Virtual metrology systems and methods for using feedforward critical dimension data to predict other critical dimensions of a wafer
A controller includes a memory that stores a first model corresponding to a first critical dimension of a substrate processed by a substrate processing system and a second model corresponding to a second critical dimension of the substrate. The second model includes a predicted relationship between the first critical dimension and the second critical dimension. A critical dimension prediction module calculates a first prediction of the first critical dimension of the substrate using the first model, provides the first prediction of the first critical dimension as an input to the second model, and calculates and outputs a second prediction of the second critical dimension of the substrate using the second model.
1. A controller, comprising:
a memory that stores (i) a first model corresponding to a first critical dimension of a substrate processed by a substrate processing system and (ii) a second model corresponding to a second critical dimension of the substrate, wherein the second model includes a predicted relationship between the first critical dimension and the second critical dimension; and
a critical dimension prediction module that (i) calculates a first prediction of the first critical dimension of the substrate using the first model, (ii) provides the first prediction of the first critical dimension as an input to the second model, and (iii) calculates and outputs a second prediction of the second critical dimension of the substrate using the second model.
2. The controller of claim 1 , wherein at least one of the first model and the second model corresponds to a virtual metrology model.
3. The controller of claim 1 , wherein the first critical dimension and the second critical dimension are associated with a same feature of the substrate.
4. The controller of claim 1 , wherein the first critical dimension and the second critical dimension correspond to a height of a high aspect ratio structure on the substrate.
5. The controller of claim 1 , wherein the first critical dimension and the second critical dimension correspond to a width between high aspect ratio structures on the substrate.
6. The controller of claim 1 , wherein the critical dimension prediction module updates the second model based on a comparison between the first prediction of the first critical dimension and a measurement of the first critical dimension.
7. The controller of claim 1 , wherein the predicted relationship is based on respective measurements of the first critical dimension and the second critical dimension on a plurality of substrates.
8. The controller of claim 1 , wherein the predicted relationship is based on a ratio of (i) a difference between the first critical dimension and a third critical dimension to (ii) a difference between the first critical dimension and the second critical dimension.
9. The controller of claim 1 , wherein the predicted relationship corresponds to a linear or a non-linear relationship between the first critical dimension and the second critical dimension.
10. A system, comprising:
the controller of claim 1 ; and
the substrate processing system, wherein the controller is remotely located from the substrate processing system.
11. A method, comprising:
storing (i) a first model corresponding to a first critical dimension of a substrate processed by a substrate processing system and (ii) a second model corresponding to a second critical dimension of the substrate, wherein the second model includes a predicted relationship between the first critical dimension and the second critical dimension;
calculating a first prediction of the first critical dimension of the substrate using the first model;
providing the first prediction of the first critical dimension as an input to the second model; and
calculating and outputting a second prediction of the second critical dimension of the substrate using the second model.
12. The method of claim 11 , wherein at least one of the first model and the second model corresponds to a virtual metrology model.
13. The method of claim 11 , wherein the first critical dimension and the second critical dimension are associated with a same feature of the substrate.
14. The method of claim 11 , wherein the first critical dimension and the second critical dimension correspond to a height of a high aspect ratio structure on the substrate.
15. The method of claim 11 , wherein the first critical dimension and the second critical dimension correspond to a width between high aspect ratio structures on the substrate.
16. The method of claim 11 , further comprising updating the second model based on a comparison between the first prediction of the first critical dimension and a measurement of the first critical dimension.
17. The method of claim 11 , wherein the predicted relationship is based on respective measurements of the first critical dimension and the second critical dimension on a plurality of substrates.
18. The method of claim 11 , wherein the predicted relationship is based on a ratio of (i) a difference between the first critical dimension and a third critical dimension to (ii) a difference between the first critical dimension and the second critical dimension.
19. The method of claim 11 , wherein the predicted relationship corresponds to a linear or a non-linear relationship between the first critical dimension and the second critical dimension.