Predictive modeling of metrology in semiconductor processes
Implementations described herein generally relate to improving silicon wafer manufacturing. In one implementation, a method includes receiving data from one or more manufacturing tools about a manufacturing process of a silicon wafer. The method further includes determining, based on the data, predictive information about a quality of the silicon wafer. The method further includes providing the predictive information to a manufacturing system, wherein the predictive information is used to determine whether to take corrective action.
1. A method for improving silicon wafer manufacturing, comprising:
receiving time-series data from one or more manufacturing tools about a manufacturing process of a silicon wafer;
converting the time-series data from a parameter-specific and measurement unit-specific space comprising a first number of dimensions into a parameter-agnostic and measurement unit-agnostic space comprising a second number of dimensions to produce converted data;
determining, based on the converted data, predictive information about a quality of the silicon wafer;
providing the predictive information to a manufacturing system, wherein the predictive information is used to determine whether to take corrective action.
2. The method of claim 1 , wherein the predictive information is determined using stochastic modeling.
3. The method of claim 1 , wherein the time-series data comprises information about behavior of one or more parameters.
4. The method of claim 1 , wherein the predictive information comprises information about at least one of: a thickness, an optical reflective index, an absorption index, a strength, and a critical dimension variation across the silicon wafer.
5. The method of claim 1 , further comprising:
providing the predictive information to a metrology tool, wherein the metrology tool checks for a problem based on the predictive information.
6. The method of claim 1 , wherein determining the predictive information further comprises converting a result of one or more calculations back into the parameter-specific and measurement unit-specific space.
7. A computing system, comprising:
a memory; and
a processor configured to perform a method for improving silicon wafer manufacturing, the method comprising:
receiving time-series data from one or more manufacturing tools about a manufacturing process of a silicon wafer;
converting the time-series data from a parameter-specific and measurement unit-specific space comprising a first number of dimensions into a parameter-agnostic and measurement unit-agnostic space comprising a second number of dimensions to produce converted data;
determining, based on the converted data, predictive information about a quality of the silicon wafer;
providing the predictive information to a manufacturing system, wherein the predictive information is used to determine whether to take corrective action.
8. The computing system of claim 7 , wherein the predictive information is determined using stochastic modeling.
9. The computing system of claim 7 , wherein the time-series data comprises information about behavior of one or more parameters.
10. The computing system of claim 7 , wherein the predictive information comprises information about at least one of: a thickness, an optical reflective index, an absorption index, a strength, and a critical dimension variation across the silicon wafer.
11. The computing system of claim 7 , wherein the method further comprises:
providing the predictive information to a metrology tool, wherein the metrology tool checks for a problem based on the predictive information.
12. The computing system of claim 7 , wherein determining the predictive information further comprises converting a result of one or more calculations back into the parameter-specific and measurement unit-specific space.
13. A non-transitory computer-readable medium comprising instructions that when executed by a computing device cause the computing device to perform a method for improving silicon wafer manufacturing, the method comprising:
receiving time-series data from one or more manufacturing tools about a manufacturing process of a silicon wafer;
converting the time-series data from a parameter-specific and measurement unit-specific space comprising a first number of dimensions into a parameter-agnostic and measurement unit-agnostic space comprising a second number of dimensions to produce converted data;
determining, based on the converted data, predictive information about a quality of the silicon wafer;
providing the predictive information to a manufacturing system, wherein the predictive information is used to determine whether to take corrective action.
14. The non-transitory computer-readable medium of claim 13 , wherein the predictive information is determined using stochastic modeling.
15. The non-transitory computer-readable medium of claim 13 , wherein the time-series data comprises information about behavior of one or more parameters.
16. The non-transitory computer-readable medium of claim 13 , wherein the predictive information comprises information about at least one of: a thickness, an optical reflective index, an absorption index, a strength, and a critical dimension variation across the silicon wafer.
17. The non-transitory computer-readable medium of claim 13 , wherein the method further comprises:
providing the predictive information to a metrology tool, wherein the metrology tool checks for a problem based on the predictive information.
18. The non-transitory computer-readable medium of claim 13 , wherein determining the predictive information further comprises converting a result of one or more calculations back into the parameter-specific and measurement unit-specific space.