System and method for analyzing a sample with a dynamic recipe based on iterative experimentation and feedback

1. A system for analyzing one or more samples, comprising:

a sample analysis sub-system configured to perform one or more measurements on the one or more samples; and

at least one controller communicatively coupled to the sample analysis sub-system, the at least one controller configured to:

receive design of experiment (DoE) data for performing the one or more measurements on the one or more samples;

determine rankings for a set of target parameters;

generate a recipe for performing the one or more measurements on the one or more samples based on the DoE data and the rankings for the set of target parameters;

execute a run manager configured to determine one or more run parameters of the sample analysis sub-system based on the recipe, wherein the run manager dynamically selects one or more wavelengths and one or more targets based on DoE priority defined by the recipe, wherein the run manager is configured to adjust the one or more run parameters of the sample analysis sub-system for each run, wherein the one or more run parameters for each run are applied to a learning algorithm, and an output of the learning algorithm serves as an input to the run manager; and

perform the one or more measurements on the one or more samples, via the sample analysis sub-system according to the one or more run parameters from the run manager.

2. The system of claim 1 , wherein the at least one controller is further configured to perform one or more pre-recipe operations to tune the sample analysis sub-system.

3. The system of claim 2 , wherein the one or more pre-recipe operations include at least one of a tool parameter calibration procedure or a tool parameter calibration test.

4. The system of claim 1 , wherein the at least one controller is further configured to adjust at least one run parameter of the one or more run parameters based on external reference data.

5. The system of claim 4 , wherein the external reference data comprises user-input reference data.

6. The system of claim 1 , wherein the one or more run parameters include at least one of an illumination parameter, a target parameter, an optical parameter, a spatial parameter, a scan sequence, or an actuation sequence.

7. The system of claim 1 , wherein the output includes at least one of a current measurement, a historical measurement, a current run parameter, or a historical run parameter.

8. The system of claim 1 , wherein the sample analysis sub-system comprises a metrology system.

9. The system of claim 1 , wherein the at least one controller is further configured to adjust the recipe for performing the one or more measurements on the one or more samples in either of an evaluation mode or a hardware virtualization mode.

10. The system of claim 9 , wherein the at least one controller is configured to adjust the recipe based on at least one of an on-tool data processing algorithm or an off-tool data processing algorithm.

11. The system of claim 1 , wherein the at least one controller is further configured to adjust the recipe based on external reference data.

12. The system of claim 1 , wherein the at least one controller is further configured to rank one or more recipe parameters of the recipe based on at least one of an on-tool ranking algorithm or an off-tool ranking algorithm.

13. A controller for a sample analysis sub-system, comprising:

one or more processors; and

at least one computer-readable medium communicatively coupled to the one or more processors, the at least one computer-readable medium storing program instructions that, when executed by the one or more processors, are configured to cause the one or more processors to:

receive design of experiment (DoE) data for performing one or more measurements on the one or more samples;

determine rankings for a set of target parameters;

generate a recipe for performing the one or more measurements on one or more samples based on the DoE data and the rankings for the set of target parameters;

execute a run manager configured to determine one or more run parameters of the sample analysis sub-system based on the recipe, wherein the run manager dynamically selects one or more wavelengths and one or more targets based on DoE priority defined by the recipe, wherein the run manager is configured to adjust the one or more run parameters of the sample analysis sub-system for each run, wherein the one or more run parameters for each run are applied to a learning algorithm, and an output of the learning algorithm serves as an input to the run manager; and

perform the one or more measurements on the one or more samples, via the sample analysis sub-system according to the one or more run parameters from the run manager.

14. The controller of claim 13 , wherein the program instructions are further configured to cause the one or more processors to perform one or more pre-recipe operations to tune the sample analysis sub-system.

15. The controller of claim 14 , wherein the one or more pre-recipe operations include at least one of a tool parameter calibration procedure or a tool parameter calibration test.

16. The controller of claim 13 , wherein the program instructions are further configured to cause the one or more processors to adjust the one or more run parameters based on external reference data.

17. The controller of claim 16 , wherein the external reference data comprises user-input reference data.

18. The controller of claim 13 , wherein the one or more run parameters include at least one of an illumination parameter, a target parameter, an optical parameter, a spatial parameter, a scan sequence, or an actuation sequence.

19. The controller of claim 13 , wherein the output includes at least one of a current measurement, a historical measurement, a current run parameter, or a historical run parameter.

20. The controller of claim 13 , wherein the sample analysis sub-system comprises a metrology system.

21. The controller of claim 13 , wherein the program instructions are further configured to cause the one or more processors to adjust the recipe for performing the one or more measurements on the one or more samples in either of an evaluation mode or a hardware virtualization mode.

22. The controller of claim 21 , wherein the program instructions are configured to cause the one or more processors to adjust the recipe based on at least one of an on-tool data processing algorithm or an off-tool data processing algorithm.

23. The controller of claim 13 , wherein the program instructions are further configured to cause the one or more processors to adjust the recipe based on external reference data.

24. The controller of claim 13 , wherein the program instructions are further configured to cause the one or more processors to rank one or more recipe parameters of the recipe based on at least one of an on-tool ranking algorithm or an off-tool ranking algorithm.

25. A method of analyzing one or more samples, comprising:

receiving design of experiment (DoE) data for performing one or more measurements on the one or more samples;

determining rankings for a set of target parameters;

generating a recipe for performing the one or more measurements on the one or more samples based on the DoE data and the rankings for the set of target parameters;

executing a run manager to determine one or more run parameters of a sample analysis sub-system based on the recipe, wherein the run manager dynamically selects one or more wavelengths and one or more targets based on DoE priority defined by the recipe, wherein the run manager is configured to adjust the one or more run parameters of the sample analysis sub-system for each run, wherein the one or more run parameters for each run are applied to a learning algorithm, and an output of the learning algorithm serves as an input to the run manager; and

performing the one or more measurements on the one or more samples, via the sample analysis sub-system according to the one or more run parameters from the run manager.