Pattern evaluation system, pattern evaluation method and program

• US 20030059104A1
• Filed: 09/24/2002
• Published: 03/27/2003
• Est. Priority Date: 09/26/2001
• Status: Active Grant

• Current Assignee: Kioxia Corporation
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Mitsui, Tadashi
• Granted Patent: US 7,274,820 B2
• Time in Patent Office: Days
• US Class Current: 382/145
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume

Abstract

a pattern evaluation system which receives image data of a pattern to be evaluated to evaluate the pattern includes an edge model producing part which produces a pattern edge model and an edge point coordinate detecting part which carries out an image matching processing to an image of the pattern with the pattern edge model to detect coordinates of an edge point of the pattern.

Claims

1. A pattern evaluation system which receives image data of a pattern to be evaluated to evaluate the pattern, said pattern evaluation system comprising:

an edge model producing part which produces a pattern edge model; and

an edge point coordinate detecting part which carries out an image matching processing to an image of the pattern with said pattern edge model to detect coordinates of an edge point of the pattern.

2. A pattern evaluation system according to claim 1, wherein said pattern edge model is produced in a form of an array of cells, said array having a relative intensity value stored therein and a position of an edge point being assigned thereto, the position of the edge point being defined as a coordinate value.

3. A pattern evaluation system according to claim 1, wherein said edge model producing part produces said pattern edge model using the image of the pattern.

4. A pattern evaluation system according to claim 1, wherein said edge model producing part produces said pattern edge model corresponding to a magnification in measurement of the image of the pattern.

5. A pattern evaluation system according to claim 1, wherein the pattern has a plurality of edge shapes,

said edge model producing part produces a plurality of said pattern edge models, and

said edge point coordinate detecting part detects coordinates of a group of edge points of the edge shape corresponding to each of the plurality of said pattern edge models.

6. A pattern evaluation system according to claim 2, wherein the positions of a plurality of said edge points are assigned to said pattern edge model, and

said edge point coordinate detecting part detects coordinates of said edge points of the pattern corresponding to each of said positions of said plurality of edge points.

7. A pattern evaluation system according to claim 1, wherein said edge point coordinate detecting part defines a value, which is obtained by an image processing capable of being defined for every pixel, as an edge parameter and adopts a teacher data from said edge parameter to calculate a Mahalanobis distance from said teacher data with respect to each pixel, said value including at least one of an intensity value of a pixel of an edge point assigned by a user, a dispersion value, and a matching score to said pattern edge model.

8. A pattern evaluation method comprising:

acquiring an image data of a pattern to be evaluated;

producing a pattern edge model; and

carrying out an image matching processing to an image of the pattern with said pattern edge model to detect coordinates of an edge point of the pattern.

9. A pattern evaluation method according to claim 8, wherein producing said pattern edge model includes assigning an array of cells, relative intensity values and a position of an edge point defined by coordinate values, each of the relative intensity values being stored in a corresponding one of said cells.

10. A pattern evaluation method according to claim 8, wherein said pattern edge model is produced using an image of the pattern.

11. A pattern evaluation method according to claim 8, wherein said pattern edge model is produced in response to a magnification in measurement of an image of the pattern.

12. A pattern evaluation method according to claim 8, wherein the pattern has a plurality of edge shapes,

a plurality of said edge model are produced, and

detecting said edge point coordinate includes detecting coordinates of a group of edge points in said edge shapes corresponding to each of said plurality of pattern edge models.

13. A pattern evaluation method according to claim 9, wherein a plurality of the positions of said edge points are assigned to said pattern edge model, and

detecting said edge point coordinate includes detecting coordinates of said edge points of the pattern which correspond to each of said plurality of positions of said edge points assigned to said pattern edge model.

14. A pattern evaluation method according to claim 8, which further comprises:

defining a value, which is obtained by an image processing capable of being defined for every pixel, as an edge parameter, said value including at least one of an intensity value of a pixel of an edge point assigned by a user, a dispersion value, and a matching score with said pattern edge model;

adopting a teacher data from said edge parameter; and

calculating a Mahalanobis distance from said teacher data with respect to each pixel.

15. A program which causes a computer to execute a pattern evaluation method comprising:

acquiring an image data of a pattern to be evaluated;

producing a pattern edge model; and

carrying out an image matching processing to an image of the pattern with said pattern edge model to detect coordinates of an edge point of the pattern.

16. A program according to claim 15, wherein producing said pattern edge model includes assigning an array of cells, relative intensity values and a position of an edge point defined by coordinate values, each of the relative intensity values being stored in a corresponding one of said cells.

17. A program according to claim 15, wherein said pattern edge model is produced using an image of the pattern.

18. A program according to claim 15, wherein said pattern edge model is produced in response to a magnification in measurement of an image of the pattern.

19. A program according to claim 15, wherein the pattern has a plurality of edge shapes,

a plurality of said edge model are produced, and

detecting said edge point coordinate includes detecting coordinates of a group of edge points in said edge shapes corresponding to each of said plurality of pattern edge models.

20. A program according to claim 16, wherein a plurality of the positions of said edge points are assigned to said pattern edge model, and

detecting said edge point coordinate includes detecting coordinates of said edge points of the pattern which correspond to each of said plurality of positions of said edge points assigned to said pattern edge model.