corresponding to a number of first parameters for the sample measured using the first measurement tool;
• obtaining a second data set corresponding to a number of second parameters for the sample measured using the second measurement tool;
• selecting at least one common parameter for each of the first and second data sets, the common parameter having a substantially identical value for both the first and second data sets;
• performing in parallel a regression analysis on each of the first and second data sets using a mathematical model to obtain a first solution, wherein during the regression, the values of the first and second parameters are varied with the value selected for the common parameter being shared in order to minimize the number of fitting parameters in each regression analysis.

A method for analyzing multiple data sets, comprising the steps of:

• obtaining a first data set corresponding to a number of first parameters;
• obtaining a second data set corresponding to a number of second parameters;
• selecting at least one common parameter for each of the first and second data sets, the common parameter having a substantially identical value for both the first and second data sets;
• selecting a mathematical model for the first and second data sets;
• performing in parallel a regression analysis on each of the first and second data sets using the mathematical model to obtain a first solution, wherein during the regression, the values of the first and second parameters are varied with the value selected for the common parameter being shared in order to minimize the number of fitting parameters in each regression analysis;
• and
• storing the first solution.