System and method for key parameter identification, process model calibration and variability analysis in a virtual semiconductor device fabrication environment

1. A non-transitory computer-readable medium holding computer-executable instructions for key parameter identification in a virtual semiconductor fabrication environment, the instructions when executed causing at least one computing device to:

receive, for a semiconductor device structure to be virtually fabricated in a computing device-generated virtual fabrication environment, a selection of 2D design data and a process sequence that includes a plurality of processes;

perform with the computing device a plurality of virtual fabrication runs for the semiconductor device structure based on a Design of Experiment (DOE) using the 2D design data and the process sequence, the plurality of virtual fabrication runs building a plurality of 3D models;

receive a user identification of one or more targets for the semiconductor device structure;

execute an analytics module in the virtual fabrication environment to identify one or more outliers in measurement data for the one or more targets in the plurality of 3D models produced from the virtual fabrication runs;

receive a user selection to include or remove one or more of the one or more identified outliers from the measurement data for the one or more targets in the plurality of 3D models, the selection received via a user interface provided in the virtual fabrication environment, the outliers including one or more repeated values and/or one or more data values exceeding a threshold based on the measurement data;

perform a linear or quadratic regression analysis on the measurement data for the one or more targets with the analytics module after the including or removing of the selected outliers from the measurement data;

identify one or more key parameters with the analytics module based on a result of the regression analysis, the key parameters being parameters whose value affects the measurement data for the one or more targets; and

display or export an identification of the identified one or more key parameters.

2. The medium of claim 1 , wherein the instructions when executed further cause the at least one computing device to:

rank programmatically the identified one or more key parameters.

3. The medium of claim 1 , wherein the instructions when executed further cause the at least one computing device to:

provide a user interface in the virtual fabrication environment for receiving a user selection of at least one of the type of DOE, parameters to vary in the DOE, numbers of levels, and values for levels for the DOE.

4. The medium of claim 1 , wherein the selected targets are at least one of a metrology measurement, a structure search, a DTC check and an electrical analysis.

5. The medium of claim 1 , wherein the instructions when executed further cause the at least one computing device to:

perform a multicollinearity check on target data for the plurality of 3D models.

6. The medium of claim 1 , wherein the instructions when executed further cause the at least one computing device to:

receive a user selection of desired values for selected targets via the user interface in the virtual fabrication environment, the selected targets made from a group of targets associated with key parameters;

receive a user selection of upper and lower bounds for each identified key parameter via the user interface in the virtual fabrication environment;

execute an optimization algorithm for the plurality of 3D models using the identified key parameters, desired values and upper and lower bounds; and

display or export results from the optimization algorithm.

7. The medium of claim 6 , wherein the selected targets from the group of targets are associated with key parameters that were previously identified by the analytics module and for which regression data exists and wherein the optimization algorithm performs indirect optimization using the regression data.

8. The medium of claim 6 , wherein the key parameters are manually identified by a user and wherein the optimization algorithm performs direct optimization.

9. The medium of claim 1 , wherein the instructions when executed further cause the at least one computing device to:

receive calibration options for the optimization algorithm from a user via the user interface and the calibration options include one or more of a number of iterations, a convergence tolerance, a number of trials and a type of scoring function.

10. The medium of claim 1 , wherein the D.O.E. is a Monte Carlo simulation and wherein the instructions when executed further cause the at least one computing device to:

receive a user selection to perform a variability analysis for the plurality of virtual fabrication runs;

perform the plurality of virtual fabrication runs; and

provide results enabling assessment of the precision of the sigma.

11. The medium of claim 1 , wherein the D.O.E. is a Monte Carlo simulation and wherein the instructions when executed further cause the at least one computing device to:

receive a user selection to perform a variability analysis for the plurality of virtual fabrication runs;

perform the plurality of virtual fabrication runs; and

provide results enabling assessment of target data normality.

12. The medium of claim 1 , wherein the D.O.E. is a Monte Carlo simulation and the number of virtual fabrication runs is approximately two hundred.

13. The medium of claim 1 , wherein the D.O.E. is a Monte Carlo simulation and the number of virtual fabrication runs can be adjusted by the user to achieve desired precision (CI) of the sigma.

14. The medium of claim 1 , wherein the D.O.E. is a Monte Carlo simulation and wherein the instructions when executed further cause the at least one computing device to:

receive a user selection to perform a variability analysis for the plurality of virtual fabrication runs;

perform the plurality of virtual fabrication runs; and

display simultaneously results for a plurality of selected targets.

15. A method for key parameter identification in a virtual semiconductor fabrication environment, comprising:

receiving, for a semiconductor device structure to be virtually fabricated in a computing device-generated virtual fabrication environment, a selection of 2D design data and a process sequence that includes a plurality of processes;

performing with the computing device a plurality of virtual fabrication runs for the semiconductor device structure based on a Design of Experiment (DOE) using the 2D design data and the process sequence, the plurality of virtual fabrication runs building a plurality of 3D models;

receiving a user identification of one or more targets for the semiconductor device structure;

executing an analytics module in the virtual fabrication environment to identify one or more outliers in measurement data for the one or more targets in the plurality of 3D models produced from the virtual fabrication runs;

receiving a user selection to include or remove one or more of the one or more identified outliers from the measurement data for the one or more targets in the plurality of 3D models, the selection received via a user interface provided in the virtual fabrication environment, the outliers including one or more repeated values and/or one or more data values exceeding a threshold based on the measurement data;

performing a linear or quadratic regression analysis on the measurement data for the one or more targets with the analytics module after the including or removing of the selected outliers from the measurement data;

identifying one or more key parameters with the analytics module based on a result of the regression analysis, the key parameters being parameters whose value affects the measurement data for the one or more targets; and

displaying or exporting an identification of the identified one or more key parameters.

16. The method of claim 15 , further comprising:

ranking programmatically the identified one or more key parameters.

17. The method of claim 15 , further comprising:

providing a user interface in the virtual fabrication environment for receiving a user selection of at least one of the type of DOE, parameters to vary in the DOE, numbers of levels, and values for levels for the DOE.

18. The method of claim 15 , further comprising:

performing a multicollinearity check on target data for the plurality of 3D models.

19. The method of claim 15 , further comprising:

receiving a user selection of desired values for selected targets via the user interface in the virtual fabrication environment, the selected targets made from a group of targets associated with key parameters;

receiving a user selection of upper and lower bounds for each identified key parameter via the user interface in the virtual fabrication environment;

executing an optimization algorithm for the plurality of 3D models using the identified key parameters, desired values and upper and lower bounds; and

displaying or exporting results from the optimization algorithm.

20. The method of claim 15 , further comprising:

receiving calibration options for the optimization algorithm from a user via the user interface and the calibration options include one or more of a number of iterations, a convergence tolerance, a number of trials and a type of scoring function.

21. The method of claim 15 , wherein the D.O.E. is a Monte Carlo simulation and further comprising:

receiving a user selection to perform a variability analysis for the plurality of virtual fabrication runs;

performing the plurality of virtual fabrication runs; and

providing results enabling assessment of the precision of the sigma.

22. The method of claim 15 , wherein the D.O.E. is a Monte Carlo simulation and further comprising:

receiving a user selection to perform a variability analysis for the plurality of virtual fabrication runs;

performing the plurality of virtual fabrication runs; and

providing results enabling assessment of target data normality.

23. The method of claim 15 , wherein the D.O.E. is a Monte Carlo simulation and further comprising:

receiving a user selection to perform a variability analysis for the plurality of virtual fabrication runs;

performing the plurality of virtual fabrication runs; and

displaying simultaneously results for a plurality of selected targets.

24. A virtual fabrication system, comprising:

a computing device equipped with a processor and configured to generate a virtual fabrication environment that includes an analytics module, the virtual fabrication environment:

receiving, for a semiconductor device structure to be virtually fabricated, a selection of 2D design data and a process sequence that includes a plurality of processes,

performing a plurality of virtual fabrication runs for the semiconductor device structure based on a Design of Experiment (DOE) using the 2D design data and the process sequence, the plurality of virtual fabrication runs building a plurality of 3D models,

receiving a user identification of one or more targets for the semiconductor device structure,

executing the analytics module in the virtual fabrication environment to identify one or more outliers in measurement data for the one or more targets in the plurality of 3D models produced from the virtual fabrication runs,

receiving a user selection to include or remove one or more of the one or more identified outliers from the measurement data for the one or more targets in the plurality of 3D models, the selection received via a user interface provided in the virtual fabrication environment, the outliers including one or more repeated values and/or one or more data values exceeding a threshold based on the measurement data,

performing a linear or quadratic regression analysis on the measurement data for the one or more targets with the analytics module after the including or removing of the selected outliers from the measurement data,

identifying one or more key parameters with the analytics module based on a result of the regression analysis the key parameters being parameters whose value affects the measurement data for the one or more target, and

displaying or exporting an identification of the identified one or more key parameters; and

a display surface in communication with the computing device, the display surface configured to display the 3D structural model in a 3D view.

25. The virtual fabrication system of claim 24 , wherein the virtual fabrication environment:

ranks programmatically the identified one or more key parameters.

26. The virtual fabrication system of claim 24 , wherein the virtual fabrication environment:

receives a user selection of desired values for selected targets via the user interface, the selected targets made from a group of targets associated with key parameters;

receives a user selection of upper and lower bounds for each identified key parameter via the user interface;

executes an optimization algorithm for the plurality of 3D models using the identified key parameters, desired values and upper and lower bounds; and

displays or export results from the optimization algorithm.

27. The virtual fabrication system of claim 24 , wherein the virtual fabrication environment:

receive a user selection of desired values for selected targets via the user interface, the selected targets made from a group of targets associated with key parameters;

receive a user selection of upper and lower bounds for each identified key parameter via the user interface;

execute an optimization algorithm for the plurality of 3D models using the identified key parameters, desired values and upper and lower bounds; and

display or export results from the optimization algorithm.