CN118014988A - Intelligent gallium arsenide substrate wafer defect detection method - Google Patents
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- 230000007547 defect Effects 0.000 title claims abstract description 254
- 239000000758 substrate Substances 0.000 title claims abstract description 91
- JBRZTFJDHDCESZ-UHFFFAOYSA-N AsGa Chemical compound [As]#[Ga] JBRZTFJDHDCESZ-UHFFFAOYSA-N 0.000 title claims abstract description 65
- 229910001218 Gallium arsenide Inorganic materials 0.000 title claims abstract description 65
- 238000001514 detection method Methods 0.000 title claims abstract description 33
- 230000002950 deficient Effects 0.000 claims description 43
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- 238000003708 edge detection Methods 0.000 claims description 4
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- 235000012431 wafers Nutrition 0.000 description 59
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- 238000005755 formation reaction Methods 0.000 description 1
- 229910052738 indium Inorganic materials 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000004377 microelectronic Methods 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 230000005693 optoelectronics Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
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- 239000004065 semiconductor Substances 0.000 description 1
- 238000000927 vapour-phase epitaxy Methods 0.000 description 1
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- G06T7/0004—Industrial image inspection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20061—Hough transform
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Abstract
The invention relates to the technical field of image processing, in particular to an intelligent gallium arsenide substrate wafer defect detection method, which comprises the following steps: collecting gallium arsenide substrate wafer images and epitaxial layer images; obtaining edge lines, nonlinear edge lines and neighbor nonlinear edge densities according to the gallium arsenide substrate wafer image; obtaining a defect area according to the density of the non-linear edges of the neighborhood; obtaining an epitaxial layer defect area according to the epitaxial layer image; obtaining a defect area corresponding to the epitaxial layer defect area, and obtaining a comprehensive distortion index by combining a nonlinear edge line; obtaining a spiral curvature change coefficient according to the curvature of the edge line of each defect area; obtaining average gray level variation according to gray level value difference between pixel points in the defect area; obtaining a curvature gray scale change density index according to the comprehensive distortion index, the average gray scale change amount and the spiral curvature change coefficient; and obtaining a screw dislocation defect area according to the curvature gray scale change density index, and improving the detection accuracy of gallium arsenide substrate wafer defects.
Description
Technical Field
The application relates to the technical field of image processing, in particular to an intelligent gallium arsenide substrate wafer defect detection method.
Background
GaAs gallium arsenide materials have important applications in the semiconductor field, as a good quality substrate wafer, and are widely used in the fabrication of optoelectronic and microelectronic devices. However, during the production process, there are often various defects on the gallium arsenide substrate wafer that directly affect the performance and stability of the device. Therefore, the design of the high-efficiency accurate intelligent detection method has important significance for analyzing the defects on the gallium arsenide substrate wafer.
The screw dislocation defects commonly found in gallium arsenide substrate wafers have a serious impact on device performance, however, the micro-scale and complex morphology thereof make accurate detection and positioning difficult, and are prone to missed detection and false detection. In addition, the distribution of threading dislocation defects in the wafer is more discrete, increasing the difficulty of inspection. Therefore, for this specific type of defect, an intelligent detection method needs to be developed, which can realize rapid and accurate detection and positioning of the screw dislocation defect, and improve the detection accuracy and stability of the screw dislocation defect, and is one of the key problems to be solved in the current research.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent detection method for defects of a gallium arsenide substrate wafer, which aims to solve the existing problems.
The intelligent gallium arsenide substrate wafer defect detection method adopts the following technical scheme:
the embodiment of the invention provides an intelligent gallium arsenide substrate wafer defect detection method, which comprises the following steps:
Collecting gallium arsenide substrate wafer images and epitaxial layer images;
Obtaining each edge line and each linear edge line according to the gallium arsenide substrate wafer image; acquiring nonlinear edge lines, and acquiring neighbor nonlinear edge densities according to the number of linear edge lines and nonlinear edge lines in the neighbor domains of the edge lines; obtaining a defect area according to the density of the non-linear edges of the neighborhood; obtaining an epitaxial layer defect region by adopting the same acquisition mode as that of the defect region for the epitaxial layer image; obtaining a defect area corresponding to each epitaxial layer defect area according to Euclidean distance between each pixel point in the epitaxial layer defect area and the pixel points of all defect areas; obtaining the comprehensive distortion index of each defect area according to the nonlinear edge line in the epitaxial layer defect area corresponding to each defect area; obtaining a spiral curvature change coefficient according to the curvatures of all pixel points on each edge line of the defect area; obtaining average gray level variation according to gray level value differences among all adjacent pixel points in the defect area; the comprehensive distortion index, the average gray level variation and the spiral curvature variation coefficient of each defect area are combined to obtain the curvature gray level variation density index of each defect area;
And obtaining the screw dislocation defect area according to the curvature gray scale change density index.
Further, the obtaining each edge line and each linear edge line according to the gallium arsenide substrate wafer image includes:
adopting a Canny edge detection algorithm to the gallium arsenide substrate wafer image to obtain an edge image; all edge lines in the edge image are detected by adopting Hough transformation, and each linear edge line is obtained.
Further, the obtaining the nonlinear edge line, according to the number of linear edge lines and nonlinear edge lines in the neighborhood of each edge line, obtains the neighborhood nonlinear edge density, including:
counting the number of pixel points on each edge line in the edge image, and obtaining the average value of the number of pixel points on all the edge lines; marking edge lines except all linear edge lines in the edge image as nonlinear edge lines;
Each edge line in the edge image takes a central pixel point of the edge line as a circle center, and takes the average value as a radius to acquire a neighborhood of the edge line; counting the number of pixel points on all nonlinear edge lines in the neighborhood of the edge line, and recording the number as a nonlinear pixel total value; counting the number of all nonlinear edge lines in the neighborhood of the edge line; counting the number of all pixel points in the neighborhood of the edge line, and recording the number as the neighborhood pixel total value; acquiring the ratio of the nonlinear pixel total value to the neighborhood pixel total value; acquiring an exponential function taking a natural constant as a base and taking the ratio as an index; and taking the product of the calculation result of the exponential function and the number as the neighborhood nonlinear edge density of the edge line.
Further, the obtaining the defect area according to the neighborhood nonlinear edge density includes:
Calculating the upper quartile of the neighbor nonlinear edge density of all edge lines, and marking the edge line with the neighbor nonlinear edge density larger than the upper quartile as a defect area edge line;
Marking edge lines of defective areas in the edge image, obtaining all connected areas by adopting a connected area analysis algorithm on the marked edge image, and marking the areas of all the connected areas corresponding to the gallium arsenide substrate wafer image as all the defective areas.
Further, the obtaining the defect area corresponding to each epitaxial layer defect area according to the euclidean distance between each pixel point in the epitaxial layer defect area and the pixel points of all the defect areas includes:
For each epitaxial layer defect area, calculating Euclidean distances from each pixel point in the epitaxial layer defect area to the pixel points of all defect areas; taking the pixel points with the minimum Euclidean distance in all defect areas as corresponding points of all the pixel points in the epitaxial layer defect areas; and counting the number of the corresponding points in each defect area, and taking the defect area with the largest number of the corresponding points as the defect area corresponding to the epitaxial layer defect area.
Further, the obtaining the comprehensive distortion index of each defect area according to the nonlinear edge line in the epitaxial layer defect area corresponding to each defect area includes:
Counting the number of nonlinear edge lines in the defect area for each defect area, and recording the number as the number of defect edges; counting the number of edge lines in the defect area, and recording the number as the total number of edges; obtaining the ratio of the number of defective edges to the total number of edges, and recording the ratio as a first ratio; counting the number of nonlinear edge lines in the epitaxial layer defect area corresponding to the defect area, and recording the number as the corresponding defect edge number; counting the number of edge lines in the epitaxial layer defect area corresponding to the defect area, and recording the number as the total number of corresponding edges; obtaining the ratio of the number of the corresponding defective edges to the total number of the corresponding edges, and marking the ratio as a second ratio; obtaining a difference value between the first ratio and the second ratio, and recording the difference value as a first difference value; calculating a difference value between the number of pixel points in the epitaxial layer defect area corresponding to the defect area and the number of pixel points in the defect area, and marking the difference value as a second difference value; taking the product of the first difference value and the second difference value as the comprehensive distortion index of the defect area.
Further, the obtaining the spiral curvature change coefficient according to the curvature of all the pixel points on each edge line of the defect area includes:
For each edge line in the defect area, taking the end point with the minimum sum of the abscissa and the ordinate of two end points of the edge line as a starting point; starting from a starting point, sequentially arranging all points on an edge line according to a topological order to form an ordered point set; the sequence formed by the curvatures of each point in the ordered point set is recorded as a curvature sequence; calculating a first-order differential sequence of the curvature sequence, and recording the first-order differential sequence as a curvature trend sequence of the edge line;
If the first in the curvature trend sequence The number of elements is greater than or equal to zero, then the number/>The threshold function value of each element is 1; otherwise, the first/>The threshold function value of each element is-1;
Calculating the average value of threshold function values of all elements in the curvature trend sequence; acquiring an absolute value of the mean value; calculating a downward rounding value of the absolute value; and taking the sum of the downward rounding values of all edge lines contained in the defect area as a spiral curvature change coefficient of the defect area.
Further, the obtaining the average gray level variation according to the gray level value differences between all adjacent pixel points in the defect area includes:
Recording any pixel point in the defect area as a pixel point to be analyzed, and calculating the absolute value of the difference between the gray value of the pixel point to be analyzed and the gray value of each pixel point in the eight adjacent areas of the pixel point to be analyzed; acquiring the average value of all the absolute difference values of the pixel points to be analyzed; and taking the average value of all the pixel points in the defect area as the average gray level variation of the defect area.
Further, the obtaining the curvature gray scale change density index of each defect area by integrating the integrated distortion index, the average gray scale change amount and the spiral curvature change coefficient of each defect area includes:
Calculating the product of the comprehensive distortion index, the average gray level variation and the spiral curvature variation coefficient of each defect area; obtaining an exponential function taking a natural constant as a base and taking the opposite number of the product as an index; and taking the difference between the number 1 and the calculation result of the exponential function as the curvature gray scale variation density index of each defect area.
Further, the obtaining the screw dislocation defect region according to the curvature gray scale variation density index includes: and taking the defect area with the curvature gray scale change density index larger than a preset defect threshold value as the screw dislocation defect area.
The invention has at least the following beneficial effects:
According to the method, the morphological characteristics of the screw dislocation defects of the gallium arsenide substrate wafer are analyzed, firstly, the characteristics of relatively uniform, continuous and clear edges of the normal area of the gallium arsenide substrate wafer are analyzed, the adjacent nonlinear edge density is calculated according to the number and the length of nonlinear edge lines around the edge lines in the edge image, the edge lines of the defect area are determined, and then the defect area is obtained, and the characteristics of larger and irregular edge line density in the adjacent area of the edge lines of the defect area are reflected; then, according to the characteristics of a defect evolution process of epitaxial growth of the substrate wafer, the number of nonlinear edge lines in the defect area on the substrate wafer and the corresponding defect area on the epitaxial layer and the size of the defect area are combined, and a comprehensive distortion index is calculated to reflect the distortion degree; finally, the gray level change and curvature change of the defect area on the substrate wafer are combined, the curvature gray level change density index is calculated, and the complex form of the screw dislocation defect is better described according to the epitaxial growth characteristics, the gray level characteristics and the curvature change characteristics of the screw dislocation defect, so that the screw dislocation defect is detected more accurately, and the detection precision of the gallium arsenide substrate wafer defect is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of an intelligent detection method for gallium arsenide substrate wafer defects;
Fig. 2 is a flowchart for obtaining the integrated distortion index.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the intelligent detection method for gallium arsenide substrate wafer defects according to the invention with reference to the attached drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the gallium arsenide substrate wafer defect intelligent detection method provided by the invention with reference to the accompanying drawings.
The invention provides an intelligent detection method for gallium arsenide substrate wafer defects, in particular to an intelligent detection method for gallium arsenide substrate wafer defects, referring to fig. 1, comprising the following steps:
and S001, acquiring a gallium arsenide substrate wafer image and an epitaxial layer image of the gallium arsenide substrate wafer, and preprocessing.
Collecting a gallium arsenide substrate wafer by using an SEM scanning electron microscope to obtain an original gallium arsenide substrate wafer image; and then observing the epitaxial layer of the same gallium arsenide substrate wafer by using SEM to obtain an original epitaxial layer image. Respectively taking an original gallium arsenide substrate wafer image and an original epitaxial layer image as inputs, and obtaining a preprocessed gallium arsenide substrate wafer image by utilizing a median filtering technologyAnd pretreated epitaxial layer image/>. The median filtering is a well-known technique, and the specific process is not described in detail.
Step S002, calculating the neighborhood nonlinear edge density according to the gallium arsenide substrate wafer image and the epitaxial layer image; calculating a comprehensive distortion index according to the number of nonlinear edge lines in the defect areas on the substrate wafer and the corresponding epitaxial layer and the size of the defect areas; and calculating the curvature gray scale change density index by combining the gray scale change and the curvature change of the defect area on the substrate wafer.
Wafer image with gallium arsenide substrateFor input, edge image/>, of the gallium arsenide substrate wafer is obtained by adopting Canny edge detection. Then uses the edge image/>, of the gallium arsenide substrate waferFor input, hough transform is used to output linear edge lines. For epitaxial layer image/>And performing the same treatment to obtain the epitaxial layer linear edge line. Canny edge detection and hough transform are known techniques, and detailed processes are not described.
On the gallium arsenide substrate wafer, the structure of the normal area is relatively uniform, continuous and the edge is clear, so that the edge line of the normal area is in a regular straight line or no obvious edge curve is detected, and the edge image of the gallium arsenide substrate wafer is displayedThe density of edge lines in the neighborhood around the edge line in the normal region will typically be relatively small and mostly straight; the edge image/>, on the gallium arsenide substrate wafer, is because defects may cause changes, fracture or dislocation of the crystal structure, so that the edge curve at the defect area tends to be irregular and more complex, may appear as curves, fracture lines, sharp edges, etcThe density of edge lines in the neighborhood around the edge line of the defect area will typically be relatively large and mostly irregular curves.
Edge image on gallium arsenide substrate waferIn the process, the neighborhood nonlinear edge density of each edge line is calculated. Edge image/>In (2), the edge lines other than all the straight edge lines are denoted as non-straight edge lines. At each edge line/>In the neighborhood, all nonlinear edge lines are counted,/>The neighborhood refers to the center of the edge line as the center of the circle,/>Is a neighborhood of radius formations. In this embodiment/>Equal to edge image/>The average value of the number of pixel points on all edge lines. With edge image/>Edge line/>For example, a neighborhood non-linear edge density is calculated:
Wherein the method comprises the steps of Is edge line/>Is a neighborhood non-linear edge density; /(I)Is edge line/>/>The number of the pixel points on all the nonlinear edge lines in the neighborhood is recorded as the total nonlinear pixel value; /(I)Is edge line/>/>The number of the pixel points in the neighborhood is recorded as the total value of the neighborhood pixels; /(I)Is edge line/>/>The number of non-linear edge lines in the neighborhood; /(I)Is an exponential function based on a natural constant e.
If edge lineIs the edge line of the normal region, then edge/>The surroundings are mostly straight lines, so that the number of non-straight edge lines is smaller, i.e./>Relatively small and the density of edge lines in the surrounding neighborhood is small, so that the non-linear edge line density is also small, i.e./>Is also relatively small; if edge line/>Is the edge line of the defective area, then edge line/>The surroundings are mostly irregular curves, i.e./>Relatively large and the density of edge lines in the surrounding neighborhood is large, so that the non-linear edge line density is also large, i.e./>And is also relatively large. Thus, edge line/>Neighborhood non-linear edge density/>The smaller the edge line/>The more likely it is an edge line of the normal region; neighborhood non-straight edge Density/>The larger the edge line/>The more likely it is an edge line located in the defective area.
Since on a GaAs substrate wafer, the defect area should be small, i.e. the edge lines should be mostly normal, the edge image is calculatedNeighborhood nonlinear edge densities of all edge lines in the model (a), and taking the upper quartile/>, of the neighborhood nonlinear edge densitiesThe density of the non-linear edges of the neighborhood is larger than the upper quartile/>The edge line of the defect area is marked as the edge line of the defect area, and the calculation of the upper quartile is a known technology and is not repeated.
At the edge imageMarking edge lines of a defect area, taking edge images marked with the edge lines of all the defect areas as input, adopting a connected domain analysis technology to obtain connected domain information formed by the edge lines of the defect area, and carrying out image/>, on a gallium arsenide substrate waferAnd marking a defect area according to the connected domain information. The connected domain analysis technology is known as a technology, and a specific process is not described in detail.
For the epitaxial layer, an epitaxial layer image is obtained according to the processing procedureIs in the epitaxial layer defect region. For epitaxial layer image/>Pixel points of each epitaxial layer defect area in the gallium arsenide substrate wafer image/>, calculating the pixel points to the gallium arsenide substrate wafer image/>The Euclidean distance between the pixel points of all the defect areas, and the pixel point with the smallest Euclidean distance between the pixel points in all the defect areas is used as the corresponding point of the pixel point, so that the defect area corresponding to each epitaxial layer defect area on the epitaxial layer is obtained. The acquisition mode is as follows, and the epitaxial layer defect area/>, in each defect area, is countedThe corresponding point number of the pixel points in the epitaxial layer is defined as the defect area with the largest corresponding point numberCorresponding defective area/>. It should be noted that, during the defect evolution process of epitaxial growth of the substrate wafer, defects on the substrate wafer gradually migrate, aggregate, etc., so that one epitaxial layer defect area of the epitaxial layer may be formed by aggregating a plurality of defect areas on the substrate wafer, so that a certain epitaxial layer defect area on the epitaxial layer may correspond to a plurality of defect areas on the substrate wafer.
If the defect area on the substrate wafer corresponds to a certain defect area on the epitaxial layer, the quality of the substrate wafer is directly reflected in the epitaxial layer because the homoepitaxial growth process of the vapor phase epitaxy is actually a continuous process of the substrate crystal lattice, and the defects on the substrate wafer gradually migrate, gather and the like in the defect evolution process of the epitaxial growth, so that the defect density of the substrate wafer is larger than the defect density of the epitaxial layer, and the size of the defects on the substrate wafer is smaller than the size of the defects on the epitaxial layer. Therefore, the comprehensive distortion index is obtained according to the corresponding relation between the defect area and the edge line in the epitaxial layer defect area corresponding to the defect area, and the specific process of obtaining is shown in fig. 2.
With defective areas on the substrate waferFor example, if the defective area/>, on the substrate waferCorresponding to a certain defective area on the epitaxial layer, then it is assumed that the defective area/>, on the substrate waferCorresponding epitaxial layer defect region/>Calculating a comprehensive distortion index:
Wherein the method comprises the steps of Is defective region/>Is a complex distortion index of (2); /(I)Is defective region/>The number of the non-linear edge lines is recorded as the number of the defect edges; /(I)Is defective region/>The number of the middle edge lines is recorded as the total number of the edges; /(I)Is associated with defective region/>Corresponding epitaxial layer defect region/>The number of the non-linear edge lines is recorded as the corresponding defect edge number; /(I)Is the defect region/>, on the epitaxial layerThe number of the middle edge lines is recorded as the total number of the corresponding edges; /(I)Is epitaxial layer defect region/>The number of the middle pixel points; /(I)Is defective region/>The number of the middle pixel points.
If defective areas on a substrate waferNot corresponding to the epitaxial layer defect region on any one epitaxial layer. If defective area/>, on a substrate waferAnd defect region/>, on epitaxial layerCorrespondingly, defective area/>, on the substrate waferThe number of non-linear edge lines in the epitaxial layer should be greater than the defect region/>In, i.e./>Second, the size of the defect on the substrate wafer is smaller than the size of the defect on the epitaxial layer, so that the defect area/>, on the substrate waferThe number of the middle pixel points is also smaller than the corresponding defect area/>, on the epitaxial layerThe number of middle pixels, i.e./>So that if defective areas/>, on the substrate waferAnd epitaxial layer defect region/>Correspondingly, defective area/>, thenIs a complex distortion index/>And/>The larger the defect region/>, theThe more severe the distortion of (a). If defective area/>, on a substrate waferDoes not correspond to a defective region on any one epitaxial layer, due to/>Thereby/>And because of/>Therefore/>So that if defective areas/>, on the substrate waferNot corresponding to the defective region on any epitaxial layer, defective region/>Is a complex distortion index/>A kind of electronic device.
Screw dislocation defects cause problems such as wafer distortion, localized stress concentrations, and non-uniform growth, which can typically affect the epitaxial layer, i.e., the screw dislocation defect region on the substrate wafer typically becomes one of the corresponding defect regions of a defect region on the epitaxial layer. Secondly, since one of the characteristics of the screw dislocation defect is that the distortion is generated due to the change of the atomic arrangement of the region around the dislocation line, the gray value of the pixel point in the screw dislocation defect region is frequently changed and the difference is obvious, and furthermore, the non-linear edge line density is large. Finally, the dislocation line of the screw dislocation in the gallium arsenide substrate wafer usually presents a spiral shape extending along a specific direction, and the spiral radius is gradually increased or gradually decreased, and the magnitude of the spiral radius affects the magnitude of the curvature of each point on the dislocation line, that is, the curvature of adjacent points along the dislocation line has a uniform variation trend.
Then to the defect areaWith defective area/>Edge line/>For example, edge line/>The end point with the smallest sum of the abscissa and the ordinate is taken as a starting point, and the edge line/>All the points are orderly arranged according to the topological order to form an ordered point set, the sequence formed by the curvatures of each point in the ordered point set is marked as a curvature sequence, and then the first-order differential sequence of the curvature sequence is calculated and is marked as an edge line/>Curvature trend sequence/>. The first-order differential sequence is a known technique, and the specific process is not repeated. Calculating defective area/>Curvature gray scale change density index of (c):
Wherein, Is defective region/>Density index of curvature gray change,/>Is defective region/>Is the integrated distortion index of/>Is defective region/>For measuring the average gray level variation of defective area/>Average difference in mid-gray value variation,/>Is defective region/>Is used for judging the defect area/>, based on the curvature changeIf there are helical edge lines and how many,/>Is defective region/>Middle pixel dot/>Gray value of/>Is pixel/>The 8 th/>, of the 8 th neighborhood takenGray value of each pixel/(Is pixel/>Number of pixel points in 8 neighborhood,/>Is defective region/>Number of middle pixel points,/>Is defective region/>Number of middle edge lines,/>Is defective region/>Edge line/>Curvature trend sequence/>Number of elements in/>Is a threshold function, the variable in brackets is more than or equal to 0, the function value is 1, otherwise the function value is-1,/>Is curvature trend sequence/>/>Element,/>Is a downward rounding function,/>Is an exponential function based on a natural constant e.
If edge lineIs a spiral edge line, then the edge line/>Along an edge line with any one of the end points of (a) as a starting pointThe curvature being progressively decreasing or progressively increasing, i.e. when the change has monotonicity,/>1, And the downward rounding is 1; if edge line/>Is a non-helical edge line, then the edge line/>Is started along the edge line/>The change in curvature is irregular, so that the curvature trend sequence/>The sign of all elements in (a) is also irregular, i.e./>The rounding down is 0.
If a defective areaIs a screw dislocation defect region, then the defect region/>Corresponds to a certain defect region on the epitaxial layer, and the defect region/>The linear density of the edge of the non-straight line is larger, so the comprehensive distortion index/>And/>Middle/>Larger, thereby/>And is relatively large; the difference of gray values in the defect area is larger, so that the average gray variation in the defect area is larger, namelyLarger; the presence of helical edge lines in the defect region, i.e./>And/>The larger the number of edge lines showing a spiral shape, the larger the probability of being a dislocation defect region of the spiral shape; thus if defective area/>Is a screw dislocation defect region, and the curvature gray scale change density index/>, of the regionAnd/>The larger the more likely the threading dislocation defect region.
And S003, obtaining a screw dislocation defect area according to the curvature gray scale change density index, and finishing the detection of the screw dislocation defect of the gallium arsenide substrate wafer.
Calculating curvature gray scale change density indexes of all defect areas, and setting defect threshold valuesAnd taking a defect area with the curvature gray scale change density index larger than the defect threshold value as a screw dislocation defect area. Thus, the detection of the screw dislocation defect of the gallium arsenide substrate wafer is completed.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; the technical solutions described in the foregoing embodiments are modified or some of the technical features are replaced equivalently, so that the essence of the corresponding technical solutions does not deviate from the scope of the technical solutions of the embodiments of the present application, and all the technical solutions are included in the protection scope of the present application.
Claims (10)
1. The intelligent gallium arsenide substrate wafer defect detection method is characterized by comprising the following steps:
Collecting gallium arsenide substrate wafer images and epitaxial layer images;
Obtaining each edge line and each linear edge line according to the gallium arsenide substrate wafer image; acquiring nonlinear edge lines, and acquiring neighbor nonlinear edge densities according to the number of linear edge lines and nonlinear edge lines in the neighbor domains of the edge lines; obtaining a defect area according to the density of the non-linear edges of the neighborhood; obtaining an epitaxial layer defect region by adopting the same acquisition mode as that of the defect region for the epitaxial layer image; obtaining a defect area corresponding to each epitaxial layer defect area according to Euclidean distance between each pixel point in the epitaxial layer defect area and the pixel points of all defect areas; obtaining the comprehensive distortion index of each defect area according to the nonlinear edge line in the epitaxial layer defect area corresponding to each defect area; obtaining a spiral curvature change coefficient according to the curvatures of all pixel points on each edge line of the defect area; obtaining average gray level variation according to gray level value differences among all adjacent pixel points in the defect area; the comprehensive distortion index, the average gray level variation and the spiral curvature variation coefficient of each defect area are combined to obtain the curvature gray level variation density index of each defect area;
And obtaining the screw dislocation defect area according to the curvature gray scale change density index.
2. The intelligent detection method for defects of gallium arsenide substrate wafer according to claim 1, wherein the obtaining edge lines and straight line type edge lines according to gallium arsenide substrate wafer image comprises:
adopting a Canny edge detection algorithm to the gallium arsenide substrate wafer image to obtain an edge image; all edge lines in the edge image are detected by adopting Hough transformation, and each linear edge line is obtained.
3. The intelligent gallium arsenide substrate wafer defect detection method according to claim 2, wherein the obtaining nonlinear edge lines, according to the number of linear edge lines and nonlinear edge lines in the neighborhood of each edge line, obtains the neighborhood nonlinear edge density, comprises:
counting the number of pixel points on each edge line in the edge image, and obtaining the average value of the number of pixel points on all the edge lines; marking edge lines except all linear edge lines in the edge image as nonlinear edge lines;
Each edge line in the edge image takes a central pixel point of the edge line as a circle center, and takes the average value as a radius to acquire a neighborhood of the edge line; counting the number of pixel points on all nonlinear edge lines in the neighborhood of the edge line, and recording the number as a nonlinear pixel total value; counting the number of all nonlinear edge lines in the neighborhood of the edge line; counting the number of all pixel points in the neighborhood of the edge line, and recording the number as the neighborhood pixel total value; acquiring the ratio of the nonlinear pixel total value to the neighborhood pixel total value; acquiring an exponential function taking a natural constant as a base and taking the ratio as an index; and taking the product of the calculation result of the exponential function and the number as the neighborhood nonlinear edge density of the edge line.
4. The intelligent detection method for defects of gallium arsenide substrate wafer according to claim 2, wherein the obtaining the defect area according to the neighborhood nonlinear edge density comprises:
Calculating the upper quartile of the neighbor nonlinear edge density of all edge lines, and marking the edge line with the neighbor nonlinear edge density larger than the upper quartile as a defect area edge line;
Marking edge lines of defective areas in the edge image, obtaining all connected areas by adopting a connected area analysis algorithm on the marked edge image, and marking the areas of all the connected areas corresponding to the gallium arsenide substrate wafer image as all the defective areas.
5. The intelligent gallium arsenide substrate wafer defect detection method according to claim 1, wherein the obtaining the defect area corresponding to each epitaxial layer defect area according to the euclidean distance between each pixel point in the epitaxial layer defect area and the pixel points of all defect areas comprises:
For each epitaxial layer defect area, calculating Euclidean distances from each pixel point in the epitaxial layer defect area to the pixel points of all defect areas; taking the pixel points with the minimum Euclidean distance in all defect areas as corresponding points of all the pixel points in the epitaxial layer defect areas; and counting the number of the corresponding points in each defect area, and taking the defect area with the largest number of the corresponding points as the defect area corresponding to the epitaxial layer defect area.
6. The intelligent gallium arsenide substrate wafer defect detection method according to claim 1, wherein the obtaining the comprehensive distortion index of each defect region according to the nonlinear edge line in the epitaxial layer defect region corresponding to each defect region comprises:
Counting the number of nonlinear edge lines in the defect area for each defect area, and recording the number as the number of defect edges; counting the number of edge lines in the defect area, and recording the number as the total number of edges; obtaining the ratio of the number of defective edges to the total number of edges, and recording the ratio as a first ratio; counting the number of nonlinear edge lines in the epitaxial layer defect area corresponding to the defect area, and recording the number as the corresponding defect edge number; counting the number of edge lines in the epitaxial layer defect area corresponding to the defect area, and recording the number as the total number of corresponding edges; obtaining the ratio of the number of the corresponding defective edges to the total number of the corresponding edges, and marking the ratio as a second ratio; obtaining a difference value between the first ratio and the second ratio, and recording the difference value as a first difference value; calculating a difference value between the number of pixel points in the epitaxial layer defect area corresponding to the defect area and the number of pixel points in the defect area, and marking the difference value as a second difference value; taking the product of the first difference value and the second difference value as the comprehensive distortion index of the defect area.
7. The intelligent gallium arsenide substrate wafer defect detection method according to claim 1, wherein the obtaining the spiral curvature change coefficient according to the curvature of all pixel points on each edge line of the defect area comprises the following steps:
For each edge line in the defect area, taking the end point with the minimum sum of the abscissa and the ordinate of two end points of the edge line as a starting point; starting from a starting point, sequentially arranging all points on an edge line according to a topological order to form an ordered point set; the sequence formed by the curvatures of each point in the ordered point set is recorded as a curvature sequence; calculating a first-order differential sequence of the curvature sequence, and recording the first-order differential sequence as a curvature trend sequence of the edge line;
If the first in the curvature trend sequence The number of elements is greater than or equal to zero, then the number/>The threshold function value of each element is 1; otherwise, the first/>The threshold function value of each element is-1;
Calculating the average value of threshold function values of all elements in the curvature trend sequence; acquiring an absolute value of the mean value; calculating a downward rounding value of the absolute value; and taking the sum of the downward rounding values of all edge lines contained in the defect area as a spiral curvature change coefficient of the defect area.
8. The intelligent detection method for defects of gallium arsenide substrate wafer according to claim 1, wherein the obtaining average gray scale variation according to gray scale value differences between all adjacent pixel points in the defect area comprises:
Recording any pixel point in the defect area as a pixel point to be analyzed, and calculating the absolute value of the difference between the gray value of the pixel point to be analyzed and the gray value of each pixel point in the eight adjacent areas of the pixel point to be analyzed; acquiring the average value of all the absolute difference values of the pixel points to be analyzed; and taking the average value of all the pixel points in the defect area as the average gray level variation of the defect area.
9. The intelligent gallium arsenide substrate wafer defect detection method according to claim 1, wherein the obtaining the curvature gray scale variation density index of each defect region by integrating the integrated distortion index, the average gray scale variation and the spiral curvature variation coefficient of each defect region comprises:
Calculating the product of the comprehensive distortion index, the average gray level variation and the spiral curvature variation coefficient of each defect area; obtaining an exponential function taking a natural constant as a base and taking the opposite number of the product as an index; and taking the difference between the number 1 and the calculation result of the exponential function as the curvature gray scale variation density index of each defect area.
10. The intelligent gallium arsenide substrate wafer defect detection method according to claim 1, wherein the obtaining the screw dislocation defect area according to the curvature gray scale variation density index comprises: and taking the defect area with the curvature gray scale change density index larger than a preset defect threshold value as the screw dislocation defect area.
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