Source: https://patents.google.com/patent/US20100150426A1/en
Timestamp: 2020-02-18 13:34:52
Document Index: 98459972

Matched Legal Cases: ['art 531', 'art 50', 'art 521', 'art 522', 'art 54', 'art 50', 'art 523', 'art 521', 'art 522', 'art 54']

US20100150426A1 - Apparatus and method for inspecting pattern - Google Patents
US20100150426A1
US20100150426A1 US12/710,844 US71084410A US2010150426A1 US 20100150426 A1 US20100150426 A1 US 20100150426A1 US 71084410 A US71084410 A US 71084410A US 2010150426 A1 US2010150426 A1 US 2010150426A1
US12/710,844
2004-09-29 Priority to JPP2004-283003 priority Critical
2004-09-29 Priority to JP2004283003A priority patent/JP2006098151A/en
2005-09-27 Priority to US11/235,288 priority patent/US7689029B2/en
2010-02-23 Application filed by Screen Holdings Co Ltd filed Critical Screen Holdings Co Ltd
2010-02-23 Priority to US12/710,844 priority patent/US20100150426A1/en
2010-06-17 Publication of US20100150426A1 publication Critical patent/US20100150426A1/en
238000007689 inspection Methods 0 abstract claims description 147
Discussion will be made on a basic method for obtaining an autocorrelation feature value. FIG. 6 is a view illustrating an exemplary autocorrelation matrix 81 used in obtaining an autocorrelation feature value. In the autocorrelation matrix 81 of FIG. 6, the elements are arranged in 3×3 (3 rows and 3 columns), where the value “1” is assigned to the center element and the element adjacent to the upper of this element and the value “0” is assigned to the other elements. In the feature value calculation part 531, the center element of the autocorrelation matrix 81 is overlapped to one pixel in the masked inspection image 611 (hereinafter, referred to as a “target pixel”) which is an image to be operated, to obtain a product of values of pixels in the masked inspection image 611 corresponding to the elements of the value “1” in the autocorrelation matrix 81. Then, a new value of the target pixel is determined by dividing the obtained value by a predetermined value such as the average pixel value, the largest pixel value or the like of inspection image 611.
FIGS. 7A, 7B and 7C may be used as other examples of autocorrelation matrixes. In the autocorrelation matrixes 81A, 81B and 81C of FIGS. 7A to 7C, an autocorrelation feature value which indicates a feature with the directivities on the directions of 0°, 45° and 135° transversely can be obtained, respectively.
In the operation part 50 a, when the defect candidate image 71 is generated (FIG. 4: Step S12), concurrently with masking the inspection image 61 in the inspection image masking part 521 (Step S13), the reference image 62 (when two reference images are acquired, any one of reference images or one new reference image which is generated by calculating the average of values of corresponding pixels in the two reference images) is masked by the reference image masking part 522 with the defect candidate image 71, to obtain a masked reference image 621 as shown in the right of lower position of FIG. 5 (Step 513 a). Then, an autocorrelation feature value of each defect candidate area 7 is obtained from the masked inspection image 611 and an autocorrelation feature value of each defect candidate area 7 is also obtained from the masked inspection image 621 (FIG. 4: Step S14). In the classifying part 54, the two autocorrelation feature values of each defect candidate area 7 obtained from the two masked images 611 and 612 are inputted to the classifier 541, to classify a class of defect candidate (Step S15).
In the pattern inspection apparatus 1 with the operation part 50 e of FIG. 14, the differential image, the inspection image and the reference image are masked with the defect candidate image in the differential image masking part 523, the inspection image masking part 521 and the reference image masking part 522, respectively (FIG. 12: Step S22, FIG. 4: Step S13, FIG. 9: Step 513 a) to obtain a masked differential image, a masked inspection image and a masked reference image, and autocorrelation feature values of each defect candidate area are obtained from the masked differential image, the masked inspection image and the masked reference image, respectively (FIG. 4: Step S14). In the classifying part 54, a classification of defect candidate is performed on the basis of the autocorrelation feature value obtained from the masked reference image in addition to the autocorrelation feature values obtained from the masked differential image and the masked inspection image. It is therefore possible to classify classes of defect candidates with high accuracy.
While the autocorrelation matrix in which the elements are arranged in 3×3 is used in the preferred embodiments, by using an autocorrelation matrix representing large area in which elements are arranged in 5×5, 9×9 or the like, high-level texture analysis or the like may be performed. In this case, it is preferable to enlarge a defect candidate area where a feature value is extracted by performing a larger dilation or the like on the defect candidate area in the operation of the inspection part.
In the preferred embodiment, the inspection is performed to pattern formed on a semiconductor substrate, but the pattern inspection apparatus can be utilized to inspect pattern formed on, for example, a printed circuit board, a glass substrate for manufacturing a flat panel display or the like. An object inspected by the pattern inspection apparatus may be something other than the substrate.
1. An apparatus for inspecting pattern on an object, comprising:
a differential image generator for generating a differential image representing a difference between a grayscale inspection image representing pattern on an object and a grayscale reference image or a difference between two images obtained from said inspection image and said reference image, respectively, or a differential image obtained from an image representing said difference;
a defect candidate image generator for generating a defect candidate image representing an area which includes a defect candidate in said inspection image by comparing said inspection image with said reference image;
a differential image masking part for masking said differential image with said defect candidate image to obtain a masked differential image;
a feature value calculation part for obtaining an autocorrelation feature value from said masked differential image; and
a classifying part for performing a classification of said defect candidate on the basis of said autocorrelation feature value.
an inspection image masking part for masking said inspection image with said defect candidate image to obtain a masked inspection image, wherein
said classification is also based on an autocorrelation feature value obtained from said masked inspection image.
a reference image masking part for masking said reference image with said defect candidate image to obtain a masked reference image, wherein
said classification is also based on an autocorrelation feature value obtained from said masked reference image.
said classifying part comprises a classifier construction part for constructing a classifier by learning which outputs a classification result in accordance with autocorrelation feature value.
9. A method for inspecting pattern on an object, comprising the steps of:
a) generating a differential image representing a difference between a grayscale inspection image representing pattern on an object and a grayscale reference image or a difference between two images obtained from said inspection image and said reference image, respectively, or a differential image obtained from an image representing said difference;
b) generating a defect candidate image representing an area which includes a defect candidate in said inspection image by comparing said inspection image with said reference image;
c) masking said differential image with said defect candidate image to obtain a masked differential image;
d) obtaining an autocorrelation feature value from said masked differential image; and
e) performing a classification of said defect candidate on the basis of said autocorrelation feature value.
masking said inspection image with said defect candidate image to obtain a masked inspection image, wherein
masking said reference image with said defect candidate image to obtain a masked reference image, wherein
said classification is performed by a classifier constructed by learning which outputs a classification result in accordance with said autocorrelation feature value.
US12/710,844 2004-09-29 2010-02-23 Apparatus and method for inspecting pattern Abandoned US20100150426A1 (en)
JPP2004-283003 2004-09-29
JP2004283003A JP2006098151A (en) 2004-09-29 2004-09-29 Pattern inspection device and pattern inspection method
US11/235,288 US7689029B2 (en) 2004-09-29 2005-09-27 Apparatus and method for inspecting pattern
US12/710,844 US20100150426A1 (en) 2004-09-29 2010-02-23 Apparatus and method for inspecting pattern
US11/235,288 Division US7689029B2 (en) 2004-09-29 2005-09-27 Apparatus and method for inspecting pattern
US20100150426A1 true US20100150426A1 (en) 2010-06-17
ID=36099142
US11/235,288 Expired - Fee Related US7689029B2 (en) 2004-09-29 2005-09-27 Apparatus and method for inspecting pattern
US12/710,844 Abandoned US20100150426A1 (en) 2004-09-29 2010-02-23 Apparatus and method for inspecting pattern
US (2) US7689029B2 (en)
JP (1) JP2006098151A (en)
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2004-09-29 JP JP2004283003A patent/JP2006098151A/en active Pending
2005-09-27 US US11/235,288 patent/US7689029B2/en not_active Expired - Fee Related
2010-02-23 US US12/710,844 patent/US20100150426A1/en not_active Abandoned
US20060067570A1 (en) 2006-03-30
JP2006098151A (en) 2006-04-13
US7689029B2 (en) 2010-03-30