Apparatus and method for inspecting pattern

• US 20030228049A1
• Filed: 05/22/2003
• Published: 12/11/2003
• Est. Priority Date: 06/11/2002
• Status: Abandoned Application

• Current Assignee: SCREEN Holdings Co., Ltd.
• Original Assignee: SCREEN Holdings Co., Ltd.
• Inventors: Asai, Hiroshi
• Application Number:
  US10/442,957
• Publication Number:
  US 20030228049A1
• Time in Patent Office: Days
• US Class Current: 382/145
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection
• CPC Class Codes: G06T 7/001 using an image reference approach

Abstract

An inspection apparatus (1) comprises an image pickup part (2) for performing an image pickup of a substrate (9), an operation part (4) to which an image signal is inputted from said image pickup part (2) and a computer (5), and the operation part (4) specifies an inspection image and a reference image from an object image acquired by the image pickup part (2). In the operation part (4), a class teaching part (501) generates a teaching image from the inspection image and an image sampling part (502) samples a plurality of pixels from the teaching image while a feature value calculation part (43) calculates pixel feature values from values of corresponding pixels in the inspection image and the reference image. A dataset generation part (503) generates a dataset of pixel feature values and a class on each of the sampled pixels. A classifier construction part (504) performs training with the dataset to generate a defect check condition and the defect check condition is inputted to an inspection result generation part (44). With this operation, the inspection apparatus (1) can efficiently and appropriately detect defects.

Claims

1. An apparatus for inspecting pattern on an object, comprising:

an image pickup device for performing an image pickup of an object to acquire data of multitone inspection image;

a memory for storing data of reference image and classification data obtained by adding a class to each pixel of said inspection image;

a feature value calculation part for calculating a feature value on the basis of a value of each of a plurality of pixels selected from said inspection image and a value of corresponding pixel in said reference image;

a dataset generation part for generating a dataset indicating a combination of a feature value and a class on each of said plurality of pixels; and

a classifier construction part for constructing a classifier by training with said dataset to output a classification result in accordance with an inputted feature value.

2. The apparatus according to claim 1, wherein

pattern on said object have periodicity and said inspection image is part of an object image acquired by said image pickup device, and

said memory stores a region away from said inspection image by an integral multiple of a cycle of said pattern as said reference image.

3. The apparatus according to claim 1, further comprising

a mechanism for moving said object relatively to said image pickup device,

wherein pattern on said object have periodicity, and said inspection image and said reference image are images of regions which are away from each other by an integral multiple of a cycle of said pattern.

4. A method of inspecting pattern on an object, comprising the steps of:

a) preparing data of a multitone inspection image acquired from an object;

b) preparing data of a reference image;

c) preparing classification data obtained by adding a class to each pixel of said inspection image;

d) selecting a plurality of pixels in said inspection image;

e) obtaining a feature value on the basis of a value of each of said plurality of pixels and a value of corresponding pixel in said reference image;

f) generating a dataset indicating a combination of a feature value and a class on each of said plurality of pixels;

g) constructing a classifier by training with said dataset to output a classification result in accordance with an inputted feature value;

h) preparing another data of an inspection image and a reference image;

i) selecting one pixel in said inspection image;

j) obtaining a feature value on the basis of a value of said one pixel and a value of corresponding pixel in said reference image; and

k) acquiring a classification result by inputting said feature value to said classifier.

5. The method according to claim 4, wherein

said classification data is data of an image which is obtained by adding a pixel value indicating defective or a pixel value indicating non-defective to each pixel.

6. The method according to claim 4, wherein

said feature value is calculated on the basis of at least one of a differential image between an inspection image and a reference image, a normalized image which is obtained by normalizing pixel values of said differential image with a differential statistics feature value and an image which is obtained by smoothing said normalized image in said steps e) and j).

7. The method according to claim 4, wherein

pattern on said object have periodicity,

part of an object image acquired from said object is specified as said inspection image in said step a), and

a region away from said inspection image by an integral multiple of a cycle of said pattern is specified as said reference image in said step b).

8. The method according to claim 4, wherein

pattern on said object have periodicity, and

an image of a region away from a region on said object corresponding to said inspection image by an integral multiple of a cycle of said pattern is regarded as said reference image in said step b).

9. The method according to claim 4, wherein

said reference image is a golden template image in said step b).

10. The method according to claim 4, wherein

a plurality of reference images are prepared before said step (e) and a new reference image is generated from said plurality of reference images.

11. The method according to claim 4, wherein

said step g) comprises the steps of:

g1) generating a training dataset and an evaluation dataset from said dataset;

g2) generating a classifier by using said training dataset;

g3) evaluating said classifier by inputting said evaluation dataset to said classifier; and

g4) correcting said training dataset on the basis of an evaluation result.

12. The method according to claim 11, wherein

said step g) further comprises the step of

repeating said steps g2) to g4).

13. The method according to claim 4, wherein

said classifier is a decision tree.

14. The method according to claim 4, wherein

said classifier is a neural network or a function tree.

15. The method according to claim 4, wherein

a plurality of classifiers are constructed in said step g), and

selection is made among a plurality of classification results acquired by said plurality of classifiers in said step k).

16. A computer-readable recording medium carrying a program for inspection of pattern on an object, wherein execution of said program by a computer causes said computer to perform the steps of:

a) preparing data of a multitone inspection image acquired from an object;

b) preparing data of a reference image;

c) preparing classification data obtained by adding a class to each pixel of said inspection image;

d) selecting a plurality of pixels in said inspection image;

e) obtaining a feature value on the basis of a value of each of said plurality of pixels and a value of corresponding pixel in said reference image;

f) generating a dataset indicating a combination of a feature value and a class on each of said plurality of pixels; and

g) constructing a classifier by training with said dataset to output a classification result in accordance with an inputted feature value.

17. The computer-readable recording medium according to claim 16, wherein

said classification data is data of an image which is obtained by adding a pixel value indicating defective or a pixel value indicating non-defective to each pixel.

18. The computer-readable recording medium according to claim 16, wherein

said feature value is calculated on the basis of at least one of a differential image between said inspection image and said reference image, a normalized image which is obtained by normalizing pixel values of said differential image with a differential statistics feature value and an image which is obtained by smoothing said normalized image in said step e).

19. The computer-readable recording medium according to claim 16, wherein

pattern on said object have periodicity,

part of an object image acquired from said object is specified as said inspection image in said step a), and

a region away from said inspection image by an integral multiple of a cycle of said pattern is specified as said reference image in said step b).

20. The computer-readable recording medium according to claim 16, wherein

pattern on said object have periodicity, and

an image of a region away from a region on said object corresponding to said inspection image by an integral multiple of a cycle of said pattern is regarded as said reference image in said step b).

21. The computer-readable recording medium according to claim 16, wherein

said reference image is a golden template image in said step b).

22. The computer-readable recording medium according to claim 16, wherein

a plurality of reference images are prepared before said step e) and a new reference image is generated from said plurality of reference images.

23. The computer-readable recording medium according to claim 16, wherein

said step g) comprises the steps of:

g1) generating a training dataset and an evaluation dataset from said dataset;

g2) generating a classifier by using said training dataset;

g3) evaluating said classifier by inputting said evaluation dataset to said classifier; and

g4) correcting said training dataset on the basis of an evaluation result.

24. The computer-readable recording medium according to claim 23, wherein

said step g) further comprises the step of

repeating said steps g2) to g4).

25. The computer-readable recording medium according to claim 16, wherein

said classifier is a decision tree.

26. The computer-readable recording medium according to claim 16, wherein

said classifier is a neural network or a function tree.

27. The computer-readable recording medium according to claim 16, wherein execution of said program by said computer causes said computer to further perform the steps of:

h) preparing another data of an inspection image and a reference image;

i) selecting one pixel in said inspection image;

j) obtaining a feature value on the basis of a value of said one pixel and a value of corresponding pixel in said reference image; and

k) acquiring a classification result by inputting said feature value to said classifier.

28. The computer-readable recording medium according to claim 27, wherein

a plurality of classifiers are constructed in said step g), and

selection is made among a plurality of classification results acquired by said plurality of classifiers in said step k).