CN112767398A - Method and device for detecting wafer defects - Google Patents
Method and device for detecting wafer defects Download PDFInfo
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Abstract
The application relates to a method and a device for detecting wafer defects. The method comprises the following steps: image areas corresponding to different defect forms on the wafer to be detected are identified through a template matching algorithm, so that characteristic parameters are extracted according to detection images of different image areas, the extracted characteristic parameters can reliably represent the wafer to be detected, threshold judgment is carried out on the extracted characteristic parameters, and a defect detection result of the wafer to be detected is obtained according to a judgment result. According to the scheme provided by the application, the defect type can be identified, and the station abnormity in the wafer production process can be calculated according to the mapping relation, so that an instructive improvement suggestion is provided for the wafer process.
Description
Technical Field
The present application relates to the field of computer vision technologies, and in particular, to a method and an apparatus for detecting wafer defects.
Background
In the global semiconductor market, more than 95% of semiconductor devices and more than 99% of integrated circuits are fabricated using high purity wafers. Millions of transistors are etched on the wafer, and the transistors are hundreds of times finer than human hair, so that the requirement for precision in wafer detection is high. As a core part of a semiconductor device, the quality of a wafer plays a decisive role in whether the semiconductor device can work normally, so that the defect inspection of the wafer plays an important role in the production of semiconductors, and the finding of an efficient and accurate wafer defect detection method is a hot topic in the semiconductor industry.
At present, the internal defects of the wafer are generally judged by testing the electrical performance, the method can only detect whether the defects exist on the wafer, but can not detect different defect types, so that the processing problems of sites such as yellow light, etching or cutting in the wafer processing can not be intuitively reflected, instructive improvement suggestions can not be generated in the wafer production process, and compared with an optical detection method, the method also has the advantages of high efficiency and low cost.
The patent document CN505153093A discloses a method for detecting defects of a wafer to be tested by using a residual image of an image of the wafer to be tested and a template image, but the method has the following defects:
1. according to the scheme, the defect judgment is carried out by utilizing the gray level image and the template image of the whole wafer, the area which is larger than the gray level residual error threshold value is judged to be a defect, and the defect type is not distinguished;
2. the scheme only depends on the gray characteristic to detect the defect, and the reliability of the detection result is low.
Disclosure of Invention
In order to overcome the problems in the related art, the application provides a method for detecting the defects of the wafer, and the method can efficiently and accurately detect the defect types of the wafer.
The first aspect of the present application provides a method for detecting wafer defects, including:
collecting an infrared image of a wafer to be detected;
positioning the infrared image by using a template matching algorithm to obtain a target morphological image; the target morphology image includes: newton's ring diagram;
preprocessing the target form image to obtain a target detection image; the target detection image includes: denoising the Newton ring skeleton map;
extracting to obtain detection characteristic parameters based on the target detection image; the detecting the characteristic parameters comprises: the number of Newton ring layers;
and judging a threshold value based on the detection characteristic parameters to obtain the defect type of the wafer to be detected.
In one embodiment, when the target morphology image is a newton ring diagram, the preprocessing the target morphology image to obtain a target detection image includes:
processing the Newton ring graph by using a Laplace operator, and extracting to obtain a Newton ring characteristic graph;
performing framework extraction on the Newton ring characteristic diagram by utilizing a framework extraction algorithm to obtain a Newton ring framework diagram;
and eliminating abnormal contour lines of the Newton ring skeleton map to obtain a de-noised Newton ring skeleton map.
In one embodiment, the extracting, based on the target detection image, a detection feature parameter includes:
calculating central point data of each contour line in the denoising Newton ring skeleton map to obtain a central point data set;
classifying the central point data set by using a k-means clustering algorithm to obtain N clusters; n is a positive integer;
calculating the mean value of all central point data in the cluster P as the central point data of the Newton ring; the cluster P is the cluster with the largest data size in the N clusters;
and establishing a plane coordinate system by taking the central point of the Newton ring as an origin, and calculating the maximum intersection point number of the Newton ring and the positive half shaft of the horizontal shaft of the coordinate system as the number of Newton ring layers.
In one embodiment, the performing a threshold judgment based on the detection characteristic parameter to obtain a defect detection result includes:
and judging whether the number of Newton ring layers is greater than or equal to a Newton ring threshold, if so, judging that the defect type of the wafer to be detected is a bonding defect, and the station abnormity corresponding to the bonding defect is the polishing station abnormity in the manufacturing process of the wafer to be detected.
In one embodiment, the target morphology image further includes: a corner image;
the target detection image further includes: a corner sharpening picture;
when the target form image is a corner image, preprocessing the target form image to obtain a target detection image, including:
and sharpening the corner image to obtain a corner sharpening image.
In one embodiment, the detecting the characteristic parameters further includes: averaging the gray level differences;
the extracting of the detection characteristic parameters based on the target detection image comprises the following steps:
reading the traversal line position information of the corner sharpening graph;
calculating to obtain an average gray value a of the image in the area A according to the position information of the traverse line; the area A is a triangular area formed by a traverse line and an image edge;
calculating to obtain the average gray value B of the B area image according to the traverse line position information; the B area is a trapezoidal area formed by the traverse line and the image edge;
and calculating to obtain an average gray difference based on the average gray values a and b.
In one embodiment, the performing a threshold judgment based on the detection characteristic parameter to obtain a defect detection result includes:
judging whether the average gray difference is larger than a gray difference threshold value or not, if so, judging that the defect type of the wafer to be detected is a corner breaking defect, and judging that the station abnormality corresponding to the corner breaking defect is the cutting station abnormality in the manufacturing process of the wafer to be detected;
if not, reading the traversal line position information of the corner sharpening graph again until the traversal condition is met; the traversal condition comprises the following steps: the average gray difference is larger than a gray difference threshold value or the traverse line position information is overlapped with the image edge position information of the corner sharpening image.
In one embodiment, the target morphology image further includes: a substrate image;
the target detection image further includes: a substrate gray-scale map;
when the target form image is a substrate image, preprocessing the target form image to obtain a target detection image, comprising:
calling a standard substrate image from a standard image library;
and carrying out gray level adjustment on the substrate image based on the standard substrate image to obtain a substrate gray level image.
In one embodiment, the detecting the characteristic parameter further includes: a maximum pixel difference;
the extracting of the detection characteristic parameters based on the target detection image comprises the following steps:
comparing each pixel point in the substrate gray-scale image with the standard substrate image to obtain the pixel difference of each pixel point;
and calculating the maximum value in the pixel difference of each pixel point to be used as the maximum pixel difference.
In one embodiment, in the pixel comparison of each pixel point in the grayscale map with the standard substrate image, the pixel comparison of one pixel point includes:
reading the pixel value m of the pixel point in the substrate gray-scale image;
reading a pixel value n of the pixel point position in the standard substrate image;
and calculating the absolute value of the difference value of the m and the n.
In one embodiment, the performing a threshold judgment based on the detection characteristic parameter to obtain a defect detection result includes:
and judging whether the maximum pixel difference is larger than or equal to a gray scale threshold value, if so, judging that the defect type of the wafer to be detected is an etching defect, and judging that the site abnormality corresponding to the etching defect is the etching site abnormality in the manufacturing process of the wafer to be detected.
The second aspect of the present application provides a wafer defect detection apparatus, including:
the infrared imaging device comprises a halogen lamp, an infrared filter, an infrared lens, an infrared imaging module and an image processing module;
the halogen lamp is used for providing a detection light source;
the infrared filter is used for filtering the detection light source to obtain infrared light with single wavelength;
the infrared lens is used for receiving light rays reflected by the wafer to be detected and transmitting the reflected light rays to the infrared imaging module;
the infrared imaging module is used for receiving the light of the infrared lens and generating an infrared image of the wafer to be detected;
the image processing module includes: a processor and a memory; the memory has stored thereon executable code which, when executed by the processor, causes the processor to perform the method as described above.
The technical scheme provided by the application can comprise the following beneficial effects:
according to the scheme, the image areas corresponding to different defect forms on the wafer to be detected are identified through a template matching algorithm, so that the characteristic parameters are extracted according to the detection images of different image areas, the extracted characteristic parameters can reliably represent the wafer to be detected, and therefore when the characteristic parameters are adopted for defect judgment, an accurate defect detection result can be obtained; in addition, because the defect form, the image area and the characteristic parameters have unique mapping relations, the threshold value judgment is carried out according to the characteristic parameters, whether the wafer to be detected has defects or not can be judged, the defect type can be identified, and the station abnormity in the wafer production process can be calculated according to the mapping relations, so that an instructive improvement suggestion is provided for the wafer process.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application, as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.
Fig. 1 is a first flowchart illustrating a method for detecting a wafer defect according to an embodiment of the present disclosure;
FIG. 2 is a second flowchart of a wafer defect inspection method according to an embodiment of the present disclosure;
fig. 3 is a third flowchart of a wafer defect detection method according to an embodiment of the present disclosure;
FIG. 4 is a fourth flowchart illustrating a method for detecting wafer defects according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an apparatus for detecting a wafer defect according to an embodiment of the present disclosure.
Detailed Description
Preferred embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
The existing wafer defect detection method can only detect whether the wafer has defects or not, but can not detect different defect types, so that the process problems of sites such as yellow light, etching or cutting in the wafer process can not be visually reflected, and instructive improvement suggestions can not be generated in the wafer production process.
Example one
In view of the above problems, embodiments of the present application provide a method for detecting a wafer defect, which can efficiently and accurately detect a defect type of a wafer.
The technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a first flowchart of a wafer defect detection method according to an embodiment of the present disclosure.
Referring to fig. 1, the method for detecting wafer defects includes:
101. collecting an infrared image of a wafer to be detected;
in the embodiment of the application, light reflected by the halogen lamp is filtered by the 1100nm filter plate to obtain 1100nm infrared light, and the infrared imaging module and the infrared lens are used for collecting infrared images of the wafer to be measured under the irradiation of 1100nm external red light.
102. Positioning the infrared image by using a template matching algorithm to obtain a target morphological image;
in an embodiment of the present application, the target morphological image includes: newton ring diagram. In an actual application process, the target morphological image may further include: at least one of a corner image and a substrate image. In the practical application process, the infrared image of the wafer to be measured can be positioned by utilizing the template matching algorithm to obtain the Newton ring diagram, or the Newton ring diagram and the corner image, or the Newton ring diagram and the substrate image, or the Newton ring diagram, the corner image and the substrate image.
It should be noted that, different types of defects of the wafer to be detected can be detected according to the obtained different target form images, so that in the actual application process, the template matching process can be adjusted according to actual production requirements, so as to obtain different target form images, so as to meet different wafer detection requirements, for example, a newton ring diagram and a corner image can be obtained by positioning through a template matching algorithm, so as to perform bonding defect detection and corner collapse detection on the wafer to be detected.
It should be understood that the above description of the target modality image is only an example in the embodiment of the present application, and should not be taken as a limitation of the present invention.
103. Preprocessing the target form image to obtain a target detection image;
in an embodiment of the present application, the target detection image includes: denoising the Newton ring skeleton map; the de-noising Newton ring skeleton map is a target detection image obtained after preprocessing based on the Newton ring map.
In practical application, the target detection image may further include: at least one of a corner sharpening image and a substrate gray scale image; the corner sharpening image and the substrate gray-scale image are respectively a target detection image obtained after preprocessing based on the corner image and the substrate image.
In an embodiment of the present application, the preprocessing process may include: one or more of sharpening, gamma correction, and noise rejection.
It should be noted that the above description of the target detection image and the preprocessing is only an example, and is not necessarily taken as a limitation of the present invention.
104. Extracting to obtain detection characteristic parameters based on the target detection image;
in an embodiment of the present application, the detecting the characteristic parameter includes: the number of Newton ring layers; and the number of the Newton ring layers is extracted based on the de-noising Newton ring skeleton diagram to obtain detection characteristic parameters.
In an actual application process, the detecting the characteristic parameters may further include: at least one of an average gray difference and a maximum pixel difference; and the average gray difference and the maximum pixel difference are respectively extracted based on the corner sharpening image and the substrate gray-scale image to obtain detection characteristic parameters.
It should be noted that the above description of detecting characteristic parameters is only an example, and is not necessarily taken as a limitation of the present invention.
105. And judging a threshold value based on the detection characteristic parameters to obtain the defect type of the wafer to be detected.
In the embodiment of the application, the number of Newton ring layers is compared with a Newton ring threshold, and the bonding defect detection result of the wafer to be detected can be obtained according to the comparison result. Because the bonding defect and the polishing station abnormity in the wafer production process have a mapping relation, when the wafer to be detected is judged to have the bonding defect, the result of the polishing station abnormity in the process of the wafer to be detected can be obtained.
Further, comparing the average gray level difference with a gray level difference threshold, obtaining a detection result of the corner break defect of the wafer to be detected according to the comparison result, and deducing whether the cutting station is abnormal in the manufacturing process of the wafer to be detected through a mapping relation; and comparing the maximum pixel difference with a gray scale threshold, obtaining an etching defect detection result of the wafer to be detected according to the comparison result, and deducing whether an etching site of the wafer to be detected is abnormal in the manufacturing process through a mapping relation.
It should be noted that, in the present scheme, the bonding defect detection of the wafer to be detected can be performed based on the number of newton rings, the corner collapse defect detection of the wafer to be detected can be performed based on the average gray level difference, and the etching defect detection of the wafer to be detected can be performed based on the maximum pixel difference. In the practical application process, only bonding defect detection can be performed, and according to the practical requirements, multiple detections including multiple defect type detections can be performed, for example, double detection of bonding defects and corner breakup defects, or double detection of bonding defects and etching defects, or triple detection of bonding defects, corner breakup defects and etching defects.
It should be noted that, when a multiple detection scheme is adopted, the embodiment of the present application does not have strict requirements on the time sequence of detection of each type of defect, and taking dual detection of a bonding defect and a corner collapse defect as an example, in an actual application process, detection may be performed according to the sequence of first bonding defect detection and then corner collapse defect detection, or according to the sequence of first corner collapse defect detection and then corner collapse defect detection, or two types of defect detection are performed simultaneously.
It is to be understood that the order of detection of the various defect types should not be construed as limiting the invention.
The embodiment of the application provides a method for detecting wafer defects. Image areas corresponding to different defect forms on the wafer to be detected are identified through a template matching algorithm, so that characteristic parameters are extracted according to detection images of different image areas, the extracted characteristic parameters can reliably represent the wafer to be detected, and accurate defect detection results can be obtained when defect judgment is carried out by adopting the characteristic parameters; in addition, because the defect form, the image area and the characteristic parameters have unique mapping relations, the threshold value judgment is carried out according to the characteristic parameters, whether the wafer to be detected has defects or not can be judged, the defect type can be identified, and the station abnormity in the wafer production process can be calculated according to the mapping relations, so that an instructive improvement suggestion is provided for the wafer process.
Example two
In accordance with the first embodiment, the present application provides a method for detecting a wafer defect when the target morphology image is a newton ring diagram.
Fig. 2 is a second flowchart of a wafer defect detection method according to an embodiment of the present disclosure.
Referring to fig. 2, when the target morphology image is a newton ring diagram, the method for detecting a wafer defect includes:
201. collecting an infrared image of a wafer to be detected;
in the embodiment of the present application, the specific content of step 201 is the same as that of step 101 in the first embodiment, and is not described herein again.
202. Positioning the infrared image by using a template matching algorithm to obtain a Newton ring diagram;
specifically, the method comprises the following steps:
calling a Newton ring template from a template library;
and carrying out template matching on the infrared image based on the Newton ring template, and positioning to obtain a Newton ring image.
In the embodiment of the present application, the specific process of performing template matching on the infrared image based on the newton ring template is as follows: and reading template characteristic vectors in the Newton ring template, circularly calculating the distance between each characteristic vector in the infrared image and the template characteristic vector, comparing all distance values, and finding out an image area with the minimum distance value in the infrared image by using a minimum distance method to obtain the Newton ring image.
It should be noted that the above description of the template matching process is only an example in the embodiment of the present application, and should not be taken as a limitation of the present invention.
203. Preprocessing the Newton ring graph to obtain a denoising Newton ring skeleton graph;
specifically, the method comprises the following steps:
processing the Newton ring graph by using a Laplace operator, and extracting to obtain a Newton ring characteristic graph;
performing framework extraction on the Newton ring characteristic diagram by utilizing a framework extraction algorithm to obtain a Newton ring framework diagram;
and eliminating abnormal contour lines of the Newton ring skeleton map to obtain a de-noised Newton ring skeleton map.
In the embodiment of the application, after the newton ring map is processed by using the laplacian operator, a region with a sudden change in gray level in the newton ring map is enhanced, and a region with a slow change in gray level is weakened, so that a newton ring feature map with enhanced gray level contrast is obtained.
It should be noted that, in the practical application process, other operators may also be used to perform gray contrast enhancement on the newton ring map to obtain the newton ring feature map, for example, a Canny operator or a gaussian-laplacian operator.
It should be understood that the above description of feature extraction is only an example in the embodiments of the present application, and should not be taken as a limitation on the present invention.
In an embodiment of the present application, the skeleton extraction algorithm includes: Zhangard-Suen image skeleton extraction algorithm.
It should be noted that, in the embodiment of the present application, there is no strict limitation on the adopted skeleton extraction algorithm, and different skeleton extraction algorithms may be adopted according to actual requirements in an actual application process, that is, the description of the skeleton extraction algorithm should not be taken as a limitation on the present invention.
In the embodiment of the application, the process of removing the abnormal contour line from the newton ring skeleton diagram is as follows: and calculating the length and curvature of each contour line in the Newton ring skeleton diagram, regarding the contour lines with the length shorter than a length threshold or the curvature exceeding a curvature threshold as abnormal contour lines, and eliminating the abnormal contour lines to obtain the denoising Newton ring skeleton diagram.
In the embodiment of the present application, the length threshold is 50; the curvature threshold is 0.1.
It should be noted that, the values of the length threshold and the curvature threshold are examples in the embodiment of the present application, and may be adjusted in an actual application process, that is, the values of the length threshold and the curvature threshold are not necessarily taken as limitations of the present invention.
204. Extracting the number of Newton ring layers based on the denoising Newton ring skeleton diagram;
specifically, the method comprises the following steps:
calculating central point data of each contour line in the denoising Newton ring skeleton map to obtain a central point data set;
classifying the central point data set by using a k-means clustering algorithm to obtain N clusters; n is a positive integer;
calculating the mean value of all central point data in the cluster P as the central point data of the Newton ring; the cluster P is the cluster with the largest data size in the N clusters;
and establishing a plane coordinate system by taking the central point of the Newton ring as an origin, and calculating the maximum intersection point number of the Newton ring and the positive half shaft of the horizontal shaft of the coordinate system as the number of Newton ring layers.
In the embodiment of the present application, the classification algorithm used for classifying the center point data set may be adjusted according to actual situations, for example, the maximum inter-class variance method may also be used to classify the center point data set.
It should be understood that the above description of the clustering algorithm is only an example in the embodiment of the present application, and should not be taken as a limitation to the present invention.
In the embodiment of the application, when a plane coordinate system is established by taking the central point of the newton ring as an origin, the positive semi-axis of the horizontal axis of the plane coordinate system is a ray pointing to any contour point on any contour line from the central point of the newton ring, and the number of newton ring layers in the denoising newton ring skeleton map can be obtained by calculating the maximum intersection point number of all contour lines and the positive semi-axis of the horizontal axis.
It should be noted that the above description of the newton ring layer number acquisition process is only an example given in the embodiments of the present application, and should not be taken as a limitation of the present invention.
205. And judging a threshold value of the number of the Newton rings to obtain a defect detection result.
Specifically, the method comprises the following steps:
judging whether the number of Newton ring layers is greater than or equal to a Newton ring threshold value, if so, judging that the defect type of the wafer to be detected is a bonding defect, and judging that the station abnormity corresponding to the bonding defect is the polishing station abnormity in the manufacturing process of the wafer to be detected;
if not, judging that the wafer to be detected has no bonding defect, and judging that the polishing station has no abnormality in the manufacturing process of the wafer to be detected.
In the embodiment of the present application, the newton ring threshold is 4. It should be noted that, in practical applications, the value of the newton ring threshold may be adjusted according to practical situations, that is, the value of the newton ring threshold is not necessarily taken as a limitation to the present invention.
The embodiment of the application shows a method for detecting wafer defects, which can realize bonding defect detection on a wafer to be detected. The method comprises the steps of obtaining a Newton ring skeleton diagram by performing feature extraction and skeleton extraction on the Newton ring diagram, and removing abnormal contour lines from contour lines in the Newton ring skeleton diagram by using length and curvature, so that a de-noised Newton ring skeleton diagram which can accurately represent the Newton ring image of the wafer to be detected is obtained, the interference of noise points and background pixels on defect judgment is eliminated, and the accuracy and reliability of detection are improved; the number of the Newton rings obtained by calculation is compared with the Newton ring threshold value to obtain the detection result of the bonding defect of the wafer to be detected, and the judging process is simple and objective, so that the technical scheme shown in the embodiment of the application can accurately detect the bonding defect of the wafer, and further provides guidance for process improvement in the wafer manufacturing process.
EXAMPLE III
Corresponding to the first embodiment, the embodiment of the present application provides a method for detecting a wafer defect when the target shape image is a corner image.
Fig. 3 is a third flowchart of a wafer defect detection method according to an embodiment of the present disclosure.
Referring to fig. 3, when the target shape image is a corner image, the method for detecting the wafer defect includes:
301. collecting an infrared image of a wafer to be detected;
in the embodiment of the present application, step 301 is the same as step 101 in the first embodiment, and is not described herein again.
302. Positioning the infrared image by using a template matching algorithm to obtain a corner image;
specifically, the method comprises the following steps:
calling corner templates from the template library;
and carrying out template matching on the infrared image based on the corner template, and positioning to obtain a corner image.
303. Preprocessing the corner image to obtain a corner sharpening image;
specifically, the method comprises the following steps:
and sharpening the corner image to obtain a corner sharpening image.
In the embodiment of the present application, the sharpening process is not unique, and different algorithms, such as a gradient method, a high-pass filter, or a mask matching method, may be used according to the actual situation.
It should be understood that the above description of the sharpening process is only an example in the embodiment of the present application, and should not be taken as a limitation of the present invention.
304. Extracting to obtain an average gray difference based on the corner sharpening image;
specifically, the method comprises the following steps:
reading the traversal line position information of the corner sharpening graph;
calculating to obtain an average gray value a of the image in the area A according to the position information of the traverse line; the area A is a triangular area formed by a traverse line and an image edge;
calculating to obtain the average gray value B of the B area image according to the traverse line position information; the B area is a trapezoidal area formed by the traverse line and the image edge;
and calculating to obtain an average gray difference based on the average gray values a and b.
In the embodiment of the application, the corner images are the area images of the four corners of the infrared image of the wafer to be detected, which are in the shape of an isosceles right triangle. And obtaining a corner sharpening image after the corner image is sharpened. The traverse line is parallel to the hypotenuse of the isosceles right triangle, the moving direction of the traverse line is perpendicular to the hypotenuse of the isosceles right triangle, and the traverse line is translated from the right angle to the hypotenuse.
In the embodiment of the present application, the average gray value a is subtracted from the average gray value b, and an absolute value is taken as an obtained difference value, so as to obtain the average gray difference.
305. And judging the threshold value of the average gray difference to obtain a defect detection result.
Specifically, the method comprises the following steps:
judging whether the average gray difference is larger than a gray difference threshold value or not, if so, judging that the defect type of the wafer to be detected is a corner breaking defect, and judging that the station abnormality corresponding to the corner breaking defect is the cutting station abnormality in the manufacturing process of the wafer to be detected;
if not, reading the traversal line position information of the corner sharpening graph again until the traversal condition is met; the traversal condition comprises the following steps: the average gray difference is larger than a gray difference threshold value or the traverse line position information is overlapped with the image edge position information of the corner sharpening image.
In the embodiment of the present application, the value range of the gray scale difference threshold is 15 to 25, and the preferred value of the gray scale difference threshold in the embodiment of the present application is 20.
It should be noted that, in the practical application process, the value of the gray scale difference threshold may be adjusted according to the practical detection requirement, that is, the value of the gray scale difference threshold should not be construed as a limitation to the present invention.
In the embodiment of the present application, the traversal condition can be understood as the following two conditions:
when the calculated average gray difference is larger than the gray difference threshold, traversing is finished, the traverse line does not move any more, the defect type of the wafer to be detected is judged to be the corner breaking defect, and the station abnormity corresponding to the corner breaking defect is the cutting station abnormity in the manufacturing process of the wafer to be detected;
or
And when the traverse line moves to the bevel edge of the isosceles right triangle, the traversal is finished, the wafer to be tested is judged to have no corner breakage defect, and the cutting station is not abnormal in the manufacturing process of the wafer to be tested.
The embodiment of the application provides a wafer defect detection method, which can realize corner collapse defect detection on a wafer to be detected. The corner area is divided through the movement of the traverse line, the average gray values of two detection areas formed after the division are detected in real time, and therefore the whole corner area is subjected to overall corner collapse detection, and the accuracy of the detection result is guaranteed; whether the wafer to be detected has the defect of corner breakage or not is judged and detected through the threshold value, and the judging process is simple and quick.
Example four
In accordance with a first embodiment, a method for detecting a wafer defect when the target morphology image is a substrate image is provided.
Fig. 4 is a fourth flowchart illustrating a wafer defect detection method according to an embodiment of the present application.
Referring to fig. 4, when the target morphology image is a substrate image, the method for detecting the wafer defect includes:
401. collecting an infrared image of a wafer to be detected;
in the embodiment of the present application, step 401 is the same as step 101 in the first embodiment, and is not described herein again.
402. Positioning the infrared image by using a template matching algorithm to obtain a substrate image;
specifically, the method comprises the following steps:
calling a substrate template from a template library;
and carrying out template matching on the infrared image based on the substrate template, and positioning to obtain a substrate image.
403. Preprocessing the substrate image to obtain a substrate gray-scale image;
specifically, the method comprises the following steps:
calling a standard substrate image from a standard image library;
and carrying out gray level adjustment on the substrate image based on the standard substrate image to obtain a substrate gray level image.
In the embodiment of the present application, the purpose of performing the gray scale adjustment on the substrate image based on the standard substrate image is to perform gray scale correction on the substrate image, so that the gray scale coefficient of the substrate image is consistent with that of the standard substrate image.
It should be noted that, in the embodiment of the present application, there is no strict limitation on the algorithm used for the gray scale correction, and any algorithm that can make the gray scale coefficient of the substrate image match the standard substrate image is applicable to this embodiment, that is, the description of the gray scale correction is only an example in the embodiment of the present application, and should not be taken as a limitation to the present invention.
404. Extracting to obtain a maximum pixel difference based on the substrate gray-scale image;
specifically, the method comprises the following steps:
comparing each pixel point in the substrate gray-scale image with the standard substrate image to obtain the pixel difference of each pixel point;
and calculating the maximum value in the pixel difference of each pixel point to be used as the maximum pixel difference.
In the embodiment of the present application, taking a pixel point p (x, y) as an example, the pixel difference of the pixel point can be obtained according to the following pixel difference solving process:
reading the pixel value m of the pixel point p (x, y) in the substrate gray-scale image;
reading a pixel value n of a pixel point at a position (x, y) in the standard substrate image;
and calculating the absolute value of the difference value of the m and the n as the pixel difference of the pixel point p (x, y).
In the embodiment of the present application, the above pixel difference obtaining process needs to be performed on each pixel point in the substrate gray-scale map to obtain the pixel difference set of all the pixel points in the substrate gray-scale map, and then the maximum value in the obtained pixel difference set is selected as the maximum pixel difference of the substrate gray-scale map.
It should be noted that the above description of the pixel difference is only an example in the embodiment of the present application, and should not be taken as a limitation of the present invention.
405. And judging the threshold value of the maximum pixel difference to obtain a defect detection result.
Specifically, the method comprises the following steps:
judging whether the maximum pixel difference is larger than or equal to a gray scale threshold value, if so, judging that the defect type of the wafer to be detected is an etching defect, and judging that the site abnormality corresponding to the etching defect is the etching site abnormality in the manufacturing process of the wafer to be detected;
if not, judging that the wafer to be tested has no etching defects, and judging that the etching station in the manufacturing process of the wafer to be tested has no abnormity.
In the embodiment of the present application, the value range of the grayscale threshold is 25 to 35, and the value of the grayscale threshold is preferably 30 in the embodiment of the present application.
It should be noted that, in practical applications, the value of the gray level threshold may be adjusted according to actual needs, that is, the description of the gray level threshold should not be taken as a limitation to the present invention.
In another embodiment, for each pixel point, the difference value between m and n can be used as the pixel difference of the pixel point, the minimum value and the maximum value are taken from the pixel differences of all the pixel points, whether the minimum value and the maximum value are both in the threshold range of-30 to 30 is judged, if yes, it is judged that the wafer to be tested has no etching defect, and the etching station has no abnormality in the process of manufacturing the wafer to be tested; otherwise, judging that the defect type of the wafer to be detected is an etching defect, wherein the site abnormality corresponding to the etching defect is the etching site abnormality in the manufacturing process of the wafer to be detected.
It should be noted that the above-mentioned detection of etching defects is an alternative shown in the embodiments of the present application, and does not necessarily constitute a limitation of the present invention.
The embodiment of the application provides an etching defect detection method, wherein a threshold value judgment is carried out on the pixel difference between each pixel point in a substrate image and a standard substrate image to obtain the detection result of the etching defect of a wafer to be detected; meanwhile, before the pixel difference is calculated, the gray level of the acquired image is corrected, and the gray level difference between the template and the image to be detected, which is caused by different light source irradiation conditions, is eliminated, so that the pixel difference caused by non-etching defects is eliminated, and the detection reliability is improved.
EXAMPLE five
Corresponding to the embodiment of the application function implementation method, the application also provides a device for detecting the wafer defects and a corresponding embodiment.
Fig. 5 is a schematic structural diagram of an apparatus for detecting a wafer defect according to an embodiment of the present disclosure.
Referring to fig. 5, the apparatus for detecting wafer defects includes:
a halogen lamp 501, an infrared filter 502, an infrared lens 503, an infrared imaging module 504 and an image processing module 505;
the halogen lamp 501 is used for providing a detection light source;
the infrared filter 502 is used for filtering the detection light source to obtain infrared light with a single wavelength;
the infrared lens 503 is configured to receive light reflected by the wafer 506 to be measured, and transmit the reflected light to the infrared imaging module 504;
the infrared imaging module 504 is configured to receive light from the infrared lens and generate an infrared image of the wafer 506 to be measured;
the image processing 505 modules include: processor 5051 and memory 5052; the memory has stored thereon executable code which, when executed by the processor, causes the processor to perform the method as described above.
Preferably, the embodiment of the application adopts a filter with the wavelength of 1100nm to filter the light reflected by the halogen lamp, so as to obtain infrared light with the wavelength of 1100 nm.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The Processor 5051 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The aspects of the present application have been described in detail hereinabove with reference to the accompanying drawings. In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments. Those skilled in the art should also appreciate that the acts and modules referred to in the specification are not necessarily required in the present application. In addition, it can be understood that the steps in the method of the embodiment of the present application may be sequentially adjusted, combined, and deleted according to actual needs, and the modules in the device of the embodiment of the present application may be combined, divided, and deleted according to actual needs.
Furthermore, the method according to the present application may also be implemented as a computer program or computer program product comprising computer program code instructions for performing some or all of the steps of the above-described method of the present application.
Alternatively, the present application may also be embodied as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) having stored thereon executable code (or a computer program, or computer instruction code) which, when executed by a processor of an electronic device (or electronic device, server, etc.), causes the processor to perform part or all of the various steps of the above-described method according to the present application.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the applications disclosed herein may be implemented as electronic hardware, computer software, or combinations of both.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (12)
1. A method for detecting wafer defects is characterized by comprising the following steps:
collecting an infrared image of a wafer to be detected;
positioning the infrared image by using a template matching algorithm to obtain a target morphological image; the target morphology image includes: newton's ring diagram;
preprocessing the target form image to obtain a target detection image; the target detection image includes: denoising the Newton ring skeleton map;
extracting to obtain detection characteristic parameters based on the target detection image; the detecting the characteristic parameters comprises: the number of Newton ring layers;
and judging a threshold value based on the detection characteristic parameters to obtain the defect type of the wafer to be detected.
2. The method as claimed in claim 1, wherein when the target shape image is a newton ring diagram, the preprocessing the target shape image to obtain a target detection image comprises:
processing the Newton ring graph by using a Laplace operator, and extracting to obtain a Newton ring characteristic graph;
performing framework extraction on the Newton ring characteristic diagram by utilizing a framework extraction algorithm to obtain a Newton ring framework diagram;
and eliminating abnormal contour lines of the Newton ring skeleton map to obtain a de-noised Newton ring skeleton map.
3. The wafer defect detection method of claim 2, wherein the extracting of the detection feature parameters based on the target detection image comprises:
calculating central point data of each contour line in the denoising Newton ring skeleton map to obtain a central point data set;
classifying the central point data set by using a k-means clustering algorithm to obtain N clusters; n is a positive integer;
calculating the mean value of all central point data in the cluster P as the central point data of the Newton ring; the cluster P is the cluster with the largest data size in the N clusters;
and establishing a plane coordinate system by taking the central point of the Newton ring as an origin, and calculating the maximum intersection point number of the Newton ring and the positive half shaft of the horizontal shaft of the coordinate system as the number of Newton ring layers.
4. The wafer defect detection method of claim 3, wherein the determining a threshold value based on the detection characteristic parameter to obtain a defect detection result comprises:
and judging whether the number of Newton ring layers is greater than or equal to a Newton ring threshold, if so, judging that the defect type of the wafer to be detected is a bonding defect, and the station abnormity corresponding to the bonding defect is the polishing station abnormity in the manufacturing process of the wafer to be detected.
5. The method of claim 1, wherein the wafer defect detection method,
the target morphology image further includes: a corner image;
the target detection image further includes: a corner sharpening picture;
when the target form image is a corner image, preprocessing the target form image to obtain a target detection image, including:
and sharpening the corner image to obtain a corner sharpening image.
6. The method of claim 5, wherein the wafer defect detection method,
the detecting the characteristic parameters further comprises: averaging the gray level differences;
the extracting of the detection characteristic parameters based on the target detection image comprises the following steps:
reading the traversal line position information of the corner sharpening graph;
calculating to obtain an average gray value a of the image in the area A according to the position information of the traverse line; the area A is a triangular area formed by a traverse line and an image edge;
calculating to obtain the average gray value B of the B area image according to the traverse line position information; the B area is a trapezoidal area formed by the traverse line and the image edge;
and calculating to obtain an average gray difference based on the average gray values a and b.
7. The method as claimed in claim 6, wherein the determining a threshold value based on the detected characteristic parameters to obtain a defect detection result comprises:
judging whether the average gray difference is larger than a gray difference threshold value or not, if so, judging that the defect type of the wafer to be detected is a corner breaking defect, and judging that the station abnormality corresponding to the corner breaking defect is the cutting station abnormality in the manufacturing process of the wafer to be detected;
if not, reading the traversal line position information of the corner sharpening graph again until the traversal condition is met; the traversal condition comprises the following steps: the average gray difference is larger than a gray difference threshold value or the traverse line position information is overlapped with the image edge position information of the corner sharpening image.
8. The method of claim 1, wherein the wafer defect detection method,
the target morphological image further comprises: a substrate image;
the target detection image further includes: a substrate gray-scale map;
when the target form image is a substrate image, preprocessing the target form image to obtain a target detection image, comprising:
calling a standard substrate image from a standard image library;
and carrying out gray level adjustment on the substrate image based on the standard substrate image to obtain a substrate gray level image.
9. The method of claim 8, wherein the wafer defect is detected by the wafer defect detecting device,
the detecting the characteristic parameters further comprises: a maximum pixel difference;
the extracting of the detection characteristic parameters based on the target detection image comprises the following steps:
comparing each pixel point in the substrate gray-scale image with the standard substrate image to obtain the pixel difference of each pixel point;
and calculating the maximum value in the pixel difference of each pixel point to be used as the maximum pixel difference.
10. The method as claimed in claim 9, wherein the comparing of each pixel point in the gray-scale map with the standard substrate image comprises:
reading the pixel value m of the pixel point in the substrate gray-scale image;
reading a pixel value n of the pixel point position in the standard substrate image;
and calculating the absolute value of the difference value of the m and the n.
11. The method as claimed in claim 9, wherein the determining a threshold value based on the detected characteristic parameter to obtain a defect detection result comprises:
and judging whether the maximum pixel difference is larger than or equal to a gray scale threshold value, if so, judging that the defect type of the wafer to be detected is an etching defect, and judging that the site abnormality corresponding to the etching defect is the etching site abnormality in the manufacturing process of the wafer to be detected.
12. An apparatus for detecting wafer defects, comprising:
the infrared imaging device comprises a halogen lamp, an infrared filter, an infrared lens, an infrared imaging module and an image processing module;
the halogen lamp is used for providing a detection light source;
the infrared filter is used for filtering the detection light source to obtain infrared light with single wavelength;
the infrared lens is used for receiving light rays reflected by the wafer to be detected and transmitting the reflected light rays to the infrared imaging module;
the infrared imaging module is used for receiving the light of the infrared lens and generating an infrared image of the wafer to be detected;
the image processing module includes: a processor and a memory; the memory has stored thereon executable code which, when executed by the processor, causes the processor to perform the method of any one of claims 1-11.
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