Method for analyzing defect data and inspection apparatus and review system

• US 8,086,422 B2
• Filed: 12/22/2008
• Issued: 12/27/2011
• Est. Priority Date: 04/10/2001
• Status: Expired due to Fees

• Current Assignee: Hitachi High-Technologies Corporation (Hitachi, Ltd.), Hitachi, Ltd.
• Original Assignee: Hitachi High-Technologies Corporation (Hitachi, Ltd.), Hitachi, Ltd.
• Inventors: Shibuya, Hisae, Takagi, Yuji
• Primary Examiner(s): Charioui, Mohamed
• Application Number:
  US12/341,657
• Publication Number:
  US 20090105990A1
• Time in Patent Office: 1,100 Days
• Field of Search: 702/58, 702/59, 702/69, 702/90, 702/118, 702/137, 702/150, 702/179, 702/183, 702/185, 716/4, 324/718
• US Class Current: 702/183
• CPC Class Codes: G01N 21/9501 Semiconductor wafers manufacturing processes per se of semiconductor devices implementing a measuring step H01L22/10
• CPC Class Codes: G01N 21/956 Inspecting patterns on the surface of objects contactless testing of electronic circuits G01R31/308; testing currency G07D; manufacturing processes per se of semiconductor devices implementing a measuring step H01L22/10
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/0006 using a design-rule based approach
• CPC Class Codes: G06T 7/001 using an image reference approach

Abstract

The distribution states of defects are analyzed on the basis of the coordinates of defects detected by an inspection apparatus to classify them into a distribution feature category, or any one of repetitive defect, congestion defect, linear distribution defect, ring/lump distribution defect and random defect. In the manufacturing process for semiconductor substrates, defect distribution states are analyzed on the basis of defect data detected by an inspection apparatus, thereby specifying the cause of defect in apparatus or process.

Claims

1. A method of analyzing defect data obtained from inspection of defects of a semiconductor substrate in an apparatus for analyzing defect data, comprising:

generating a defect distribution map using information of positions of defects of the semiconductor substrate detected by an inspection apparatus; and

classifying said defects that exist on said generated defect distribution map into one of a plurality of defect categories including a congestion defect, a linear distribution defect, a ring/lump distribution defect and a random distribution defect through at least a discriminating step by using distance between adjacent defects and the local density of defects calculated from said defect distribution map.

2. A method of analyzing defect data according to claim 1, wherein, in the step of classifying, said ring/lump distribution defect is classified by grouping defects on said defect distribution map based on a distribution density of said defects and calculating a degree of coincidence between said grouped defects and a plurality of geometric patterns.

3. A method of analyzing defect data according to claim 2, wherein said geometric patterns include a ring pattern and a part of the ring pattern.

4. A method of analyzing defect data according to claim 1, wherein the distance between adjacent defects and the local density of defects are calculated by a Voronoi diagram which is generated based on position coordinates of said defects on said defects distribution map.

5. A method of analyzing defect data according to claim 4, wherein the random distribution defect is extracted after classification of the congestion defect, the linear distribution defect and the ring/lump defect.

6. A method of analyzing defect data according to claim 1, wherein the random distribution defect is extracted after classification of the congestion defect, the linear distribution defect and the ring/lump defect.

7. An apparatus for analyzing defect data, comprising:

a first processor configured to generate a defect distribution map using information of positions of defects detected by an inspection apparatus; and

a second processor configured to classify said defects that exist on said generated defect distribution map into one of a congestion defect, a linear distribution defect, a ring/lump distribution defect and a random distribution defect through at least a discriminating step by using, distance between adjacent defects and the local density of defects calculated from said defect distribution map generated by the first processor.

8. An apparatus for analyzing defect data according to claim 7, wherein said second processor is configured to classify said ring/lump distribution defect by grouping defects on said defect distribution map based on a distribution density of said defects and calculating a degree of coincidence between said grouped defects and a plurality of geometric patterns.

9. An apparatus for analyzing defect data according to claim 8, wherein said geometric patterns include a ring pattern and a part of the ring pattern.

10. An apparatus for analyzing defect data according to claim 7, wherein the distance between adjacent defects and the local density of defects are calculated by a Voronoi diagram which is generated based on position coordinates of said defects on said defect distribution map.

11. An apparatus of analyzing defect data according to claim 10, wherein the random distribution defect is extracted after classification of the congestion defect, the linear distribution defect and the ring/lump defect.

12. An apparatus of analyzing defect data according to claim 7, wherein the random distribution defect is extracted after classification of the congestion defect, the linear distribution defect and the ring/lump defect.

13. A method of analyzing defect data obtained from inspection of defects of a semiconductor substrate in an apparatus for analyzing defect data, comprising:

generating a defect distribution map with positions of all defects by using position information of defects of the semiconductor substrate detected by an inspection apparatus;

calculating distance between adjacent defects and a Voronoi region area by using a closest point Voronoi diagram corresponding to said defect distribution map; and

discriminating a congestion defect through at least a discriminating step using said distance between the adjacent defects;

discriminating a linear distribution defect through at least a discriminating step using said distance between the adjacent defects and said Voronoi region area;

discriminating a ring/lump distribution defect through at least a discriminating step using said distance between the adjacent defects and said Voronoi region area; and

extracting another defect other than said congestion defect, said linear distribution defect and said ring/lump distribution defect.

14. A method of analyzing defect data according to claim 13, wherein said another defect is a random distribution defect.

15. A method of analyzing defect data according to claim 14, wherein the random distribution defect is extracted after classification of the congestion defect, the linear distribution defect and the ring/lump defect.

16. A method of analyzing defect data according to claim 13, including a step of classifying said ring/lump distribution defect by grouping defects on said defect distribution map based on a distribution density of said defects and calculating a degree of coincidence between said grouped defects and a plurality of geometric patterns.

17. A method of analyzing defect data according to claim 16, wherein said geometric patterns include a ring pattern and a part of the ring pattern.

18. An apparatus for analyzing defect data, comprising:

a defect distribution map generator configured to generate a defect distribution map with positions of all defects by using position information of defects detected by an inspection apparatus;

a calculator configured to calculate distances between adjacent defects and a Voronoi region area by using a closest point Voronoi diagram corresponding to said defect distribution map; and

a processor configured to classify said defects by discriminating a congestion defect through at least a discriminating step using said distances between the adjacent defects, by discriminating a linear distribution defect through at least a discriminating step using said distance between the adjacent defects and said Voronoi region area, by discriminating a ring/lump distribution defect through at least a discriminating step using said distance between the adjacent defect and said Voronoi region area and by extracting another defect other than said congestion defect, said linear distribution defect and said ring/lump distribution defect.

19. An apparatus for analyzing defect data according to claim 18, wherein said another defect is a random distribution defect.

20. An apparatus of analyzing defect data according to claim 19, wherein the random distribution defect is extracted after classification of the congestion defect, the linear distribution defect and the ring/lump defect.

21. An apparatus for analyzing defect data according to claim 18, wherein said processor is configured to classify said ring/lump distribution defect by grouping defects on said defect distribution map based on a distribution density of said defects and calculating a degree of coincidence between said grouped defects and a plurality of geometric patterns.

22. An apparatus for analyzing defect data according to claim 21, wherein said geometric patterns include a ring pattern and a part of the ring pattern.