System and method for learning and/or optimizing manufacturing processes

1. A system, comprising:

a memory storing computer executable components; and

a processor configured to execute the following computer executable components stored in the memory:

a learning component that generates a set of candidate process models based on process data associated with one or more fabrication tools and selects a particular process model from the set of candidate process models based on a first defined criterion; and

an optimization component that generates a set of candidate solutions associated with the particular process model and selects a particular solution from the set of candidate solutions based on a second defined criterion.

2. The system of claim 1 , wherein the learning component selects the particular process model from the set of candidate process models based on an error value associated with the particular process model.

3. The system of claim 1 , wherein the learning component determines that the particular process model from the set of candidate process models is associated with lowest error.

4. The system of claim 1 , wherein the optimization component selects the particular solution from the set of candidate solutions based on a target output value.

5. The system of claim 1 , wherein the optimization component selects the particular solution from the set of candidate solutions based on an output value associated with the particular solution.

6. The system of claim 1 , wherein the optimization component selects the particular solution from the set of candidate solutions based on a target output value and an output value associated with the particular solution.

7. The system of claim 1 , wherein the learning component generates the set of candidate process models based on a data matrix associated with the process data.

8. The system of claim 1 , wherein the learning component generates the set of candidate process models based on a data matrix associated with at least one of sensor measurement data, spectral data, device measurement data and maintenance data.

9. The system of claim 1 , wherein the learning component generates a quality value for each candidate process model in the set of candidate process models to facilitate selection of the particular process model from the set of candidate process models.

10. The system of claim 9 , wherein the learning component further generates a diversity value associated with uniqueness for each candidate process model in the set of candidate process models.

11. The system of claim 10 , wherein the learning component generates a ranking of the set of candidate process models as a function of the quality value and the diversity value for each candidate process model in the set of candidate process models, selects one or more other process models from the set of candidate process models based on the ranking, and stores the particular process model and the one or more other process models in a data store.

12. The system of claim 1 , wherein the optimization component generates a quality value for each candidate solution in the set of candidate solutions to facilitate selection of the particular solution from the set of candidate solutions.

13. The system of claim 12 , wherein the optimization component further generates a diversity value associated with uniqueness for each candidate solution in the set of candidate solutions.

14. The system of claim 13 , wherein the optimization component generates a ranking of the set of candidate solutions as a function of the quality value and the diversity value for each candidate solution in the set of candidate solutions, selects one or more other solutions from the set of candidate solutions based on the ranking, and stores the particular solution and the one or more other solutions in a data store.

15. A method, comprising:

employing a processor that facilitates execution of computer executable instructions stored on a non-transitory computer readable medium to implement operations, comprising:

generating a set of candidate process models based on process data associated with one or more manufacturing tools;

selecting a particular process model from the set of candidate process models based on a first defined criterion;

generating a set of candidate solutions associated with the particular process model; and

selecting a particular solution from the set of candidate solutions based on a second defined criterion.

16. The method of claim 15 , further comprising:

generating a quality value for each candidate process model in the set of candidate process models.

17. The method of claim 16 , wherein the selecting the particular process model comprises selecting the particular process model from the set of candidate process models based on the quality value associated with each candidate process model in the set of candidate process models.

18. The method of claim 15 , wherein the selecting the particular solution comprises selecting the particular solution from the set of candidate solutions based on a target output value and an output value associated with the particular solution.

19. A non-transitory computer-readable medium having stored thereon computer-executable instructions that, in response to execution by a system including a processor, cause the system to perform operations, the operations including:

generating a set of candidate process models based on process data associated with a fabrication tool;

selecting a process model from the set of candidate process models based on a defined criterion;

generating a set of candidate solutions associated with the process model; and

selecting a solution from the set of candidate solutions based on a target output value.

20. The non-transitory computer-readable medium of claim 19 , wherein the selecting the solution comprises selecting the solution from the set of candidate solutions based on an output value associated with the solution.