System and method for inspecting a mask for EUV lithography

1 . A system for inspecting a mask for EUV lithography, comprising

1.1. a first partial system for optical inspection of a mask for identifying and/or localizing potential mask defects, wherein the first partial system comprises a first optical system for generating at least one first image of at least a portion of the mask, wherein the first optical system comprises an illumination device for generating radiation for illuminating the at least a portion of the mask, a projection device for projecting radiation from the at least a portion of the mask, and an imaging device for capturing the at least one first image of the at least a portion of the mask, wherein the first partial system is configured to identify and/or localize the potential mask defects based on the at least one first image of the mask generated by the first optical system,

1.2. a second partial system for pre-classification of the potential mask defects, wherein the second partial system comprises a second optical system for generating at least one second image of at least a portion of the mask, wherein the second optical system comprises an illumination device for generating radiation for illuminating the at least a portion of the mask, a projection device for projecting radiation from the at least a portion of the mask, and an imaging device for capturing the at least one second image of the at least a portion of the mask, wherein the second partial system is configured to pre-classify the potential mask defects based at least in part on the at least one second image of the mask generated by the second optical system, and

1.3. a third partial system for checking the potential mask defects,

1.4. wherein the second partial system is embodied in such a way that it assigns to the potential mask defects identified and/or localized by the first partial system a confidence parameter for characterizing the reliability of the identification and/or for characterizing the relevance of the defect to subsequent applications, and

1.5. wherein the third partial system is controllable in such a way that a subset of the mask defects identified and/or localized by the first partial system is checked on the basis of the confidence parameter assigned to the potential mask defects by the second partial system;

wherein the second partial system has a classification speed v 2 and the third partial system has a checking speed v 3 , wherein the following applies: v 2 >v 3 .

2 . The system of claim 1 , wherein the second partial system for pre-classification of the potential mask defects utilizes an automated image analysis method.

3 . The system of claim 1 , wherein the second partial system for pre-classification of the potential mask defects utilizes machine learning.

4 . The system of claim 1 , wherein the second partial system has a memory for storing a database with pre-classified mask defects.

5 . The system of claim 1 , wherein the second partial system is embodied in such a way that it divides the potential mask defects into two, three or more classes.

6 . The system of claim 1 , wherein the first partial system for inspection of the mask utilizes illumination radiation at a wavelength of longer than 30 nm.

7 . The system of claim 1 , wherein the second partial system for pre-classification of the potential mask defects and/or the third partial system for checking a subset of the potential mask defects comprises an optical system with illumination radiation at a wavelength in the EUV range.

8 . A method for inspecting a mask for EUV lithography, comprising the following steps:

8.1. providing a mask for EUV lithography,

8.2. a first inspection step for recording a first image of the provided mask and for identifying and/or localizing potential mask defects in the provided mask, including using a first optical system to generate the first image, wherein the first optical system comprises an illumination device for generating radiation for illuminating the provided mask, a projection device for projecting radiation from the provided mask, and an imaging device for recording the first image of the provided mask, wherein the identifying and/or localizing potential mask defects comprises identifying and/or localizing potential mask defects based on the first image,

8.3. a second inspection step for pre-classifying the potential mask defects into at least two nonempty subsets, including using a second optical system to generate a second image of the provided mask, wherein the second optical system comprises an illumination device for generating radiation for illuminating the provided mask, a projection device for projecting radiation from the provided mask, and an imaging device for recording the second image of the provided mask, wherein the pre-classifying the potential mask defects comprises pre-classifying the potential mask defects based on the second image, and

8.4. a third inspection step for checking the potential mask defects in one of the subsets ascertained in the second inspection step,

8.5. wherein the second inspection step for pre-classifying the potential mask defects comprises a method based on machine learning,

wherein the second inspection step has a classification speed v 2 and the third inspection step has a checking speed v 3 , wherein the following applies: v 2 >v 3 .

9 . The method of claim 8 , wherein a distribution of one-dimensional and/or two-dimensional structures in the image of the mask recorded in the first inspection step is analyzed for the purposes of pre-classifying the potential mask defects.

10 . The method of claim 8 , wherein an intensity distribution in an image of the mask recorded in the first inspection step is analyzed for the purposes of pre-classifying the potential mask defects.

11 . The method of claim 8 , wherein the second inspection step comprises a comparison step for comparing the recorded image of the mask with data in a database.

12 . The method of claim 8 , wherein the third inspection step comprises an actinic method.

13 . The method of claim 8 , wherein the first inspection step comprises a non-actinic method.

14 . The system of claim 2 , wherein the second partial system for pre-classification of the potential mask defects utilizes machine learning.

15 . The system of claim 2 , wherein the second partial system has a memory for storing a database with pre-classified mask defects.

16 . The system of claim 2 , wherein the second partial system is embodied in such a way that it divides the potential mask defects into two, three or more classes.

17 . The method of claim 9 , wherein an intensity distribution in an image of the mask recorded in the first inspection step is analyzed for the purposes of pre-classifying the potential mask defects.

18 . The method of claim 9 , wherein the second inspection step comprises a comparison step for comparing the recorded image of the mask with data in a database.

19 . The system of claim 1 , wherein the second partial system comprises a second optical system for generating at least one second image of at least a portion of the mask, wherein the second partial system is configured to pre-classify the potential mask defects based at least in part on the at least one second image of the mask generated by the second optical system.

20 . The system of claim 19 , wherein the third partial system comprises a third optical system for generating at least one third image of at least a portion of the mask, wherein the third partial system is configured to check the potential mask defects based at least in part on the at least one third image of the mask generated by the third optical system.

21 . The system of claim 20 , wherein the third optical system is the same as the second optical system.

22 . The system of claim 21 , wherein the first optical system comprises a deep ultraviolet (DUV) optical system that uses deep ultraviolet light to illuminate the mask, and the second optical system comprises an extreme ultraviolet (EUV) optical system that uses extreme ultraviolet light to illuminate the mask.

23 . The system of claim 19 , wherein the first optical system comprises a deep ultraviolet (DUV) optical system that uses deep ultraviolet light to illuminate the mask, and the second optical system comprises an extreme ultraviolet (EUV) optical system that uses extreme ultraviolet light to illuminate the mask.