Machine learning based inverse optical proximity correction and process model calibration
A method for calibrating a process model and training an inverse process model of a patterning process. The training method includes obtaining a first patterning device pattern from simulation of an inverse lithographic process that predicts a patterning device pattern based on a wafer target layout, receiving wafer data corresponding to a wafer exposed using the first patterning device pattern, and training an inverse process model configured to predict a second patterning device pattern using the wafer data related to the exposed wafer and the first patterning device pattern.
1. A method comprising:
obtaining a patterning device pattern from simulation of an inverse lithographic process that predicts the patterning device pattern based on a wafer target layout, wherein the patterning device pattern is configured to be transferred, by a lithographic apparatus, from a patterning device onto a wafer with the aim to form the wafer target layout and the simulation of the inverse lithographic process involves simulation using one or more selected from:
a mask model configured to predict a mask image corresponding to the patterning device pattern,
an optical model configured to predict an aerial image corresponding to the patterning device pattern,
a resist model configured to predict a resist image corresponding to the patterning device pattern, or
an etch model configured to predict an etch image corresponding to the patterning device pattern;
receiving, by a hardware processor system, wafer data corresponding to a wafer exposed using the patterning device pattern; and
calibrating, by the hardware processor system, a process model of a patterning process based on the wafer data related to the exposed wafer and the patterning device pattern.
2. The method of claim 1 , wherein the calibrating the process model is an iterative process, an iteration comprising:
determining values of model parameters of the process model based on the wafer data and the patterning device pattern; and
adjusting the values of the model parameters until a first cost function of the process model is improved.
3. The method of claim 2 , wherein the first cost function is a difference between the wafer data and a predicted pattern obtained from the calibrated process model.
4. The method of claim 3 , wherein the difference is measured in terms of a performance parameter of the patterning process, the performance parameter including at least one selected from: a contour of a feature, critical dimension, and/or a process window.
5. The method of claim 1 , wherein the simulation of the inverse lithographic process is an iterative process, an iteration comprising:
obtaining an initial patterning device pattern;
determining, via simulation using the process model, a simulated wafer pattern based on the initial patterning device pattern;
evaluating a second cost function, wherein the second cost function computes a difference between the simulated wafer pattern and the wafer target layout; and
adjusting the initial patterning device pattern such that the second cost function is reduced.
6. The method of claim 1 , wherein the wafer data comprises measurements related to a feature printed on the wafer including a critical dimension, a contour of the feature, and/or a process window.
7. The method of claim 6 , wherein the measurements are based on an image of the exposed wafer obtained from an e-beam inspection apparatus and/or an optical inspection apparatus.
8. The method of claim 7 , wherein the e-beam inspection apparatus is a scanning electron microscope.
9. The method of claim 1 , wherein the process model is a mask model, an optical model, a resist model, and/or an etch model.
10. The method of claim 1 , wherein the process model is a physics based model and/or a machine learning model.
11. The method of claim 10 , wherein the process model is a machine learning model and the machine learning model is a convolutional neural network.
12. A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least:
obtain a patterning device pattern from simulation of an inverse lithographic process that predicts the patterning device pattern based on a wafer target layout, wherein the patterning device pattern is configured to be transferred, by a lithographic apparatus, from a patterning device onto a wafer with the aim to form the wafer target layout and the simulation of the inverse lithographic process involves use of one or more selected from:
a mask model configured to predict a mask image corresponding to the patterning device pattern,
an optical model configured to predict an aerial image corresponding to the patterning device pattern,
a resist model configured to predict a resist image corresponding to the patterning device pattern, or
an etch model configured to predict an etch image corresponding to the patterning device pattern;
receive wafer data corresponding to a wafer exposed using the patterning device pattern; and
calibrate a process model of a patterning process based on the wafer data related to the exposed wafer and the patterning device pattern.
13. The computer program product of claim 12 , wherein the instructions configured to cause the computer system to calibrate the process model are configured to do so in an iterative manner, an iteration comprising:
determination of values of model parameters of the process model based on the wafer data and the patterning device pattern; and
adjustment of the values of the model parameters until a first cost function of the process model is improved.
14. The computer program product of claim 13 , wherein the first cost function is a difference between the wafer data and a predicted pattern obtained from the calibrated process model.
15. The computer program product of claim 12 , wherein the simulation of the inverse lithographic process is an iterative process, an iteration comprising:
obtaining of an initial patterning device pattern;
determination, via simulation using the process model, a simulated wafer pattern based on the initial patterning device pattern;
evaluation of a second cost function, wherein the second cost function computes a difference between the simulated wafer pattern and the wafer target layout; and
adjustment of the initial patterning device pattern such that the second cost function is reduced.
16. The computer program product of claim 12 , wherein the wafer data comprises measurements related to a feature printed on the wafer including a critical dimension, a contour of the feature, and/or a process window.
17. The computer program product of claim 12 , wherein the process model is a mask model, an optical model, a resist model, and/or an etch model.
18. The computer program product of claim 12 , wherein the process model is a machine learning model.
19. The computer program product of claim 18 , wherein the machine learning model is a convolutional neural network.
20. The computer program product of claim 14 , wherein the difference is measured in terms of a performance parameter of the patterning process, the performance parameter including at least one selected from: a contour of a feature, critical dimension, and/or a process window.