System and method for predicting stochastic-aware process window and yield and their use for process monitoring and control
In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, using the model to predict the low-probability stochastic defect, determining a process window based on the low-probability stochastic defect, and controlling, based on the process window, a lithography tool to manufacture a device.
1. A method, comprising:
generating a model trained to predict a low-probability stochastic defect;
using the model to predict the low-probability stochastic defect;
determining a process window based on the low-probability stochastic defect; and
controlling, based on the process window, a lithography tool to manufacture a device.
2. The method of claim 1 , wherein the process window is determined based on one or more parameters of a lithography tool.
3. The method of claim 2 , further comprising modifying the one or more parameters based on the low-probability stochastic defect.
4. The method of claim 2 , wherein the one or more parameters comprise an exposure dose of a lithography tool.
5. The method of claim 2 , wherein the one or more parameters comprise a focus of a lithography tool.
6. The method of claim 1 , wherein:
the stochastic defect comprises a bridge or a break in a pattern of lines and spaces manufactured in a specific way,
calibrating is performed by measuring a stochastic behavior of a sampling of lines and spaces manufactured in the specific way, and
the stochastic behavior is characterized by a standard deviation of an edge position or linewidth, by a power spectral density of measured deviations, by a local critical dimension uniformity of short line segments as a function of a length of the line segments, by another metric, or some combination thereof.
7. The method of claim 1 , wherein:
the stochastic defect comprises a merged contact hole or pillar feature, a missing contact hole or pillar feature, or both, and
calibrating is performed by measuring a distribution of contact hole or pillar feature sizes on a sample of having a certain size.
8. The method of claim 1 , further comprising:
generating a yield model trained to predict an amount of yield based on one or more stochastic defects; and
determining, using the yield model, the amount of yield by inputting the stochastic defect probability into the yield model.
9. A system comprising:
a memory device storing instructions; and
a processing device communicatively coupled to the memory device, wherein the processing device executes the instructions to:
generate a model trained to predict a low-probability stochastic defect;
use the model to predict the low-probability stochastic defect; determining a process window based on the low-probability stochastic defect; and
control, based on the process window, a lithography tool to manufacture a device.
10. The system of claim 9 , wherein the process window is determined based on one or more parameters of a lithography tool.
11. The system of claim 10 , further comprising modifying the one or more parameters based on the low-probability stochastic defect.
12. The system of claim 10 , wherein the one or more parameters comprise an exposure dose of a lithography tool.
13. The system of claim 10 , wherein the one or more parameters comprise a focus of a lithography tool.
14. The system of claim 10 , wherein:
the stochastic defect comprises a bridge or a break in a pattern of lines and spaces manufactured in a specific way,
calibrating is performed by measuring a stochastic behavior of a sampling of lines and spaces manufactured in the specific way, and
the stochastic behavior is characterized by a standard deviation of an edge position or linewidth, by a power spectral density of measured deviations, by a local critical dimension uniformity of short line segments as a function of a length of the line segments, by another metric, or some combination thereof.
15. The system of claim 10 , wherein:
the stochastic defect comprises a merged contact hole or pillar feature feature, a missing contact hole or pillar feature, or both, and
the calibrating is performed by measuring a distribution of contact hole or pillar feature sizes on a sample of having a certain size.
16. The system of claim 10 , wherein the processing device is further configured to:
generate a yield model trained to predict an amount of yield based on one or more stochastic defects; and
determine, using the yield model, the amount of yield by inputting the stochastic defect probability into the yield model.
17. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:
generate a model trained to predict a low-probability stochastic defect;
use the model to predict the low-probability stochastic defect; determining a process window based on the low-probability stochastic defect; and
control, based on the process window, a lithography tool to manufacture a device.
18. The computer-readable medium of claim 17 , wherein the process window is determined based on one or more parameters of a lithography tool.
19. The computer-readable medium of claim 18 , further comprising modifying the one or more parameters based on the low-probability stochastic defect.
20. The computer-readable medium of claim 18 , wherein the one or more parameters comprise at least one of an exposure dose of a lithography tool and a focus of a lithography tool.