Method for measuring dislocation density and dislocation density counting device

Abstract

The present invention relates to a method for measuring dislocation density, the method comprising: obtaining an image of an object to be measured; detecting etch pits in the obtained image according to preset etch pit screening conditions, and statistically calculating an average area value S0 and an average aspect ratio R0 of the long side to the short side of a single etch pit in the obtained image; performing contour detection on the black shapes in the obtained image to obtain a contour area S and an aspect ratio R of the long side to the short side of the bounding rectangle of the black shape; determining the number of etch pits of the black shape based on the relationship between the contour area S and the aspect ratio R of the long side to the short side of the black shape and the average area value S0 and the average aspect ratio R0 of the long side to the short side of the bounding rectangle; and calculating the dislocation density of the object to be measured based on the total number of etch pits in the obtained image and the total area of the obtained image. The present invention also relates to a dislocation density counting device and a non-transitory computer-readable medium.

Description

Technical Field

[0001] The present invention relates to the technical field of dislocation measurement, and in particular, to a method for measuring dislocation density, a dislocation density counting device, and a non-transitory computer-readable medium. Background Art

[0002] "Dislocation" can also be called a slip dislocation. In materials science, it refers to an internal microscopic defect in a crystalline material, that is, a local irregular arrangement of atoms (crystallographic defect). Geometrically, a dislocation can be regarded as the boundary line between the slipped part and the unslipped part in a crystal, and its existence has a great impact on the physical properties of the material.

[0003] In particular, during the semiconductor manufacturing process, the generation of dislocations is inevitable, but dislocations will affect the migration of carriers, and thus directly affect the quality of semiconductors, epitaxial layers, and devices. Therefore, accurately obtaining the dislocation density is of great significance for product quality control.

[0004] In the traditional method for measuring dislocation density, after etching the wafer, a microscope is used to manually count the etch pits in the image area, and finally the etch pit density (EPD) is calculated. This method has the following disadvantages: the manual counting workload is large, the area to be counted on a single wafer is too large, which is time-consuming and laborious; the human factor is large, and situations such as missing counts, wrong counts, and duplicate counts often occur; as a result, the final dislocation density is greatly different from the actual value. In addition, due to manual counting, this method is often only applicable to the case of low dislocation density and small-sized wafers.

[0005] Currently, some new methods for measuring dislocation density have been developed. For example, Chinese Patent Application CN102721697A discloses a method and a detection system for detecting dislocations in crystalline silicon. This method performs optical imaging on the etched crystalline silicon, and based on the relationship between the gray pixel ratio of the etch pits and the dislocation density value, the dislocation density of the sample to be measured is obtained. However, this method requires a crystalline silicon standard sample to establish the relationship between the gray pixel ratio of the etch pits and the dislocation density value, and different relationships need to be established for different series of samples. In addition, this method only considers the gray pixel factor of the image and does not consider the actual shape of the etch pits. For example, long strip-shaped scratches will also be recognized as etch pits due to the gray level of the image, but scratches do not belong to etch pits.

[0006] Chinese Patent Application CN107356606A discloses a method for detecting the dislocation density of a semiconductor wafer. This method determines whether it is an effective etch pit by calculating the ratio a of the opening size L to the depth H of the etch pit. This method requires the establishment of a three-dimensional image of the etch pit, and the required device is complex; in addition, this method does not give an effective detection method for severely overlapping etch pits.

[0007] Chinese Patent Application CN1896727A discloses a method for detecting the types and densities of defects in GaN single crystals. This method needs to combine Scanning Electron Microscope (SEM) and Atomic Force Microscope (AFM) for judgment. The testing instruments for such data are expensive and the operation is complex. In addition, this method requires using Photoshop to segment the SEM image, printing it and then manually counting to obtain the number of etch pits, and finally obtaining the dislocation density. This method is also time-consuming and laborious, and is not conducive to large-scale industrial use.

[0008] It can be seen that there are still problems in the industrialization of detecting and counting the dislocation density of wafers; providing a simple, automated method for measuring dislocation density that can identify overlapping etch pits is of great significance. Summary of the Invention

[0009] In view of the above problems, the present application provides a method for measuring dislocation density, a dislocation density counting device, and a non-transitory computer-readable medium to solve the problem of inaccurate measurement of dislocation density.

[0010] To this end, the first aspect of the present invention provides a method for measuring dislocation density, the method comprising:

[0011] Obtaining an image of a measurement object;

[0012] Detecting etch pits in the obtained image according to preset etch pit screening conditions, and statistically calculating the average area value S0 and the average aspect ratio R0 of the long and short sides of the bounding rectangle of a single etch pit in the obtained image;

[0013] Performing contour detection on the black shapes in the obtained image to obtain the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shape;

[0014] Based on the relationship between the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shape and the average area value S0 and the average aspect ratio R0 of the long and short sides of the bounding rectangle, determining the number of etch pits of the black shape to determine the total number of etch pits in the obtained image; and

[0015] Calculate the dislocation density of the measurement object based on the total number of corrosion pits in the acquired image and the total area of the acquired image.

[0016] The second aspect of the present invention provides a dislocation density counting device, which includes:

[0017] An image acquisition device for acquiring an image of a measurement object;

[0018] A processor configured to:

[0019] Detect corrosion pits in the acquired image according to preset corrosion pit screening conditions, and statistically calculate the average area value S0 of a single corrosion pit and the aspect ratio R0 of the long and short sides of the average bounding rectangle in the acquired image;

[0020] Perform contour detection on the black shape in the acquired image to obtain the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shape;

[0021] Based on the relationship between the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shape and the average area value S0 and the aspect ratio R0 of the long and short sides of the average bounding rectangle, determine the number of corrosion pits of the black shape to determine the total number of corrosion pits in the acquired image; and

[0022] Calculate the dislocation density of the measurement object based on the total number of corrosion pits in the acquired image and the total area of the acquired image.

[0023] The third aspect of the present invention provides a non-transitory computer-readable medium having computer-readable instructions, which, when executed by a computer device, cause the computer device to execute the method according to the first aspect above.

[0024] The fourth aspect of the present invention provides a dislocation density counting device, which includes:

[0025] An image acquisition device for acquiring an image of a measurement object; and

[0026] An image processing device for receiving the image of the measurement object acquired by the image acquisition device, performing dislocation density counting on the image of the measurement object, and displaying the counting result;

[0027] Wherein, the image acquisition device includes a light source for adjusting the field-of-view brightness of the image acquisition device, and the light source is a cold white light source with a color temperature in the range of 5000 - 6000K or a CIE1931 color coordinate range in (0.31, 0.31) - (0.33, 0.33).

[0028] In an embodiment of the present invention, by comparing the contour area and the aspect ratio of the long and short sides of the bounding rectangle of the black shapes in the image with the average area value and the average aspect ratio of the long and short sides of the bounding rectangle of a single etch pit, it is determined how many etch pits each black shape corresponds to. This embodiment can effectively determine how many etch pits are contained in the overlapping area of the etch pits, so as to accurately count the total number of etch pits. In addition, through the cold white light source within the above range, the color rendering index can be higher and the image can be clearer, which can effectively help to distinguish the background from the etch pits and facilitate the identification of the etch pits.

[0029] In addition, the dislocation density measurement method of the present invention combines the identification of crystal structure and graphic characteristics; thus, it can effectively identify and count overlapping etch pits, and at the same time can exclude the influence of non-etch pit patterns such as long scratches on the total number.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] 1) Compared with the traditional template method for detection and counting, the degree of automation is high, and there is no need for manual counting of etch pits, avoiding situations such as miscounting and missing counting, improving efficiency, especially for semiconductor wafers with high dislocation density, which has more significant advantages;

[0032] 2) It can effectively identify etch pits according to the shapes of etch pits on different semiconductor wafers, and can effectively count the number of overlapping etch pits; it can filter non-etch pits (such as scratches, etc.), improve the counting accuracy, truly reflect the dislocation density information of the wafer, can effectively control the wafer quality, and track and analyze the quality problems of the wafer according to the counting results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the present application, the accompanying drawings required for use in the embodiments of the present application will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the accompanying drawings without creative efforts. In the accompanying drawings:

[0034] Figure 1 Schematically illustrates a dislocation density counting device according to an embodiment of the present invention;

[0035] Figure 2 Schematically illustrates the contour of a single etch pit on a GaAs wafer;

[0036] Figure 3 Schematically illustrates the contour of a single etch pit on an InP wafer;

[0037] Figure 4 Schematically illustrates the contour of a single etch pit on a GaN wafer;

[0038] Figure 5 A flowchart illustrating a method for measuring dislocation density according to an embodiment of the present invention;

[0039] Figure 6 A flowchart illustrating a method for determining the total number of corrosion pits in an acquired image according to an embodiment of the present invention;

[0040] Figure 7-1 A schematic diagram illustrating the determination of a bounding rectangle of a semiconductor wafer image according to an embodiment of the present invention;

[0041] Figure 7-2 A schematic diagram illustrating the determination of a bounding rectangle of a semiconductor wafer image according to an embodiment of the present invention, wherein the bounding rectangle has an inclination angle; and

[0042] Figure 7-3 A schematic diagram illustrating the determination of a bounding rectangle of a semiconductor wafer image according to an embodiment of the present invention, wherein the contour of a single corrosion pit is hexagonal.

[0043] Description of reference numerals in the drawings

[0044] 10 Flat bottom of the bracket

[0045] 101 Bracket support

[0046] 102 Image acquisition device support

[0047] 103 Bracket connecting member

[0048] 20 Stage

[0049] 30 Vacuum chuck

[0050] 40 Zoom lens

[0051] 50 CCD (charge coupled device) camera

[0052] 60 LED white light source

[0053] 70 Image processing device Detailed description of the invention

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs; the terms used herein are for the purpose of describing specific embodiments only and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion.

[0055] References to "embodiments" in this specification mean that the particular features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0056] If steps are recited in order in this specification or claims, this does not necessarily mean that the embodiment or aspect is limited to the recited order. On the contrary, it is conceivable that the steps can be performed in a different order or in parallel with each other, unless one step is based on another step, in which case the step that is absolutely required to be established must be performed subsequently (however, this will become clear in individual cases). Therefore, the recited order can be a preferred embodiment.

[0057] The first aspect of the present invention provides a method for measuring dislocation density, the method comprising:

[0058] Obtaining an image of the measurement object;

[0059] According to preset corrosion pit screening conditions, detecting corrosion pits in the obtained image, and statistically calculating the average area value S0 of a single corrosion pit and the aspect ratio R0 of the long and short sides of the average bounding rectangle in the obtained image;

[0060] Performing contour detection on the black shapes in the obtained image to obtain the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shapes;

[0061] Based on the relationship between the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shapes and the average area value S0 and the aspect ratio R0 of the long and short sides of the average bounding rectangle, determining the number of corrosion pits of the black shapes to determine the total number of corrosion pits in the obtained image; and calculating the dislocation density of the measurement object based on the total number of corrosion pits in the obtained image and the total area of the obtained image.

[0062] In the above embodiments of the present invention, by comparing the contour area and the aspect ratio of the long and short sides of the bounding rectangle of the black shapes in the image with the average area value of a single corrosion pit and the aspect ratio of the long and short sides of the average bounding rectangle, it is determined how many corrosion pits each black shape corresponds to. This embodiment can effectively determine how many corrosion pits are included in the overlapping area of the corrosion pits, so that the total number of corrosion pits can be accurately counted.

[0063] In some embodiments of the first aspect of the present invention, the step of determining the number of corrosion pits of the black shapes includes:

[0064] When And If so, record the number of corrosion pits of the black shape as 1, where k 10 and k 11 is the single corrosion pit filtering coefficient;

[0065] When and If so, record the number of corrosion pits of the black shape as 2, where k 21 is the two-overlapping corrosion pit filtering coefficient; and

[0066] When and If so, record the number of corrosion pits of the black shape as 3, where k 31 is the three-overlapping corrosion pit filtering coefficient. In some embodiments, k 21 = 2 * k 11 and k 31 = 3 * k 11 .

[0067] In the above embodiments, by setting the corrosion pit filtering coefficient, the number of overlapping corrosion pits is distinguished, non-corrosion pits (e.g., scratches, etc.) are excluded, the technical accuracy is improved, and the corrosion pit density information of the measurement object is truly reflected.

[0068] In some embodiments of the first aspect of the present invention, the steps of determining the total number of corrosion pits in the acquired image include:

[0069] When the total number of corrosion pit overlapping regions in the acquired image exceeds a preset threshold or the acquired image contains multiple corrosion pit overlapping regions with more than 3 overlapping corrosion pits, calculate the total number of corrosion pits in the acquired image according to the following formula:

[0070] Total number of corrosion pits = total area of black shapes in the image / average area value S0 of a single corrosion pit.

[0071] In the above embodiments, when the number of corrosion pits is too large, for example, there are multiple corrosion pit overlapping regions or multiple corrosion pit regions with more than 3 overlapping corrosion pits, the overall statistical idea is adopted for counting; specifically, obtain the total area of all corrosion pits in the target image, and perform global statistical counting based on the total area, rather than counting according to the corrosion pit filtering conditions. In some embodiments, the preset threshold exceeded by the total number of corrosion pit overlapping regions in the acquired image is 10. This preset threshold is an empirical value obtained from a large number of tests, and different preset thresholds can be set for different test objects.

[0072] In some embodiments of the first aspect of the present invention, the method obtains a plurality of images at a plurality of different positions of the measurement object, and counts the total number of corrosion pits in the plurality of images to calculate the dislocation density of the measurement object. In such an embodiment, the total number of corrosion pits at a plurality of different positions of the measurement object is counted, and the total number of corrosion pits is divided by the total area of these positions to obtain the dislocation density of the measurement object. This embodiment avoids the uneven distribution of dislocations in the measurement object, and extreme distributions may occur in a single image, resulting in inaccurate measurement.

[0073] In some embodiments of the first aspect of the present invention, the method includes: after obtaining the image of the measurement object, performing gray-scale processing on the obtained image. When obtaining a color image through a color-mode camera, it is necessary to perform gray-scale processing on the image to facilitate the identification of corrosion pits. In a preferred embodiment, the camera for obtaining the image is a black-and-white mode camera; the image obtained by this camera can be directly utilized without gray-scale processing.

[0074] In some embodiments of the first aspect of the present invention, the method includes performing binarization processing on the obtained image. The image after binarization processing is easier for contour recognition.

[0075] In some embodiments of the first aspect of the present invention, the method includes:

[0076] corroding the measurement object before obtaining the image of the measurement object;

[0077] and the corrosion pit screening condition is the shape of the corrosion pit determined according to the material type of the measurement object and the type of the corrosion agent.

[0078] In some embodiments of the first aspect of the present invention, when obtaining the measurement image, the illumination light source used is a cold white light source with a color temperature in the range of 5000 - 6000K.

[0079] In some other embodiments of the first aspect of the present invention, when obtaining the measurement image, the illumination light source used is a cold white light source with a CIE1931 color coordinate range in (0.31, 0.31) - (0.33, 0.33). In a preferred embodiment, the cold white light source is an LED cold white light source.

[0080] By using the cold white light source within the above range, the color rendering index can be higher and the image can be clearer, thereby effectively helping to distinguish the background from the corrosion pits and facilitating the identification of corrosion pits. Those skilled in the art can understand that the color temperature and color coordinates of the illumination light source can be controlled by instructions.

[0081] In some embodiments of the first aspect of the present invention, the normal direction of the lens for acquiring the image is perpendicular to the surface of the measurement object. With such an arrangement, the acquired image will not be deformed, that is, the shape of the corrosion pit will not be deformed, so as to facilitate obtaining the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the corrosion pit. In a preferred embodiment, the lens is an optical zoom lens capable of switching between 5X, 10X, and 20X.

[0082] In some embodiments of the first aspect of the present invention, the pixel size of the camera for acquiring the image is not greater than 2.0μm × 2.0μm. Usually, the minimum size of the corrosion pit is about 10μm × 10μm. By setting the pixel size in this way, the resolution of the corrosion pit in the picture is guaranteed.

[0083] The second aspect of the present invention provides a dislocation density counting device, and the dislocation density counting device includes:

[0084] An image acquisition device for acquiring an image of a measurement object;

[0085] A processor, and the processor is configured to:

[0086] According to preset corrosion pit screening conditions, detect corrosion pits in the acquired image, and statistically calculate the average area value S0 and the average aspect ratio R0 of the long and short sides of the bounding rectangle of a single corrosion pit in the acquired image;

[0087] Perform contour detection on the black shape in the acquired image to obtain the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shape;

[0088] Based on the relationship between the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shape and the average area value S0 and the average aspect ratio R0 of the long and short sides of the bounding rectangle, determine the number of corrosion pits of the black shape to determine the total number of corrosion pits in the acquired image; and

[0089] Based on the total number of corrosion pits in the acquired image and the total area of the acquired image, calculate the dislocation density of the measurement object.

[0090] In some embodiments of the second aspect of the present invention, the processor is configured to determine the number of corrosion pits of the black shape based on the following filtering conditions:

[0091] When And Then, record the number of corrosion pits of the black shape as 1, where k 10 And k 11 Are single corrosion pit filtering coefficients;

[0092] When And When, the number of corrosion pits of the black shape is recorded as 2, where k 21 Is the filtering coefficient of two overlapping corrosion pits and; And

[0093] When And When, the number of corrosion pits of the black shape is recorded as 3, where k 31 Is the filtering coefficient of three overlapping corrosion pits. In some embodiments, k 21 = 2 * k 11 And k 31 = 3 * k 11 .

[0094] In some embodiments of the second aspect of the present invention, the processor is configured to:

[0095] When the total number of overlapping regions of corrosion pits in the acquired image exceeds a preset threshold or the acquired image contains multiple overlapping regions of corrosion pits with more than 3 overlapping corrosion pits, calculate the total number of corrosion pits in the acquired image according to the following formula:

[0096] Total number of corrosion pits = total area of black shapes in the image / average area value S0 of a single corrosion pit.

[0097] In some embodiments of the second aspect of the present invention, the image acquisition device acquires multiple images at multiple different positions of the measurement object, and the processor is configured to count the total number of corrosion pits in the multiple images to calculate the dislocation density of the measurement object.

[0098] In some embodiments of the second aspect of the present invention, the processor is configured to: after acquiring the image of the measurement object, perform grayscale processing on the acquired image.

[0099] In some embodiments of the second aspect of the present invention, the processor is configured to perform binarization processing on the acquired image.

[0100] In some embodiments of the second aspect of the present invention, the corrosion pit screening condition is the corrosion pit shape determined according to the material type of the measurement object and the type of the etchant.

[0101] In some embodiments of the second aspect of the present invention, the dislocation density counting device includes a light source for adjusting the field brightness of the image acquisition device, and the light source is a cold white light source with a color temperature of 5000 - 6000K.

[0102] In some embodiments of the second aspect of the present invention, the dislocation density counting device includes a light source for adjusting the field of view brightness of the image acquisition device, and the light source is a cold white light source with a CIE1931 color coordinate range of (0.31, 0.31)-(0.33, 0.33). In a preferred embodiment, the cold white light source is an LED cold white light source.

[0103] In some embodiments of the second aspect of the present invention, the image acquisition device includes a lens, and the normal direction of the lens is perpendicular to the surface of the measurement object.

[0104] In some embodiments of the second aspect of the present invention, the image acquisition device includes a camera, and the pixel size of the camera is no more than 2.0μm×2.0μm.

[0105] The third aspect of the present invention provides a non-transitory computer-readable medium having computer-readable instructions that, when executed by a computer device, cause the computer device to execute the method according to the first aspect described above.

[0106] The fourth aspect of the present invention provides a dislocation density counting device, and the device includes:

[0107] An image acquisition device for acquiring an image of a measurement object; and

[0108] An image processing device for receiving the image of the measurement object acquired by the image acquisition device, performing dislocation density counting on the image of the measurement object, and displaying the counting result;

[0109] wherein the image acquisition device includes a light source for adjusting the field of view brightness of the image acquisition device, and the light source is a cold white light source with a color temperature of 5000-6000K or a CIE1931 color coordinate range of (0.31, 0.31)-(0.33, 0.33).

[0110] In some embodiments of the fourth aspect of the present invention, the image acquisition device includes a camera, and the pixel size of the camera is no more than 2.0μm×2.0μm.

[0111] In some embodiments of the fourth aspect of the present invention, the dislocation density counting device includes a stage for placing the measurement object. The distance between the light source and the surface of the stage is based on convenient operation and obtaining the best detection result, generally between 5-55 cm, preferably 10-30 cm.

[0112] In some embodiments of the fourth aspect of the present invention, the image acquisition device includes a lens, and the normal direction of the lens is perpendicular to the surface of the stage. This embodiment can reflect the corrosion pit image as accurately as possible, avoiding the deformation of the corrosion pit image caused by oblique incidence due to shadows and the like.

[0113] In some embodiments of the fourth aspect of the present invention, the dislocation density counting device includes a stage for placing the measurement object and a vacuum chuck. The stage is provided with a through hole, and the vacuum chuck is arranged below the stage to fix the measurement object on the stage through the through hole. After obtaining an image of the measurement object, it is necessary to move the stage to obtain an image at another position of the measurement object; however, during the process of moving the stage, the measurement object (especially a large wafer) may move. Through the vacuum chuck, the semiconductor wafer can be fixed on the stage without damage, while ensuring that the wafer does not move during the process of moving the stage.

[0114] In some embodiments of the fourth aspect of the present invention, the support mechanism of the stage is provided with a rail system to enable it to move in the horizontal direction.

[0115] In some embodiments of the fourth aspect of the present invention, the image processing device includes a detection component, which performs corrosion pit detection and analysis on the acquired image based on the material type of the measurement object to obtain the dislocation density of the measurement object.

[0116] In some embodiments of the fourth aspect of the present invention, the detection component includes:

[0117] 1) An image reading module that reads the acquired image;

[0118] 2) A corrosion pit screening module that receives the information of the image reading module and sets the screening conditions for corrosion pits according to the corrosion pit shapes of different materials, and obtains the average area value S0 and the aspect ratio R0 of the long and short sides of the average bounding rectangle of a single corrosion pit;

[0119] 3) A contour detection module that receives the information of the image reading module and performs contour detection on the black shapes in the acquired image to obtain the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shape;

[0120] 4) A corrosion pit filtering condition module that receives the information of the corrosion pit screening module and the contour detection module and sets the corrosion pit filtering conditions:

[0121] When and then the number of corrosion pits of the black shape is recorded as 1, where k 10 and k11 is the filtering coefficient for a single corrosion pit;

[0122] When and then the number of corrosion pits in the black shape is recorded as 2, where k 21 is the filtering coefficient for two overlapping corrosion pits;

[0123] When and then the number of corrosion pits in the black shape is recorded as 3, where k 31 is the filtering coefficient for three overlapping corrosion pits; and

[0124] When or then, if then round up to obtain the number of corrosion pits in the black shape, and if then round up to obtain the number of corrosion pits in the black shape; and

[0125] 5) A corrosion pit density calculation module that obtains the total number of corrosion pits in the collected image through the above counting, and calculates the corrosion pit density of the measurement object as the misalignment density of the measurement object based on the total area of the collected image and the total number of corrosion pits in the obtained image. In some embodiments, k 21 = 2 * k 11 and k 31 = 3 * k 11 .

[0126] In some embodiments of the fourth aspect of the present invention, the corrosion pit filtering conditions include:

[0127] When the total number of overlapping areas of corrosion pits in the obtained image exceeds a preset threshold or the obtained image contains multiple corrosion pit overlapping areas with more than 3 overlapping corrosion pits, calculate the total number of corrosion pits in the obtained image according to the following formula:

[0128] Total number of corrosion pits = total area of black shapes in the image / average area value S0 of a single corrosion pit.

[0129] In some embodiments of the fourth aspect of the present invention, the detection component includes an image processing module that performs binarization processing on the collected image.

[0130] In some embodiments of the fourth aspect of the present invention, the detection component includes an image processing module that performs grayscale processing on the collected image.

[0131] In a preferred embodiment, the image processing device includes a detection component that performs etch pit detection and analysis on the acquired image based on the material type of the semiconductor wafer to obtain the dislocation density of the semiconductor wafer.

[0132] In a preferred embodiment, the image processing device includes a display component for displaying the dislocation density of the semiconductor wafer.

[0133] In a preferred embodiment, the device further includes a support mechanism for the stage and a support mechanism for the image acquisition device. The positions, heights (i.e., positions in the vertical direction), and angles of the support mechanism for the stage and the support mechanism for the image acquisition device are adjustable to ensure that the position between the support mechanism for the stage and the support mechanism for the image acquisition device (in fact, the position between the semiconductor wafer carried on the stage and the image acquisition device) is optimized. It should be understood that all these adjustments can be automatically implemented based on control instructions and the actuating devices in the device.

[0134] In the present invention, the boundary rectangle is defined as follows: taking the center of the image of the selected measurement object (such as a wafer) as the origin, selecting the boundary points in the four directions of -x, x, y, and -y, and drawing two sets of parallel lines through these four points. The intersecting rectangle is the boundary rectangle, as shown in Figure 7-1 The boundary rectangle of the wafer is shown by the frame line. On the basis of determining the boundary rectangle, the ratio of the long side / short side of the rectangle is obtained, which is the aspect ratio of the boundary rectangle. If the selected wafer image has a certain inclination angle relative to the horizontal line, as shown in Figure 7-2 The formed boundary rectangle also has a certain inclination angle, but it does not affect the value of the aspect ratio of the boundary rectangle. Figure 7-1 and Figure 7-2 show etch pits with an octagon-shaped contour. Similarly, Figure 7-3 shows etch pits with a regular hexagon-shaped contour, where the boundary rectangle of the wafer is shown by the frame line.

[0135] In some embodiments of the present invention, the etch pit filtering coefficient is obtained based on production big data. Specifically, the sizes of the etch pits of the same type of product are statistically analyzed to obtain the numerical range in which the etch pits of the product are located. Usually, the aspect ratio values of the boundary rectangles of the single etch pits in 1000 regions of product A are statistically analyzed. For example, the minimum value is 1.4, the maximum value is 1.7, and the object of this detection also belongs to product A, and the average aspect ratio of the boundary rectangle is 1.5. Then k 10 = 1.4 / 1.5 = 0.93, k 11 = 1.7 / 1.5 = 1.13. In addition, the etch pit filtering coefficient k 21 takes the value of k 11 *2, k 31 takes the value of k11 *3; thus, in this specific embodiment, k 21 = k 11 *2 = 2.26, k 31 = k 11 *3 = 3.39. It should be understood that the larger the amount of data used, the more accurate the obtained corrosion pit filtering coefficient; in some embodiments, the aspect ratio values of the long and short sides of the bounding rectangles of single corrosion pits in 1000 regions of 1000 A products can be statistically analyzed.

[0136] Subsequently, according to and the range of, determine the number of corrosion pits: when and the number of corrosion pits with black shapes is recorded as 1; when and at this time, the number of corrosion pits with black shapes is recorded as 2; when and at this time, the number of corrosion pits with black shapes is recorded as 3.

[0137] When the number of corrosion pits is too large, for example, there are a particularly large number of corrosion pit overlapping regions or multiple corrosion pit overlapping regions have more than 3 overlapping corrosion pits, at this time, the overall statistical idea is adopted for counting. That is: obtain the total area of all corrosion pits in the target image, and conduct global statistical counting based on the total area, rather than counting according to the corrosion pit filtering conditions. Specifically, the total number of corrosion pits = the total area of corrosion pits / the average area value S0 of a single corrosion pit. In the case of corrosion pit overlap - which means there are many corrosion pits in the field of view area, considering that the determination of the bounding rectangle can be adjusted according to the overlap situation, it is still possible to basically completely count all the corrosion pits in the wafer image.

[0138] In one embodiment of the present invention, when measuring S0 of a GaAs wafer, the screening condition for corrosion pits is set as non - overlapping contours, and the contour is a long octagon, as Figure 2 shown; when measuring S0 of an InP wafer, the screening condition for corrosion pits is set as non - overlapping circles, as Figure 3 shown; when measuring a GaN wafer, the screening condition for corrosion pits is set as non - overlapping regular hexagons, as Figure 4 shown. It should be understood that according to the different materials of the wafers, the shapes of the corrosion pits can be different. For example, the corrosion pits of GaAs wafers can be long octagons; the corrosion pits of InP wafers can be circles; the corrosion pits of SiC wafers can be regular hexagons or circles; the corrosion pits of GaN wafers and AlN wafers can be regular hexagons; the corrosion pits of InN wafers can be hexagons (2 short sides and 4 long sides); the corrosion pits of ZnO wafers can be hexagons; the corrosion pits of Ga2O3 wafers can be quadrilaterals, hexagons or long strips.

[0139] In the present invention, before image acquisition of a semiconductor wafer, the wafer needs to be preprocessed, and the preprocessing process is known to those skilled in the art. The preprocessing includes edge grinding and lapping of the wafer to form a wafer with a certain thickness, such as 2 - 3 inches (inches); then the saw marks are removed, and then the wafer is etched with an acidic or alkaline etching solution for a corrosion time to completely expose the dislocations or defects of the wafer, such as 10 - 25 minutes. It should be understood that different types of materials to be measured and different types of etchants require different corrosion times.

[0140] As Figure 1 As shown, a semiconductor dislocation density counting device of the present invention includes an image acquisition device and an image processing device 70; the image acquisition device includes a CCD camera 50 and a zoom lens 40, and an image of the semiconductor crystal placed on the stage 20 is acquired by the CCD camera 50 and the zoom lens 40 and transmitted to the image processing device 70 for dislocation density counting.

[0141] The device further includes a support mechanism for the stage 20 and a support mechanism for the image acquisition device, and the positions, heights, and angles of the support mechanism for the stage 20 and the support mechanism for the image acquisition device are adjustable.

[0142] The support mechanism of the stage 20 includes a bracket flat bottom 10 and a bracket support 101; the support mechanism of the image acquisition device includes a bracket connecting member 103 and an image acquisition device support 102; the bracket support 101 is fixed on the bracket flat bottom 10, and the bracket connecting member 103 is pivotally connected to the image acquisition device support 102; the bracket connecting member 103 is pivotally connected to the bracket support 101. The CCD camera 50, the zoom lens 40, and the LED white light source 60 are fixed on the image acquisition device support 102. A guide rail system is provided below the bracket flat bottom 10 of the stage 20, so that the stage 20 can move in a plane in the transverse and longitudinal directions; the stage 20 is provided with through holes, and a vacuum chuck 30 is provided at the bottom of the stage 20, and the vacuum chuck 30 can fix the semiconductor wafer on the stage 20 without damage through the through holes.

[0143] The support mechanism of the stage 20 and the support mechanism of the image acquisition device can be adjusted so that the normal direction of the lens 40 is perpendicular to the surface of the stage 20, which can ensure that the normal direction of the lens 40 is perpendicular to the surface of the semiconductor wafer carried on the surface of the stage 20; at the same time, the distance between the lens 40 and the surface of the stage 20 can be adjusted for convenient operation and obtaining the best detection results. For example, this distance can be between 5 - 55 cm, preferably 10 - 30 cm.

[0144] Figure 5The flowchart of method 500 for measuring dislocation density according to an embodiment of the present invention is illustrated. As Figure 5 shown, through the Figure 1 dislocation density counting device shown in

[0145] Figure 6 In step 502, an image of the measurement object is acquired. After the image is acquired, in step 504, according to the preset corrosion pit screening conditions, the corrosion pits in the acquired image are detected, and the average area value S0 and the average aspect ratio R0 of the long and short sides of the boundary rectangle of a single corrosion pit in the acquired image are statistically calculated; in step 506, the contour of the black shape in the acquired image is detected, and the contour area S and the aspect ratio R of the long and short sides of the boundary rectangle of the black shape are obtained. Then, in step 508, based on the relationship between the contour area S of the black shape and the aspect ratio R of the long and short sides of the boundary rectangle and the average area value S0 and the average aspect ratio R0 of the long and short sides of the average boundary rectangle, the number of corrosion pits of the black shape is determined to determine the total number of corrosion pits in the acquired image. Finally, in step 510, based on the total number of corrosion pits in the acquired image and the total area of the acquired image, the dislocation density of the measurement object is calculated. Figure 6 shown, in step 602, the contour area S and the aspect ratio R of the long and short sides of the boundary rectangle of a black shape in the acquired image are detected. Then, the relationship between the contour area S of the black shape and the aspect ratio R of the long and short sides of the boundary rectangle and the average area value S0 and the average aspect ratio R0 of the long and short sides of the average boundary rectangle is judged. In step 604, it is judged whether and are satisfied, where k 10 and k 11 are single corrosion pit filtering coefficients. If the conditions in step 604 are satisfied, it is counted that the black shape has 1 corrosion pit, step 606; otherwise, go to step 608. In step 608, it is judged whether and are satisfied, where k 21 is a two-overlapping corrosion pit filtering coefficient. If the conditions in step 608 are satisfied, it is counted that the black shape has 2 corrosion pits, step 610; otherwise, go to step 612. In step 612, it is judged whether and are satisfied, where k 31 is a three-overlapping corrosion pit filtering coefficient. If the conditions in step 612 are satisfied, it is counted that the black shape has 3 corrosion pits, step 614; otherwise, go to step 616. In step 616, it is judged whether or If the condition in step 616 is not satisfied, it is counted that the black shape has 0 corrosion pits, step 618; otherwise, go to step 620. In step 620, it is judged whether the condition If the condition in step 620 is satisfied, then for round up as the number of corrosion pits of the black shape, step 622; otherwise, go to step 624. In step 624, for round up as the number of corrosion pits of the black shape. After determining the number of corrosion pits of the black shape, in step 626, it is judged whether the number of black shapes with overlapping corrosion pits is greater than a preset threshold or whether there are multiple black shapes in the image with more than 3 overlapping corrosion pits. If the condition in step 626 is satisfied, the counting is no longer based on the corrosion pit filtering condition, but the global statistical counting is carried out based on the total area, where the total number of corrosion pits in the image = the total area of the black shapes in the image / the average area value S0, step 628. If the condition in step 626 is not satisfied, go to step 630, accumulate the number of corrosion pits in the image, and re-enter step 602 to determine the number of corrosion pits of the next black shape.

[0146] Embodiment

[0147] The following illustrates the implementation manners of the present invention through specific specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners, and various details in this specification can also be modified, changed, and combined without departing from the spirit of the present invention.

[0148] It should be noted that the illustrations provided in this embodiment only illustrate the basic concept of the present invention in a schematic manner, only showing the components related to the present invention schematically, rather than limiting the number, shape, size, manufacturing method, and process window of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex. The process conditions involved in the embodiment can be reasonably changed within the effective window and achieve the effects disclosed by the present invention.

[0149] Embodiment 1

[0150] Use Figure 1 the semiconductor wafer dislocation density counting device shown in

[0151] (1) Wafer taking: Take a 3-inch GaAs wafer that has been edge-ground and polished, and remove the saw marks;

[0152] (2) Etching: Use a high-temperature alkaline etching solution to etch the GaAs wafer for 10 minutes to fully expose the dislocations or defects of the wafer;

[0153] (3) Acquisition: Place the etched GaAs wafer on the stage, and adjust the normal direction of the lens to be perpendicular to the surface of the semiconductor wafer carried on the surface of the stage; Turn on the LED white light source, adjust the distance between the lens and the surface of the stage to 26 cm, and adjust the field of view size to 0.25 cm 2 (0.5 cm × 0.5 cm), perform image acquisition on the corresponding area, and save the image;

[0154] (4) Processing: Perform grayscale processing on the acquired image to obtain a grayscale image with corrosion pits, identify the boundaries of the corrosion pits, and only select non-overlapping corrosion pits with an octagonal contour to obtain the average area S0 of a single corrosion pit = 785 pixel 2 (i.e., the square of the pixel) and the aspect ratio R0 of the long and short sides of the bounding rectangle = 3.3;

[0155] (5) Counting: When the aspect ratio R and area S of the bounding rectangle of the contour area meet: and at this time, the number of corrosion pits +1, when and at this time, the number of corrosion pits +2; when and at this time, the number of corrosion pits +3, and finally the total number of corrosion pits in this area is obtained as 73, and the calculated dislocation density is 292 pieces / cm2;

[0156] (6) Move the wafer to the next area in a certain direction, repeat steps (3)-(5), and the calculated dislocation density is 246 pieces / cm 2 ;

[0157] (7) Repeat the counting, and a total of 50 areas are selected in sequence. Average the dislocation densities of the 50 areas, and finally the dislocation density of the GaAs wafer can be obtained as 269 pieces / cm 2 .

[0158] Example 2

[0159] Use Figure 1 The semiconductor wafer dislocation density counting device shown in to count the dislocation density of the InP wafer. Obtain the counting result according to the following steps:

[0160] (1) Wafer taking: Take a 2-inch InP wafer that has been edged and polished, and remove the saw marks;

[0161] (2) Etching: Etch the InP wafer with an acidic etching solution for 20 minutes to fully expose the dislocations or defects of the wafer.

[0162] (3) Acquisition: Place the etched InP wafer on the stage, and adjust the normal direction of the lens to be perpendicular to the surface of the semiconductor wafer placed on the surface of the stage; turn on the LED white light source, adjust the distance between the lens and the surface of the stage to 25 cm, and adjust the field of view size to 0.25 cm 2 (0.5 cm × 0.5 cm), perform image acquisition on this area, and save the image.

[0163] (4) Processing: Perform grayscale processing on the acquired image, and appropriately perform binary processing to obtain a black-and-white picture with etching pits. Identify the circular boundaries of the etching pits, and only select non-overlapping etching pits with circular contours to obtain the average area S0 of a single etching pit = 343 pixel 2 and the aspect ratio R0 of the long and short sides of the bounding rectangle = 0.95.

[0164] (5) Counting: When the aspect ratio R and area S of the contour region meet: and , the number of etching pits +1. When and , the number of etching pits +2; when and , the number of etching pits +3. Finally, the total number of etching pits in this area is 11, and the calculated dislocation density is 44 per cm2;

[0165] (6) Move the wafer to the next area in a certain direction, repeat steps (3)-(5), and the calculated dislocation density is 26 per cm 2 ;

[0166] (7) Repeat the counting, select a total of 50 areas in sequence, average the dislocation densities of the 50 areas, and finally the dislocation density of the InP wafer can be obtained as 35 per cm 2 .

[0167] The above embodiments only illustratively explain the principles and effects of the present invention, rather than limiting the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A method for measuring dislocation density, the method comprising: Obtaining an image of a measurement object; Detecting corrosion pits in the obtained image according to preset corrosion pit screening conditions, and statistically calculating the average area value S0 of a single corrosion pit and the aspect ratio R0 of the long and short sides of the average bounding rectangle in the obtained image; Performing contour detection on the black shape in the obtained image to obtain the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shape; Based on the relationship between the contour area S of the black shape and the aspect ratio R of the long and short sides of the bounding rectangle and the average area value S0 and the average aspect ratio R0 of the long and short sides of the bounding rectangle, determining the number of corrosion pits of the black shape to determine the total number of corrosion pits in the obtained image; And Calculating the dislocation density of the measurement object based on the total number of corrosion pits in the obtained image and the total area of the obtained image; Wherein the step of determining the number of corrosion pits of the black shape includes: When and then record the number of corrosion pits of the black shape as 1, where k 10 and k 11 are single corrosion pit filtering coefficients; When and then, record the number of corrosion pits of the black shape as 2, where k 21 is the filtering coefficient of two overlapping corrosion pits; and When and then, record the number of corrosion pits in the black shape as 3, where k 31 is the filtering coefficient for three overlapping corrosion pits.

2. The method according to claim 1, wherein The step of determining the total number of corrosion pits in the obtained image includes: When the total number of overlapping regions of corrosion pits in the obtained image exceeds a preset threshold or the obtained image contains multiple corrosion pit overlapping regions with more than 3 overlapping corrosion pits, calculating the total number of corrosion pits in the obtained image according to the following formula: Total number of corrosion pits = total area of the black shape in the image / average area value S0 of a single corrosion pit.

3. The method according to claim 1 or 2, characterized in that, The method obtains multiple images at multiple different positions of the measurement object, and statistically calculates the total number of corrosion pits in the multiple images to calculate the dislocation density of the measurement object.

4. The method according to claim 1 or 2, characterized in that The method includes performing binarization processing on the obtained image.

5. The method according to claim 1 or 2, characterized in that, The method includes: Etching the measurement object before obtaining the image of the measurement object; and The corrosion pit screening condition is the corrosion pit shape determined according to the material type of the measurement object and the type of the etchant.

6. The method according to claim 1 or 2, characterized in that When obtaining the measurement image, the illumination light source used is a cold white light source with a color temperature in the range of 5000 - 6000K.

7. The method according to claim 1 or 2, characterized in that, When obtaining the measurement image, the illumination light source used is a cold white light source with a CIE1931 color coordinate range in (0.31, 0.31) - (0.33, 0.33).

8. The method according to claim 1 or 2, characterized in that The normal direction of the lens for obtaining the image is perpendicular to the surface of the measurement object.

9. The method according to claim 1 or 2, characterized in that, The pixel size of the camera for obtaining the image is not greater than 2.0μm × 2.0μm.

10. A dislocation density counting device, the dislocation density counting device comprising: An image acquisition device for obtaining an image of a measurement object; A processor, the processor being configured to: Detect corrosion pits in the obtained image according to preset corrosion pit screening conditions, and statistically calculate the average area value S0 of a single corrosion pit and the aspect ratio R0 of the long and short sides of the average bounding rectangle in the obtained image; Perform contour detection on the black shape in the obtained image to obtain the contour area S and the aspect ratio R of the long and short sides of the bounding rectangle of the black shape; Based on the relationship between the contour area S of the black shape and the aspect ratio R of the boundary rectangle and the average area value S0 and the average aspect ratio R0 of the boundary rectangle, determine the number of corrosion pits of the black shape to determine the total number of corrosion pits in the acquired image; And Based on the total number of corrosion pits in the acquired image and the total area of the acquired image, calculate the dislocation density of the measurement object; Wherein the processor is configured to determine the number of corrosion pits of the black shape based on the following filtering conditions: When and then, record the number of corrosion pits in the black shape as 1, where k 10 and k 11 are single corrosion pit filtering coefficients; When and then, record the number of corrosion pits of the black shape as 2, where k 21 is the filtering coefficient of two overlapping corrosion pits; and When and then record the number of corrosion pits in the black shape as 3, where k 31 is the filtering coefficient for three overlapping corrosion pits.

11. The dislocation density counting device according to claim 10, characterized in that, The processor is configured to: When the total number of corrosion pit overlapping areas in the acquired image exceeds a preset threshold or the acquired image contains multiple corrosion pit overlapping areas with more than 3 overlapping corrosion pits, calculate the total number of corrosion pits in the acquired image according to the following formula: Total number of corrosion pits = total area of the black shape in the image / average area value S0 of a single corrosion pit.

12. The dislocation density counting device according to claim 10 or 11, characterized in that, The image acquisition device acquires multiple images at multiple different positions of the measurement object, and the processor is configured to count the total number of corrosion pits in the multiple images to calculate the dislocation density of the measurement object.

13. The dislocation density counting device according to claim 10 or 11, characterized in that, The processor is configured to perform binarization processing on the acquired image.

14. The dislocation density counting device according to claim 10 or 11, characterized in that, The corrosion pit screening condition is the corrosion pit shape determined according to the material type of the measurement object and the type of the etchant.

15. The dislocation density counting device according to claim 10 or 11, characterized in that, The dislocation density counting device includes a light source for adjusting the field brightness of the image acquisition device, and the light source is a cold white light source with a color temperature in the range of 5000 - 6000K.

16. The dislocation density counting device according to claim 10 or 11, characterized in that The dislocation density counting device includes a light source for adjusting the field brightness of the image acquisition device, and the light source is a cold white light source with a CIE1931 color coordinate range in (0.31, 0.31) - (0.33, 0.33).

17. The dislocation density counting device according to claim 10 or 11, characterized in that, The image acquisition device includes a lens, and the normal direction of the lens is perpendicular to the surface of the measurement object.

18. The dislocation density counting device according to claim 10 or 11, characterized in that, The image acquisition device includes a camera, and the pixel size of the camera is not greater than 2.0μm × 2.0μm.

19. A non - transitory computer - readable medium having computer - readable instructions that, when executed by a computer device, cause the computer device to perform the method according to any one of claims 1 to 9.

20. A dislocation density counting device, the device comprising: An image acquisition device for acquiring an image of a measurement object; And An image processing device (70) for receiving the image of the measurement object acquired by the image acquisition device, performing dislocation density counting on the image of the measurement object, and displaying the counting result; Wherein the image acquisition device includes a light source (60) for adjusting the field brightness of the image acquisition device, and the light source (60) is a cold white light source with a color temperature in the range of 5000 - 6000K or a CIE1931 color coordinate range in (0.31, 0.31) - (0.33, 0.33); Wherein the dislocation density counting device includes a stage (20) for placing the measurement object; The image processing device (70) includes a detection component that performs corrosion pit detection and analysis on the acquired image based on the material type of the measurement object to obtain the dislocation density of the measurement object; The detection component includes: 1) An image reading module that reads the acquired image; 2) A corrosion pit screening module that receives the information from the image reading module and sets the screening conditions for corrosion pits according to the corrosion pit shapes of different materials, and obtains the average area value S0 of a single corrosion pit and the ratio R0 of the lengths of the long and short sides of the average bounding rectangle; 3) A contour detection module that receives the information from the image reading module and performs contour detection on the black shapes in the acquired image to obtain the contour area S and the ratio R of the lengths of the long and short sides of the bounding rectangle of the black shape; 4) A corrosion pit filtering condition module that receives the information from the corrosion pit screening module and the contour detection module and sets the corrosion pit filtering conditions: When and then record the number of corrosion pits in the black shape as 1, where k 10 and k 11 are single corrosion pit filtering coefficients; When and then record the number of corrosion pits in the black shape as 2, where k 21 is the filtering coefficient for two overlapping corrosion pits; When and then, record the number of corrosion pits of the black shape as 3, where k 31 is the filtering coefficient of three overlapping corrosion pits; and When the total number of corrosion pit overlapping regions in the acquired image exceeds a preset threshold or the acquired image contains multiple corrosion pit overlapping regions with more than 3 overlapping corrosion pits, calculate the total number of corrosion pits in the acquired image according to the following formula: Total number of corrosion pits = Total area of the black shapes in the image / Average area value S0 of a single corrosion pit; and 5) A corrosion pit density calculation module that obtains the total number of corrosion pits in the acquired image through the above counting, and calculates the corrosion pit density of the measurement object as the dislocation density of the measurement object based on the total area of the acquired image and the total number of corrosion pits in the acquired image.

21. The dislocation density counting device according to claim 20, characterized in that, The image acquisition device includes a camera (50), and the pixel size of the camera (50) is not greater than 2.0μm × 2.0μm.

22. The dislocation density counting device according to claim 20 or 21, characterized in that, The image acquisition device includes a lens (40), and the normal direction of the lens (40) is perpendicular to the surface of the stage (20).

23. The dislocation density counting device according to claim 20 or 21, characterized in that The dislocation density counting device includes a stage (20) for placing the measurement object and a vacuum chuck (30). The stage (20) is provided with a through hole, and the vacuum chuck (30) is arranged below the stage (20) to fix the measurement object on the stage (20) through the through hole.

24. The dislocation density counting device according to claim 20 or 21, characterized in that, The support mechanism of the stage (20) is provided with a guide rail system to enable it to move horizontally.

25. The dislocation density counting device according to claim 20 or 21, characterized in that, The detection component includes an image processing module that performs binarization processing on the acquired image.

Images