Method for displaying index values in generation of mask pattern verification model

1. A method for displaying an index value in generation of a mask pattern verification model, the method comprising:

calculating a first index value using a plurality of images, the images including an image of a resist image after lithography or an image of an after-etching image in manufacturing of a semiconductor device;

estimating a model based on the first index value and pattern information;

calculating a second index value using the model; and

displaying at least one of the first index value and the second index value.

2. The method according to claim 1 , further comprising:

detecting a first risky point based on the first index value or a second risky point based on the second index value; and

displaying at least a detected one of the first risky point and the second risky point.

3. The method according to claim 1 , wherein:

the calculating of the first index value includes:

generating a plurality of contour images from the images, respectively;

generating a mean contour image from the contour images; and

calculating a difference in a distance of contours between each of the contour images and the mean contour image.

4. The method according to claim 1 , wherein:

the calculating of the first index value includes:

obtaining a representative image, the representative image being one of the images or a mean image of the images;

extracting an inspection area from the representative image; and

counting a number of defects in the images for each position in the inspection area.

5. The method according to claim 1 , wherein:

the images are electron beam images, optical images, or X-ray images.

6. The method according to claim 1 , wherein:

the first index value is one of a number of defects of a pattern shape, a defect rate, and a fluctuation quantity of a pattern size.

7. The method according to claim 1 , wherein:

the pattern information includes information of an optical image, a resist image, or an after-etching image.

8. The method according to claim 1 , wherein:

the model is at least one of a rule-based model and a model-based model.

9. The method according to claim 1 , wherein:

the displaying includes simultaneously displaying both a display window of the second index value and a coordinate window of the second index value.

10. The method according to claim 1 , wherein:

in the displaying, at least one of a two-dimensional image of the first index value and a two-dimensional image of the second index value is displayed.

11. The method according to claim 1 , wherein:

the second index value is a defect rate or defect occurrence frequency in a pattern position.

12. The method according to claim 1 , wherein:

the second index value is an intensity of an optical image, a rate of change of the intensity to a change in exposure conditions, or a ratio of the intensity and the rate of change of the intensity to the change in exposure conditions.

13. The method according to claim 1 , wherein:

the estimating of the model is based on a relation between a pattern position and the first index value in the pattern position.

14. The method according to claim 1 , wherein:

the displaying includes simultaneously displaying a display window of the second index value and a mask layout whole image display window.

15. The method according to claim 1 , wherein:

the displaying includes simultaneously displaying a display window of the first index value or the second index value and a risky point detection value display window.

16. The method according to claim 1 , wherein:

the displaying includes simultaneously displaying a display window of the first index value and a display window of the second index value.

17. The method according to claim 1 , further comprising:

calculating a difference between the first index value and the second index value; and

displaying the difference.

18. The method according to claim 1 , wherein:

the calculating of the first index value includes:

selecting one of the images or a mean image of the images as a representative image; and

extracting an inspection area from the representative image.

19. The method according to claim 8 , wherein:

the model-based model is generated by machine learning.