Method and device for measuring grain size

Abstract

The invention relates to a method and a device for measuring a grain size. The method for measuring the grain size comprises the following steps: acquiring a polycrystal surface image of a polycrystal to be measured; gray value data on a set sampling line of the polycrystalline surface image is collected, and the set sampling line crosses a plurality of crystal grain areas of the polycrystalline surface image; fourier transform is carried out on the gray value data to obtain Fourier data corresponding to the gray value data; based on Fourier data, a characteristic grain size of the polycrystal is determined. And the characteristic grain size of the polycrystal can be effectively and accurately analyzed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor technology, in particular to a grain size measurement method and device. BACKGROUND

[0002] In semiconductor manufacturing, poly-silicon (Poly-Si) is a basic material, which is widely used in various semiconductor device structures and manufacturing links due to its unique electrical properties and process compatibility. Poly-silicon is composed of many tiny grains (domains), and the grain size directly affects the electrical properties, process reliability and subsequent process compatibility, while the temperature and time of poly-silicon processing have a great influence on the grain size. The poly-silicon particle size can be directly observed by SEM (scanning electron microscope) / AFM (atomic force microscope) to observe the surface morphology. If the particle size data needs to be quantified, image analysis needs to be performed. The existing analysis method of particle size is to segment the particles by the threshold analysis method (Threshold) of ImageJ (a Java-based open source image processing software) to count the particle size. However, for the poly-silicon surface image, the gray transition is not obvious, and there is adhesion between the particles, so the segmentation effect is poor, which will cause a relatively large measurement deviation. SUMMARY

[0003] A series of simplified concepts are introduced in the summary section, which will be further described in detail in the specific embodiments section. The summary section of the present application does not mean to attempt to limit the key features and necessary technical features of the claimed technical solutions, nor to attempt to determine the protection scope of the claimed technical solutions.

[0004] In view of the existing problems, the first aspect of the embodiment of the present application provides a grain size measurement method, which comprises:

[0005] obtaining a poly-crystal surface image of a poly-crystal to be measured;

[0006] collecting gray value data on a set sampling line of the poly-crystal surface image, wherein the set sampling line spans a plurality of grain regions of the poly-crystal surface image;

[0007] performing Fourier transform on the gray value data to obtain Fourier data corresponding to the gray value data;

[0008] determining a characteristic grain size of the poly-crystal based on the Fourier data.

[0009] In some embodiments of the present application, collecting gray value data on the set sampling line of the poly-crystal surface image comprises:

[0010] drawing a straight line spanning the plurality of grain regions on the poly-crystal surface image as the set sampling line;

[0011] Taking one end of the set sampling line as a starting end and the other end as a terminal end, position information and gray value information of a plurality of pixel points on the set sampling line are sequentially collected to obtain the gray value data.

[0012] In some embodiments of the present application, the set sampling line spans at least 20 grain regions of the polycrystalline surface image.

[0013] In some embodiments of the present application, based on the Fourier data, the characteristic grain size of the polycrystalline is determined, comprising:

[0014] Based on the Fourier data, the power spectrum corresponding to the gray value data is obtained.

[0015] A characteristic peak on the power spectrum is identified, and a characteristic spatial frequency corresponding to the characteristic peak is determined.

[0016] Based on the characteristic spatial frequency, the characteristic grain size of the polycrystalline is determined.

[0017] In some embodiments of the present application, based on the characteristic spatial frequency, the characteristic grain size of the polycrystalline is determined, comprising:

[0018] The reciprocal of the characteristic spatial frequency is taken as the pixel characteristic grain size corresponding to the plurality of grain regions.

[0019] The product of the pixel characteristic grain size and the image resolution of the polycrystalline surface image is taken as the characteristic grain size of the polycrystalline.

[0020] In some embodiments of the present application, the characteristic peak on the power spectrum is identified, comprising:

[0021] The wave peak with the largest amplitude among all wave peaks of the power spectrum is taken as the characteristic peak on the power spectrum.

[0022] In some embodiments of the present application, based on the Fourier data, the power spectrum corresponding to the gray value data is obtained, comprising:

[0023] The Fourier data is subjected to a modulus operation, and the power spectrum corresponding to the gray value data is plotted according to the result of the modulus operation.

[0024] In some embodiments of the present application, the polycrystalline surface image of the polycrystalline to be measured is obtained, comprising:

[0025] The surface of the polycrystalline is imaged by using a scanning electron microscope or an atomic force microscope to obtain the polycrystalline surface image.

[0026] In some embodiments of the present application, the polycrystalline includes polycrystalline silicon.

[0027] The second aspect of the embodiment of the present application provides a grain size measuring device, the measuring device comprising a memory and a processor, wherein the memory stores a computer program which is run by the processor, and the computer program, when being run by the processor, causes the processor to perform any of the grain size measuring methods.

[0028] According to the grain size measuring method and device provided by the present application, the gray value data on the set sampling line spanning the multiple grain regions of the polycrystal surface image is collected, the Fourier data corresponding to the gray value data is obtained by performing Fourier transform on the gray value data, and the characteristic grain size of the polycrystal is determined based on the Fourier data, so that the characteristic grain size of the polycrystal can be effectively and accurately analyzed. BRIEF DESCRIPTION OF DRAWINGS

[0029] The following drawings of the present application are hereby incorporated as part of the present application for the purpose of understanding the present application. The embodiments of the present application and the description thereof shown in the drawings are used to explain the principles of the present application.

[0030] In the drawings:

[0031] Figure 1 An effect diagram of the grain segmentation of the polycrystal surface image by using the existing threshold analysis method is shown;

[0032] Figure 2 A flow diagram of the grain size measuring method in an embodiment of the present application is shown;

[0033] Figure 3 A diagram of the set sampling line on the polycrystal surface image in an embodiment of the present application is shown;

[0034] Figure 4 A diagram of the gray-distance curve in an embodiment of the present application is shown;

[0035] Figure 5 A diagram of the power spectrum in an embodiment of the present application is shown;

[0036] Figure 6 A result diagram of the manual measurement of the grain size is shown.

[0037] Reference signs:

[0038] 301-polycrystal surface image; 302-set sampling line DETAILED DESCRIPTION

[0039] In the following description, numerous specific details are set forth to provide a more thorough understanding of the present application. However, it will be apparent to one of skill in the art upon

[0040] It should be understood that the present application can be practiced with the elements in different order, and that none is necessary before another in order for the application to be practiced. Furthermore, some of the features of the present application can be used to advantage without the corresponding use of other features. As such, the foregoing description should be considered as merely illustrative of the principles of the present application, and not in limitation thereof. For the sake of brevity and clarity, detailed descriptions of functions integral to the art will not be described in more detail, such as the detailed construction of integrated circuit chips, logic elements that perform various computing functions, or the software processes that execute on computer or networked systems.

[0041] It is to be understood that the use of terms such as "above," "below," "upper," "lower," and the like are used to aid in describing the relative location of elements in the figures. It is to be understood that the use of such terms is not intended to limit the scope of the application to only those embodiments described in the specification. It is to be understood that the use of such terms is understood to encompass the spatial relationship of the elements in the figures relative to one another, as well as the spatial relationship of the elements relative to the viewer of the figures. It is to be understood that the spatial relationship of the elements relative to one another in the figures is not limited to the orientation of the figures in the drawings. It is to be understood that the spatial relationship of the elements relative to one another in the figures is not limited to the orientation of the figures in the drawings.

[0042] Spatially relative terms, such as "beneath", "below", "lower", "above", "upper" and the like, can be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "below" or "beneath" other elements or features would then be oriented "above" or "over" the other elements or features. Thus, the exemplary term "below" can encompass both an orientation of above and below. The device can be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.

[0043] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising", when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein the term "and / or" includes any and all combinations of associated items.

[0044] The existing scheme for segmenting particles to count particle sizes by threshold analysis method has poor segmentation effect when applied to polycrystalline materials (such as ceramics, metal alloys) with unclear gray transition, for example, refer to Figure 1 The threshold analysis method is to perform color change processing on each pixel point on the image according to whether the gray value of the pixel point is greater than the threshold value, for example, refer to Figure 1 The grain distribution map segmented by the threshold analysis method cannot effectively segment the grains due to grain adhesion, inaccurate threshold setting, etc., resulting in large measurement deviation, and the threshold value is set by experience, which is highly subjective.

[0045] To solve at least part of the above problems, the present application proposes the following embodiments.

[0046] Embodiment one

[0047] Referring to Figure 2 The present application proposes a grain size measurement method, which comprises:

[0048] In S201, a polycrystalline surface image of a polycrystalline body to be measured is acquired;

[0049] In S202, gray value data on a set sampling line of the polycrystalline surface image is collected, wherein the set sampling line spans multiple grain regions of the polycrystalline surface image;

[0050] In S203, Fourier transform is performed on the gray value data to obtain Fourier data corresponding to the gray value data;

[0051] In S204, a characteristic grain size of the polycrystalline body is determined based on the Fourier data.

[0052] In the above scheme, the gray value data on the set sampling line spanning multiple grain regions of the polycrystalline surface image is collected, and the Fourier transform is performed on the gray value data to obtain the Fourier data corresponding to the gray value data, and then the characteristic grain size of the polycrystalline body is determined based on the Fourier data, so that the characteristic grain size of the polycrystalline body can be effectively and accurately analyzed.

[0053] The scheme proposed in the present application collects the gray value data on the set sampling line of the polycrystal surface image, the set sampling line spans multiple grain regions of the polycrystal surface image, so that the gray value change information of the grain boundaries between different grain regions is reflected in the collected gray value data; then the Fourier data corresponding to the gray value data is obtained by Fourier transform of the gray value data, the periodic distribution information of the grain boundaries on the set sampling line is accurately reflected in the Fourier data; then the characteristic grain size of the polycrystal can be determined based on the Fourier data. The Fourier transform is used to extract the characteristic of strong periodic distribution ability. Compared with the existing threshold analysis method, the scheme of the present application can effectively extract the grain boundary information on the set sampling line even if the particles are adhered, so as to effectively and accurately analyze the characteristic grain size of the polycrystal.

[0054] Next, with reference to Figures 1 to 6 The grain size measurement method of the embodiment of the present application is described in detail.

[0055] First, with reference to Figure 3 , the polycrystal surface image 301 of the polycrystal to be measured is obtained. When obtaining the polycrystal surface image 301 of the polycrystal to be measured, a variety of ways can be used. Some ways are exemplarily introduced as follows.

[0056] Exemplarily, obtaining the polycrystal surface image 301 of the polycrystal to be measured can include: using a scanning electron microscope or an atomic force microscope to image the surface of the polycrystal to obtain the polycrystal surface image 301, so that the grain boundary image on the collected polycrystal surface image 301 is as clear as possible, thereby improving the accuracy of the measurement result. For example, Figure 3 As shown in one polycrystal surface image 301 obtained by image collection using a scanning electron microscope. Exemplarily, the surface of the polycrystal can be directly imaged. In some other embodiments, the polycrystal can be cut, and the cutting surface formed by cutting can be used as the surface of the polycrystal for image collection.

[0057] Regarding the material type of the polycrystal, any type of polycrystal material can be used, and the polycrystal contains grains of different sizes. For example, the polycrystal can include polycrystalline silicon, and the polycrystalline silicon contains silicon grains of different sizes.

[0058] Next, with reference to Figure 3acquire the gray value data on the set sampling line 302 of the polycrystalline surface image 301, wherein the set sampling line 302 crosses the multiple grain regions of the polycrystalline surface image 301, so that the grain boundary information of the multiple grains is covered on the set sampling line 302. The set sampling line 302 can be a straight line, and the extension direction of the straight line can be any direction, i.e., the set sampling line 302 is not limited to a straight line parallel or perpendicular to the edge of the polycrystalline surface image 301.

[0059] When acquiring the gray value data on the set sampling line 302 of the polycrystalline surface image 301, various methods can be used, and some methods are exemplarily introduced as follows.

[0060] Exemplarily, acquiring the gray value data on the set sampling line 302 of the polycrystalline surface image 301 can include:

[0061] drawing a straight line crossing the multiple grain regions on the polycrystalline surface image 301 as the set sampling line 302;

[0062] taking one end of the set sampling line 302 as a starting end and the other end as a terminal end, sequentially acquiring the position information and the gray value information of the multiple pixel points on the set sampling line 302, and obtaining the gray value data.

[0063] Exemplarily, the gray value data can be in the form of table data, and the position information of the multiple pixel points on the set sampling line 302 and the gray value information of each pixel point are sequentially or reversely listed. Exemplarily, refer to Figure 4 The gray value data can also be presented in the form of a fitted curve. For example, after acquiring the position information and the gray value information of the multiple pixel points on the set sampling line 302, the distance from the starting end of the set sampling line 302 is taken as the horizontal coordinate to reflect the position information of the pixel points on the set sampling line 302, and the gray value of the pixel point is taken as the vertical coordinate to reflect the gray value information of each pixel point. The gray value data of the multiple pixel points is plotted on the rectangular coordinate system, and then a gray-distance curve is plotted by fitting, which can also be referred to as a gray profile curve on the set sampling line 302 of the polycrystalline surface image 301.

[0064] When drawing a straight line across multiple grain regions on the polycrystalline surface image 301 as the set sampling line 302, various ways can be adopted. Exemplarily, the polycrystalline surface image 301 can be opened in an image analysis software (such as ImageJ, etc.), and then a straight line tool in the image software can be used to draw a line across multiple grain regions. Then, the gray scale values of the multiple pixel points on the set sampling line 302 can be collected in sequence according to the set interval, with one end of the set sampling line 302 as the starting end and the other end as the ending end, to generate a gray scale-distance curve for the drawn line position.

[0065] Exemplarily, the size of the set interval, the length of the set sampling line 302, and other dimensions can be controlled to determine the number of collected pixel points, so as to control the measurement accuracy. For example, the set interval can be set to be smaller, so that more pixel values of the pixel points can be collected for the set sampling line 302 of the same length, to improve the measurement accuracy. For example, a longer set sampling line 302 can be set, so that the set sampling line 302 crosses more grain regions of the polycrystalline surface image 301, thereby reducing the influence of accidental errors and improving the measurement accuracy.

[0066] In some embodiments, the set sampling line 302 crosses at least 20 grain regions of the polycrystalline surface image 301. For example, the number of grain regions on the polycrystalline surface image 301 crossed by the set sampling line 302 can be 20, 25, 30, etc., any number not less than 20, so as to further reduce the influence of accidental errors and improve the measurement accuracy.

[0067] Next, the gray scale value data is subjected to Fourier transform (FFT) to obtain Fourier data corresponding to the gray scale value data. The Fourier data can include spatial amplitude spectrum data or spatial phase spectrum data, and the information that the set sampling line 302 crosses multiple grain regions can be reflected in the Fourier data.

[0068] When the gray scale value data is subjected to Fourier transform, various ways can be adopted. For example, the gray scale value data can be input into Excel / Matlab / Python to perform Fourier transform to obtain transformed Fourier data. Exemplarily, the transformed Fourier data can be presented in the form of a Fourier array, and can also be presented in other forms such as but not limited to spatial amplitude spectrum and spatial phase spectrum.

[0069] Next, the characteristic grain size of the polycrystal is determined based on the Fourier data. Regarding methods for determining the characteristic grain size of the polycrystal based on Fourier data, any calculation method can be used to extract the periodically varying grain boundary information reflected in the Fourier data, and then calculate the characteristic grain size of the polycrystal based on the extracted periodically varying grain boundary information. The following are some examples of methods.

[0070] Exemplary, reference Figure 5 Determining the characteristic grain size of a polycrystalline material based on Fourier transform data may include: obtaining a power spectrum (also known as a power density spectrum) corresponding to the grayscale value data based on the Fourier transform data; identifying characteristic peaks on the power spectrum and determining characteristic spatial frequencies corresponding to the characteristic peaks; and determining the characteristic grain size of the polycrystalline material based on the characteristic spatial frequencies. By performing a Fourier transform on the grayscale value data on a set sampling line 302 of the polycrystalline surface image 301, a power spectrum corresponding to the grayscale value data is plotted. By utilizing the spatial frequency characteristics corresponding to characteristic peaks on the power spectrum, the periodic grayscale value variation information of the grain boundaries of the multiple grain regions spanned by the set sampling line 302 is reflected in the power spectrum. The principle is that because the set sampling line 302 spans multiple grain regions, the grayscale value variation information of the grain boundaries between different grains will appear multiple times periodically from the starting end to the end end of the set sampling line 302, with a higher frequency of occurrence. This is reflected in the characteristic peaks of the power spectrum, which have larger amplitudes and contribute a higher power density. The characteristic peak corresponds to a characteristic spatial frequency, which reflects the periodic spatial information of the grain boundaries appearing on the set sampling line 302. Therefore, the characteristic grain size of the polycrystalline material can be determined based on the characteristic spatial frequency, simplifying the difficulty of calculating the actual characteristic size and improving the accuracy of the calculation. The above method effectively analyzes the characteristic grain size of the polycrystalline material by converting the grayscale value data into corresponding Fourier data and then converting the periodic variation information of the Fourier data into a power spectrum with spatial frequency.

[0071] It should be noted that the characteristic grain size does not refer to the average size of grains in a polycrystalline body. Its physical meaning refers to the length of each grain along the set sampling line 302 spanned by the multiple grains. The actual number of characteristic sizes is not limited to one; it can also be any number, such as two or three. It reflects the distribution of the length occupied by the grains along the set sampling line 302 and can be used to characterize the size range of the grains.

[0072] There are many ways to identify characteristic peaks on the power spectrum, some of which are exemplified below.

[0073] In some embodiments, reference Figure 5, identifying the characteristic peak on the power spectrum may include: taking the peak with the largest amplitude among all the peaks of the power spectrum as the characteristic peak on the power spectrum. Figure 5 As shown, peak A, with the largest amplitude among all peaks in the power spectrum, is used as a characteristic peak. The physical meaning of the peak with the largest amplitude among all peaks in the power spectrum is that it has the highest power density. The characteristic grain size of the polycrystalline material calculated by using the peak with the largest amplitude as the characteristic peak appears most frequently in the multiple grain regions spanned by the set sampling line 302, making it more representative, thereby improving the accuracy of the characteristic grain size of the polycrystalline material obtained by the final quantitative analysis. It should be noted that the number of peaks with the largest amplitude among all peaks in the power spectrum is not limited to one, and may also exist in two or more cases.

[0074] In some embodiments, the power spectrum corresponding to the gray value data can be obtained based on the Fourier data in a variety of ways. Figure 5 Obtaining a power spectrum corresponding to the grayscale value data based on the Fourier data may include performing a modulo operation on the Fourier data, and plotting the power spectrum corresponding to the grayscale value data based on the result of the modulo operation. Specifically, when obtaining the power spectrum corresponding to the grayscale value data based on the Fourier data, various methods may be used. For example, the Fourier data may be presented as a Fourier array. In this case, a modulo operation may be performed on the transformed Fourier array, and the corresponding power spectrum may be plotted based on the result of the modulo operation.

[0075] Of course, in other embodiments, the amplitude spectrum corresponding to the grayscale value data can be obtained based on Fourier data, and the actual characteristic size of the grains can also be calculated based on the amplitude spectrum. The specific method is basically the same as the method based on the power spectrum. Specifically, it includes identifying characteristic peaks in the amplitude spectrum and determining the characteristic spatial frequencies corresponding to the characteristic peaks; based on the characteristic spatial frequencies, the characteristic grain size of the polycrystalline material is determined. Identifying the characteristic peaks in the amplitude spectrum can include: determining the peak with the largest amplitude among all peaks in the amplitude spectrum as the characteristic peak in the amplitude spectrum.

[0076] There are many ways to determine the characteristic grain size of a polycrystalline material based on the characteristic spatial frequency. Figure 5 Determining the characteristic grain size of the polycrystal based on the characteristic spatial frequency may include: using the inverse of the characteristic spatial frequency as the pixel characteristic grain size corresponding to the multiple grain regions; and using the product of the pixel characteristic size and the image resolution of the polycrystal surface image 301 as the characteristic grain size of the polycrystal.

[0077] The inverse of the characteristic spatial frequency is equal to the periodic information, and the spatial periodic information precisely reflects the pixel characteristic size information of the grains on the set sampling line 302. Then, based on the image resolution of the polycrystalline surface image 301, the product of the pixel characteristic size and the image resolution is used as the actual characteristic size of the grains, thereby simplifying the calculation difficulty. The specific principle is as follows:

[0078] In the above method, the physical meaning of the reciprocal of the characteristic spatial frequency represents the pixel grain size with the strongest periodic distribution in the multiple grain regions spanned by the set sampling line 302. This application uses this pixel grain size as the pixel characteristic grain size of the multiple grain regions. The unit of the pixel characteristic grain size is pixel, not actual size. When converting the pixel characteristic grain size into actual size, the product of the pixel characteristic grain size and the image resolution of the polycrystalline surface image 301 is used as the actual grain size with the strongest periodic distribution in the multiple grain regions spanned by the set sampling line 302. This application uses the line (set sampling line 302) and the surface (polycrystalline surface image 301 of the polycrystal), and then the surface (polycrystalline surface image 301 of the polycrystal) and the volume (polycrystal) to represent the actual grain size with the strongest periodic distribution in the multiple grain regions spanned by the set sampling line 302, thereby improving the accuracy of the characteristic grain size obtained by quantitative analysis.

[0079] For example, reference Figure 4 A grayscale-distance curve representing grayscale value data is shown. Figure 5 It is based on Figure 4 The power spectrum obtained by Fourier transforming the grayscale value data shown is calculated, and the spatial frequency corresponding to the characteristic peak A with the largest amplitude on the power spectrum is identified as f0. The pixel characteristic grain size D corresponding to the multiple polycrystalline regions is the inverse of the spatial frequency f0. For example, Figure 3 The image resolution of the polycrystalline surface image 301 shown is: 1 pixel = 3.125 nm. Figure 5 The spatial frequency corresponding to the characteristic peak A in the power spectrum shown is 0.0234, so the inverse of the spatial frequency corresponding to the characteristic peak A is 42.7 pixels (which is also the calculation result of the pixel characteristic grain size D corresponding to multiple polycrystalline regions). Converted to the actual size is 0.13um, which means that the characteristic grain size of the polycrystalline includes 0.13um. Figure 6 The result of a manual measurement of particle size is shown. The measured grain size is 0.14um, which verifies that the above-mentioned grain size measurement result is very close to the actual result and has a relatively high accuracy.

[0080] So far, the main steps of the grain size measurement method according to the embodiment of the present application are completed. It can be understood that the grain size measurement method of the embodiment not only includes the above steps, but also can include other required steps, which are included in the scope of the grain size measurement method of the embodiment.

[0081] Embodiment two

[0082] The embodiment of the present application further provides a grain size measurement device, which comprises a memory and a processor, wherein the memory stores a computer program running by the processor, and the computer program, when running by the processor, causes the processor to execute any one of the grain size measurement methods.

[0083] The person skilled in the art can understand the specific operation of the grain size measurement device deployment device according to the embodiment of the present application in combination with the foregoing content, and for brevity, will not be repeated here. The storage medium may, for example, include a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer readable storage medium can be any combination of one or more computer readable storage media.

[0084] The present application has been described by the above embodiments, but it should be understood that the above embodiments are only for the purpose of example and illustration, and are not intended to limit the present application to the scope of the described embodiments. Furthermore, those skilled in the art can understand that the present application is not limited to the above embodiments, and more variants and modifications can be made according to the teachings of the present application, which all fall within the scope of the present application. The scope of protection of the present application is defined by the attached claims and their equivalent scope.

Claims

1. A method for measuring grain size, characterized in that: include: Acquiring a polycrystalline surface image of the polycrystalline body to be measured; Collecting grayscale value data on a set sampling line of the polycrystalline surface image, wherein the set sampling line spans across multiple grain regions of the polycrystalline surface image; Performing Fourier transform on the grayscale value data to obtain Fourier data corresponding to the grayscale value data; Based on the Fourier data, a characteristic grain size of the polycrystalline body is determined.

2. The measuring method according to claim 1, wherein The collecting of grayscale value data on a set sampling line of the polycrystalline surface image includes: drawing a straight line across the plurality of grain regions on the polycrystalline surface image as the set sampling line; With one end of the set sampling line as the starting end and the other end as the ending end, the position information and gray value information of multiple pixel points on the set sampling line are collected in sequence to obtain the gray value data.

3. The measuring method according to claim 2, wherein: The set sampling line spans at least 20 of the grain regions of the polycrystalline surface image.

4. The measuring method according to any one of claims 1 to 3, wherein: Determining the characteristic grain size of the polycrystalline body based on the Fourier data includes: Based on the Fourier data, obtaining a power spectrum corresponding to the grayscale value data; Identifying a characteristic peak on the power spectrum and determining a characteristic spatial frequency corresponding to the characteristic peak; The characteristic grain size of the polycrystalline body is determined based on the characteristic spatial frequency.

5. The measuring method according to claim 4, wherein: Determining the characteristic grain size of the polycrystalline material based on the characteristic spatial frequency includes: Using the reciprocal of the characteristic spatial frequency as the pixel characteristic grain size corresponding to the plurality of grain regions; The product of the pixel characteristic grain size and the image resolution of the polycrystalline surface image is used as the characteristic grain size of the polycrystalline body.

6. The measuring method according to claim 4, wherein: The identifying a characteristic peak on the power spectrum includes: The peak with the largest amplitude among all the peaks of the power spectrum is taken as the characteristic peak on the power spectrum.

7. The measuring method according to claim 4, wherein: The obtaining, based on the Fourier data, a power spectrum corresponding to the grayscale value data, comprises: A modulo operation is performed on the Fourier data, and the power spectrum corresponding to the grayscale value data is plotted according to a result of the modulo operation.

8. The measuring method according to claim 1, wherein: The step of obtaining a surface image of a polycrystal to be measured comprises: The surface image of the polycrystal is acquired by using a scanning electron microscope or an atomic force microscope to capture the surface of the polycrystal.

9. The measuring method according to claim 1, wherein: The polycrystalline body includes polycrystalline silicon.

10. A device for measuring grain size, characterized in that: include: A memory and a processor, wherein the memory stores a computer program to be executed by the processor, and when the computer program is executed by the processor, the processor executes the grain size measurement method according to any one of claims 1 to 9.

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