Detecting hotspots using machine learning on diffraction patterns

1. A method, in a data processing system, for detecting lithographic hotspots, the method comprising:

receiving a design layout;

generating spatial pattern clips from the design layout using a sliding window to split the design layout into overlapping windows;

performing a transform on the spatial pattern clips to form frequency domain pattern clips;

performing feature extraction on the frequency domain pattern clips to form frequency domain features;

utilizing the extracted features on a set of training samples to train a machine learning classifier model; and

classifying a set of previously unseen patterns, based on frequency domain features of the previously unseen patterns using the trained machine learning classifier model, into hotspots and non-hotspots.

2. The method of claim 1 , wherein performing feature extraction comprises extracting a square window of N by N samples from a given frequency domain pattern clip, wherein N is based on a numerical aperture of an imaging lens used in a photolithography process.

3. The method of claim 1 , wherein performing feature extraction comprises extracting a frequency domain feature based on a subset of a given frequency domain pattern clip using layer, illumination, and optics information.

4. The method of claim 1 , further comprising:

training the machine learning classifier model using a support vector machine.

5. The method of claim 4 , wherein training the machine learning, classifier model comprises:

finding a hyperplane w such that features partitioned using maximum separation.

6. The method of claim 5 , wherein finding the hyperplane comprises solving the following quadric equation:

min 1/2 w T w,

s.t.:w.v+b≧ 1,∀ v εnonhotspot,

w.v+b≦− 1,∀ v εhotspot,

where T is the matrix transpose and b is a constant representing the intercept of the hyperplane with the axes.

7. The method of claim 1 , wherein the trained machine learning classifier model comprises a support vector machine or a neural networks.

8. The method of claim 1 , further comprising:

performing resolution enhancement technique optimization or ground rules to fix identified hotspots.

9. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:

receive a design layout;

generate spatial pattern clips from the design layout using a sliding window to split the design layout into overlapping windows;

perform a transform on the spatial pattern clips to form frequency domain pattern clips;

perform feature extraction on the frequency domain pattern clips to form frequency domain features;

utilize the extracted features on a set of training samples to train a machine learning classifier model; and

classify a set of previously unseen patterns, based on frequency domain features of the previously unseen patterns using the trained machine learning classifier model, into hotspots and non-hotspots.

10. The computer program product of claim 9 , wherein performing feature extraction comprises extracting a square window of N by N samples from a given frequency domain pattern clip, wherein N is based on a numerical aperture of an imaging lens used in a photolithography process.

11. The computer program product of claim 9 , wherein the computer readable program further causes the computing device to:

training the machine learning classifier model using a support vector machine.

12. The computer program product of claim 11 , wherein training the machine learning classifier model comprises:

finding a hyperplane w such that features are partitioned using maximum separation.

13. The computer program product of claim 12 , wherein finding the hyperplane comprises solving the following quadric equation:

min 1/2 w T w,

s.t.:w.v+b≧ 1,∀ v εnonhotspot,

w.v+b≦− 1,∀ v εhotspot,

where T is the matrix transpose and b is a constant representing the intercept of the hyperplane with the axes.

14. An apparatus, comprising:

a processor; and

a memory coupled to the processor, wherein the memory stores instructions which, when executed by the processor, cause the processor to:

receive a design layout;

generate spatial pattern clips from the design layout using a sliding window to split the design layout into overlapping windows;

perform a transform on the spatial pattern clips to form frequency domain pattern clips;

perform feature extraction on the frequency domain pattern clips to form frequency domain features;

utilize the extracted features on a set of training samples to train a machine learning classifier model; and

classify a set of previously unseen patterns, based on frequency domain features of the previously unseen patterns using the trained machine learning classifier model, into hotspots and non-hotspots.

15. The apparatus of claim 14 , wherein performing feature extraction comprises extracting a square window of N by N samples from a given frequency domain pattern clip, wherein N is based on a numerical aperture of an imaging lens used in a photolithography process.

16. The apparatus of claim 14 , wherein the computer readable program further causes the computing device to:

training the machine learning classifier model using a support vector machine.

17. The apparatus of claim 16 , wherein training the machine learning classifier model comprises:

finding a hyperplane w such that features are partitioned using maximum separation by solving the following quadric equation:

min1/2 w T w,

s.t.:w.v+b≧ 1,∀ v εnonhotspot,

w.v+b≦− 1,∀ v εhotspot,

where T is the matrix transpose and b is a constant representing the intercept of the hyperplane with the axes.

18. The computer program product of claim 9 , wherein performing feature extraction comprises extracting a frequency domain feature based on a subset of a given frequency domain pattern(clip using layer, illumination, and optics information.

19. The computer program product of claim 9 , wherein the computer readable program further causes the computing device to:

performing resolution enhancement technique optimization or ground rules to fix identified hotspots.

20. The apparatus of claim 14 , wherein performing feature extraction comprises extracting a frequency domain feature based on a subset of a given frequency domain pattern clip using layer, illumination, and optics information.