Apparatus and method for detecting defect on object

• US 20060078191A1
• Filed: 09/06/2005
• Published: 04/13/2006
• Est. Priority Date: 09/29/2004
• Status: Abandoned Application

• Current Assignee: SCREEN Holdings Co., Ltd.
• Original Assignee: SCREEN Holdings Co., Ltd.
• Inventors: Matsumura, Akira
• Application Number:
  US11/218,775
• Publication Number:
  US 20060078191A1
• Time in Patent Office: Days
• US Class Current: 382/149
• CPC Class Codes: G01N 2021/8854 Grading and classifying of flaws
• CPC Class Codes: G01N 2021/8896 Circuits specially adapted for system specific signal conditioning
• CPC Class Codes: G01N 21/8851 Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges G01N21/8806 and G01N21/93 - G01N21/95692 take precedence; optical measurement of dimensions G01B11/00; optical scanning G02B26/10; image transformation G06T3/00; computerised image enhancement G06T5/00; image processing per se for flaw detection G06T7/0002
• 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 21/95607 using a comparative method
• CPC Class Codes: G06T 2207/30141 Printed circuit board [PCB]
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/001 using an image reference approach

Abstract

In a defect detection apparatus, data of an inspection image and that of a reference image are inputted from an image pickup part (3) to an operation part (50), and a differential image is thereby generated in a differential image generation part (52) and an image representing a defect inclusion area which includes a defect in an area image generation part (51) as an image which has less information on a false defect and shape of a defect than information on those in the differential image. A first evaluation part (53) performs a provisional evaluation on whether a defect candidate in an area of the differential image which corresponds to the defect inclusion area is true or false. A second evaluation part (54) determines the type of feature values to be obtained from the defect candidate in accordance with a result of provisional evaluation performed by the first evaluation part (53) to obtain the feature values of the defect candidate and performs an evaluation on whether the defect candidate is true or false on the basis of the feature values. With this construction, it is possible to detect a defect on a substrate (9) with high accuracy and high efficiency.

Claims

1. An apparatus for detecting a defect on an object, comprising:

an image pickup part for picking up an image of an object to acquire a grayscale inspection image;

a first image generation part for generating a differential image between said inspection image and a grayscale reference image;

a second image generation part for generating an image representing a defect inclusion area which includes a defect, as an image which has less information on a false defect and shape of a defect than information on those in said differential image;

a first evaluation part for performing a provisional evaluation on whether a defect candidate in an area of said differential image which corresponds to said defect inclusion area is true or false; and

a second evaluation part for determining at least one type of feature value which is obtained from said defect candidate in accordance with a result of provisional evaluation performed by said first evaluation part and performing an evaluation on whether said defect candidate is true or false on the basis of said feature value of said defect candidate.

2. The apparatus according to claim 1, wherein

said first evaluation part substantially compares a value on the basis of a standard deviation of values of pixels in said differential image with values of pixels included in said defect candidate to perform a provisional evaluation on whether said defect candidate is true or false.

3. The apparatus according to claim 2, wherein

said first evaluation part substantially compares a value on the basis of said standard deviation with a value of each pixel in an area of said differential image which corresponds to said defect inclusion area to specify said defect candidate.

4. The apparatus according to claim 1, wherein

said at least one type of feature value includes geometric feature values of a defect candidate.

5. The apparatus according to claim 1, wherein

said at least one type of feature value includes feature values of higher order local autocorrelations.

6. The apparatus according to claim 1, wherein

said at least one type of feature value includes feature values on the basis of a density gradient.

7. The apparatus according to claim 1, wherein

said second evaluation part comprises a checker construction part for constructing a checker which outputs a check result obtained from said feature value, by learning.

8. A method for detecting a defect on an object, comprising the steps of:

a) acquiring a grayscale inspection image of an object;

b) generating a differential image between said inspection image and a grayscale reference image;

c) generating an image representing a defect inclusion area which includes a defect, as an image which has less information on a false defect and shape of a defect than information on those in said differential image;

d) performing a provisional evaluation on whether a defect candidate in an area of said differential image which corresponds to said defect inclusion area is true or false;

e) determining at least one type of feature value which is obtained from said defect candidate in accordance with a result of said provisional evaluation; and

f) obtaining said feature value of said defect candidate and performing an evaluation on whether said defect candidate is true or false on the basis of said feature value.

9. The method according to claim 8, wherein

a value on the basis of a standard deviation of values of pixels in said differential image is substantially compared with values of pixels included in said defect candidate to perform a provisional evaluation on whether said defect candidate is true or false in said step d).

10. The method according to claim 9, wherein

a value on the basis of said standard deviation is substantially compared with a value of each pixel in an area of said differential image which corresponds to said defect inclusion area to specify said defect candidate in said step d).

11. The method according to claim 8, wherein

said at least one type of feature value includes geometric feature values of a defect candidate.

12. The method according to claim 8, wherein

said at least one type of feature value includes feature values of higher order local autocorrelations.

13. The method according to claim 8, wherein

said at least one type of feature value includes feature values on the basis of a density gradient.

14. The method according to claim 8, wherein

a checker is constructed by learning, and

said feature value is inputted to said checker to perform an evaluation on whether said defect candidate is true or false in said step f).