Cleaning control system and method for double-side-polished sapphire wafer

Abstract

The present invention belongs to the field of cleaning control over double-side-polished sapphire wafers. Disclosed are a cleaning control system and method for a double-side-polished sapphire wafer. The method in the present invention comprises: acquiring cleaning data, acquiring an image of a double-side-polished sapphire wafer and data before and after cleaning, and processing the image; constructing a cleaning effect evaluation model, and importing scratch-related parameters and the number of surface particles before and after cleaning into the cleaning effect evaluation model for evaluation; constructing a hair brush wear degree evaluation model, and importing, into the hair brush wear degree evaluation model, process parameters of a hair brush cleaning the double-side-polished sapphire wafer, so as to evaluate the wear degree of the hair brush; and constructing a hair brush remaining life evaluation model, and importing the wear amount of the hair brush, the cleaning effect of the hair brush and the thickness of the hair brush into the hair brush remaining life evaluation model, so as to evaluate the remaining life of the hair brush. In the prevent invention, a cleaning effect is monitored in real time, a wear situation of a hair brush is evaluated, and the life of the hair brush is predicted on the basis of a wear model, thereby reducing negative effects of the hair brush on a double-side-polished wafer cleaning process.

Description

Cleaning control system and method for sapphire double-polished wafers Technical Field

[0001] This invention belongs to the field of cleaning control for sapphire double-polished wafers, specifically a cleaning control system and method for sapphire double-polished wafers. Background Technology

[0002] Sapphire double-polished wafers are widely used in LED substrates, optical windows, semiconductor substrates, and other fields. Their surface quality has a significant impact on subsequent processing and device performance. During grinding, polishing, and cleaning, tiny particles, scratches, or other defects may remain on the surface of the sapphire double-polished wafer. If the cleaning effect is inadequate, it may lead to device failure in subsequent processes, affecting product yield.

[0003] Traditional sapphire double-polished wafer cleaning relies heavily on manual experience to judge cleaning effectiveness, making quantitative control difficult. Furthermore, the lifespan of the brushes is primarily determined by fixed replacement cycles, failing to dynamically adjust based on actual wear. This leads to premature brush replacement resulting in waste, or delayed replacement affecting cleaning quality. This invention proposes a cleaning control system and method for sapphire double-polished wafers. This system can monitor cleaning effectiveness in real time, assess brush wear, predict brush lifespan based on a wear model, ensure the stability of the cleaning process, and enable timely brush replacement, reducing the negative impact of brush wear on the double-polished wafer cleaning process. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a cleaning control system and method for sapphire double-polished wafers.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The cleaning and control method for sapphire double-polished wafers includes the following specific steps:

[0007] Acquire cleaning data, obtain images of sapphire double polished wafers and data before and after cleaning, and process the images;

[0008] A cleaning effect evaluation model was constructed, and scratch-related parameters and the number of surface particles before and after cleaning were imported into the cleaning effect evaluation model for evaluation.

[0009] A brush wear assessment model was constructed, and the process parameters of cleaning sapphire double polishing plates with brushes were imported into the brush wear assessment model to evaluate the degree of brush wear.

[0010] A brush remaining life assessment model was constructed, and brush wear, brush cleaning effect, and brush thickness were imported into the brush remaining life assessment model to evaluate the remaining life of the brush.

[0011] Preferably, the steps of acquiring cleaning data, acquiring sapphire double-polished wafer images and data before and after cleaning, and processing the images include the following specific steps:

[0012] S11. Acquire images of the sapphire double-polished wafer before and after double-sided cleaning using a high-resolution camera. Establish a rectangular coordinate system with the center of the sapphire double-polished wafer as the origin. Use a Gaussian filter to denoise the acquired images of the sapphire double-polished wafer before and after double-sided cleaning. The Gaussian filter formula is as follows: , where (x,y) are the pixel coordinates in the image, σ is the standard deviation, and k is the radius of the filter window;

[0013] S12. The image threshold is segmented using the maximum inter-class variance method. All possible grayscale thresholds are traversed, and the variance between the foreground and background classes is calculated at each threshold. The threshold with the largest variance is selected as the optimal segmentation threshold to separate the scratch area from the background. The background of the image is removed by image subtraction to highlight the scratch area. The difference between the two two-dimensional image data is obtained by subtracting the absolute value of the two images and outputting a difference image data. The image subtraction calculation formula is: g(x,y)=I(x,y)-J(x,y), where I(x,y) is the image after threshold segmentation and J(x,y) is the background comparison image.

[0014] S13. The depth, three-dimensional morphology and number distribution of scratches on the surface of the cleaned double polished plate are measured by a shape measurement laser microscopy system, and the scratch positions are obtained by establishing a rectangular coordinate system.

[0015] S14. Detect the number of particles on the surface of the sapphire double polished wafer before and after cleaning using a particle detector to obtain the number of particles on the surface before and after cleaning.

[0016] Preferably, the construction of the cleaning effect evaluation model, which involves importing scratch-related parameters and the number of surface particles before and after cleaning into the cleaning effect evaluation model for evaluation, includes the following specific steps:

[0017] S21. Substitute the relevant scratch parameters into the scratch comprehensive assessment index calculation formula to evaluate the scratch severity. The scratch comprehensive assessment index calculation formula is as follows: Where S is the single-sided area of ​​the sapphire double-sided polished sheet, and d i h is the depth of the i-th scratch. i Let be the length of the i-th scratch, and n be the total number of scratches on both sides;

[0018] S22. Substitute the number of particles on the surface of the sapphire double-polished wafer before and after cleaning into the particle removal rate calculation formula to calculate the particle removal rate. The particle removal rate calculation formula is as follows: Where D1 is the number of surface particles on both sides of the sapphire double-polished wafer before cleaning, and D2 is the number of surface particles on both sides of the sapphire double-polished wafer after cleaning.

[0019] Preferably, the construction of the brush wear assessment model, which involves importing the process parameters of cleaning the sapphire double polishing sheet with a brush into the brush wear assessment model to evaluate the brush wear, includes the following specific steps:

[0020] S31. Compare the comprehensive scratch assessment index with the scratch assessment threshold. If the comprehensive scratch assessment index is greater than the scratch assessment threshold, replace the brush immediately. At the same time, compare the particle removal rate with the minimum particle removal rate threshold. If the particle removal rate is less than the minimum particle removal rate threshold, replace the brush immediately.

[0021] S32. If both the scratch comprehensive evaluation index and particle removal rate are within the threshold range, substitute the process parameters of the sapphire dual-polish brush cleaning process into the brush wear calculation formula to calculate the brush wear. The brush wear calculation formula per unit time is based on the Archard wear equation, quantifying the brush wear through brush material characteristics, pressure, sliding distance, and the corrosiveness of the cleaning fluid to the brush. The brush wear calculation formula per unit time is as follows: K is the wear coefficient, F is the pressure applied by the brush, L is the sliding distance between the brush and the sapphire sheet per unit time, Z is the hardness of the brush material, and C is the corrosion coefficient of the cleaning fluid on the brush. The formula for calculating the sliding distance is: L=2πrvt, where r is the contact radius of the brush, v is the rotation speed of the brush, and t is the unit time.

[0022] Preferably, the construction of the remaining brush life assessment model, which incorporates brush wear, brush cleaning effect, and brush thickness into the model to assess the remaining brush life, includes the following specific steps:

[0023] S41. Substitute the brush wear and brush thickness into the formula for calculating the remaining brush life to calculate the remaining brush life. The formula for calculating the remaining brush life is as follows: Where λ is the cleaning evaluation factor, H0 is the real-time thickness of the brush, and H min The minimum allowable thickness of the brush is given, where the formula for calculating the cleaning evaluation factor is: Where A is the overall scratch assessment index, A r P is the scratch assessment threshold, and P is the particle removal rate. r The particle removal rate threshold is used to comprehensively evaluate the impact of the actual working condition of the brush on its remaining life by incorporating the scratch comprehensive evaluation index and particle removal rate into the cleaning evaluation factor.

[0024] S42. Compare the remaining lifespan of the brush with the brush lifespan threshold. If it is less than or equal to the threshold, replace the brush immediately. If it is close to the threshold, issue a warning.

[0025] The cleaning control system for sapphire double-polished wafers is based on the cleaning control method for sapphire double-polished wafers described above, and specifically includes:

[0026] The data acquisition module is used to acquire images of sapphire double polished wafers and data before and after cleaning, and to process the images.

[0027] The cleaning effect evaluation module is used to evaluate the cleaning effect by using scratch-related parameters and the number of particles on the surface before and after cleaning;

[0028] The brush wear assessment module is used to assess the degree of brush wear through process parameters of cleaning sapphire double polishing plates with a brush.

[0029] The remaining brush life assessment module is used to assess the remaining brush life based on brush wear, brush cleaning effect, and brush thickness.

[0030] An electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0031] The processor executes the cleaning control method for sapphire double-polished wafers by calling the computer program stored in the memory.

[0032] A computer-readable storage medium is characterized by storing instructions that, when executed on a computer, cause the computer to perform the above-described cleaning control method for sapphire double-polished wafers.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] This invention acquires cleaning data, including images of sapphire double-polished wafers and data before and after cleaning. The images are processed to construct a cleaning effect evaluation model. Scratch-related parameters and the number of surface particles before and after cleaning are imported into the cleaning effect evaluation model for assessment. A brush wear evaluation model is also constructed, incorporating process parameters from the brush cleaning process of the sapphire double-polished wafers to assess brush wear. Finally, a brush remaining lifespan evaluation model is constructed, importing brush wear amount, brush cleaning effect, and brush thickness to evaluate the remaining brush lifespan. This invention assesses brush wear in real-time by monitoring the cleaning effect and predicts brush lifespan based on the wear model, thereby reducing the negative impact of the brush on the double-polished wafer cleaning process. Attached Figure Description

[0035] Figure 1 is a schematic diagram of the overall process of the cleaning control method for sapphire double-polished wafers according to the present invention; Figure 2 is a flowchart of the scratch comprehensive evaluation index calculation.

[0036] Figure 3 is a flowchart for calculating the remaining lifespan of the brush.

[0037] Figure 4 is a schematic diagram of the overall framework of the cleaning control system for sapphire double polishing wafers according to the present invention. Detailed Implementation

[0038] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0039] Example 1

[0040] Please refer to Figures 1-3. One embodiment of the present invention provides a cleaning control method for sapphire double-polished wafers, which includes the following specific steps:

[0041] Acquire cleaning data, obtain images of sapphire double polished wafers and data before and after cleaning, and process the images;

[0042] A cleaning effect evaluation model was constructed, and scratch-related parameters and the number of surface particles before and after cleaning were imported into the cleaning effect evaluation model for evaluation.

[0043] A brush wear assessment model was constructed, and the process parameters of cleaning sapphire double polishing plates with brushes were imported into the brush wear assessment model to evaluate the degree of brush wear.

[0044] A brush remaining life assessment model was constructed, and brush wear, brush cleaning effect, and brush thickness were imported into the brush remaining life assessment model to evaluate the remaining life of the brush.

[0045] In this embodiment, it should be specifically explained that acquiring cleaning data, acquiring sapphire double-polished wafer images and data before and after cleaning, and processing the images includes the following specific steps:

[0046] S11. Acquire images of the sapphire double-polished wafer before and after double-sided cleaning using a high-resolution camera. Establish a rectangular coordinate system with the center of the sapphire double-polished wafer as the origin. Use a Gaussian filter to denoise the acquired images of the sapphire double-polished wafer before and after double-sided cleaning. The Gaussian filter formula is as follows: , where (x,y) are the pixel coordinates in the image, σ is the standard deviation, σ determines the smoothness of the filter, a larger σ makes the filtering effect smoother, and k is the radius of the filter window;

[0047] S12. The image threshold is segmented using the maximum inter-class variance method. All possible grayscale thresholds are traversed, and the variance between the foreground and background classes is calculated at each threshold. The threshold with the largest variance is selected as the optimal segmentation threshold to separate the scratch area from the background. The background of the image is removed by image subtraction to highlight the scratch area. The difference between the two two-dimensional image data is obtained by subtracting the absolute value of the two images and outputting a difference image data. The image subtraction calculation formula is: g(x,y)=I(x,y)-J(x,y), where I(x,y) is the image after threshold segmentation and J(x,y) is the background comparison image.

[0048] S13. The depth, three-dimensional morphology and number distribution of scratches on the surface of the cleaned double polished plate are measured by a shape measurement laser microscopy system, and the scratch positions are obtained by establishing a rectangular coordinate system.

[0049] S14. Detect the number of particles on the surface of the sapphire double polished wafer before and after cleaning using a particle detector to obtain the number of particles on the surface before and after cleaning.

[0050] In this embodiment, it should be specifically explained that constructing a cleaning effect evaluation model and importing scratch-related parameters and the number of surface particles before and after cleaning into the cleaning effect evaluation model includes the following specific steps:

[0051] S21. Substitute the relevant scratch parameters into the scratch comprehensive evaluation index calculation formula to assess the scratch severity. The degree of scratches after cleaning is comprehensively evaluated by considering the depth, length, and number of scratches on both sides of the cleaned sapphire double-sided polishing blade. This assesses the negative impact of the brush bristles during the cleaning process. The scratch comprehensive evaluation index calculation formula is as follows: Where S is the single-sided area of ​​the sapphire double-sided polished sheet, and d i h is the depth of the i-th scratch. i Let be the length of the i-th scratch, and n be the total number of scratches on both sides. The scratch depth and length directly reflect the severity of the damage to the surface of the double-sided polished plate. Longer or deeper scratches will penetrate a larger area of ​​the double-sided polished plate surface, increasing the negative impact on surface quality. The formula takes into account the scratch depth, length, and overall area of ​​the double-sided polished plate. Larger scratch depth and length will lead to a larger exponent value, indicating that the scratch is more serious. A smaller exponent value indicates that the scratch has less impact on the surface quality of the double-sided polished plate.

[0052] S22. Substitute the number of particles on the surface of the sapphire double-sided polished wafer before and after cleaning into the particle removal rate calculation formula to calculate the particle removal rate. Evaluate the particle removal effect during the brush cleaning process of the double-sided polished wafer. The change in the number of particles before and after cleaning reflects the change in the cleanliness of the double-sided polished wafer after cleaning compared to before cleaning. The particle removal rate calculation formula is as follows: Where D1 is the number of surface particles on both sides of the sapphire double-polished wafer before cleaning, and D2 is the number of surface particles on both sides of the sapphire double-polished wafer after cleaning.

[0053] In this embodiment, it should be specifically explained that constructing a brush wear assessment model and importing the process parameters of the brush cleaning sapphire double polishing sheet into the brush wear assessment model to evaluate the brush wear includes the following specific steps:

[0054] S31. Compare the comprehensive scratch assessment index with the scratch assessment threshold. If the comprehensive scratch assessment index is greater than the scratch assessment threshold, replace the brush immediately. At the same time, compare the particle removal rate with the minimum particle removal rate threshold. If the particle removal rate is less than the minimum particle removal rate threshold, replace the brush immediately. The scratch assessment threshold and the minimum particle removal rate threshold are determined based on the dual-polish product quality standards and actual production experience. In the production and application of dual-polish products, different products have different tolerances for scratches. By using the dual judgment of the scratch assessment threshold and the minimum particle removal rate threshold, the lifespan of the brush can be predicted in a timely manner.

[0055] S32. If both the scratch comprehensive evaluation index and particle removal rate are within the threshold range, substitute the process parameters of the sapphire dual-polish brush cleaning process into the brush wear calculation formula to calculate the brush wear. The brush wear calculation formula per unit time is based on the Archard wear equation, quantifying the brush wear through brush material characteristics, pressure, sliding distance, and the corrosiveness of the cleaning fluid to the brush. The brush wear calculation formula per unit time is as follows: K is the wear coefficient, which is determined by the characteristics of the brush material. F is the pressure applied by the brush. The greater the pressure, the stronger the contact force between the brush and the surface of the double polishing plate, and the stronger the friction, resulting in a corresponding increase in wear. L is the sliding distance between the brush and the sapphire plate per unit time. Z is the hardness of the brush material. The higher the hardness, the stronger the brush's resistance to wear. C is the corrosion coefficient of the cleaning fluid on the brush. The formula for calculating the sliding distance is: L=2πrvt, where r is the contact radius of the brush, v is the rotation speed of the brush, and t is the unit time. The contact radius is related to the shape of the brush and its range of action on the surface of the double polishing plate. A larger contact radius means that the brush covers a larger area per unit time, and the sliding distance increases accordingly. At the same time, the faster the rotation speed, the longer the distance the brush travels on the surface of the double polishing plate per unit time.

[0056] In this embodiment, it should be specifically explained that constructing a brush remaining life assessment model and importing brush wear, brush cleaning effect, and brush thickness into the brush remaining life assessment model to assess the remaining life of the brush includes the following specific steps: S41, substituting brush wear and brush thickness into the brush remaining life calculation formula to calculate the remaining life of the brush, wherein the brush remaining life calculation formula is: Where λ is the cleaning evaluation factor, H0 is the real-time thickness of the brush, obtained by an online thickness gauge, and H min The minimum allowable thickness of the brush is given, where the formula for calculating the cleaning evaluation factor is: Where A is the overall scratch assessment index, A r P is the scratch assessment threshold, and P is the particle removal rate. r The particle removal rate threshold is used as the indicator. The scratch comprehensive evaluation index reflects the impact of the brush on the scratches on the surface of the dual polishing pad, while the particle removal rate reflects the cleaning effect of the brush. A lower particle removal rate means that the brush's cleaning ability is reduced, which will also affect its remaining lifespan. By incorporating the scratch comprehensive evaluation index and particle removal rate into the cleaning evaluation factors, the impact of the actual working state of the brush on its remaining lifespan can be comprehensively evaluated.

[0057] S42. Compare the remaining lifespan of the brush with the brush lifespan threshold. If it is less than or equal to the threshold, replace the brush immediately. If it is close to the threshold, issue a warning to remind the operator to pay attention to the brush status and prepare to replace the brush in advance to avoid production interruption due to sudden brush failure.

[0058] It should be noted that the values ​​of various set parameters in this embodiment are obtained as follows: various parameters in the representative sapphire double polishing process are obtained, the surface state parameters of the double polishing wafer before and after cleaning are obtained, the production requirements and quality standards of the double polishing wafer are obtained, and experts are hired to manually judge the qualified state of the double polishing wafer cleaning. At the same time, the historical data is substituted into the calculation results and judgment results of each step in this embodiment and then into the fitting software to output the values ​​of various set parameters that meet the highest judgment accuracy.

[0059] The advantages of this embodiment compared to the prior art are:

[0060] This invention acquires cleaning data, including images of sapphire double-polished wafers and data before and after cleaning. The images are processed to construct a cleaning effect evaluation model. Scratch-related parameters and the number of surface particles before and after cleaning are imported into the cleaning effect evaluation model for assessment. A brush wear evaluation model is also constructed, incorporating process parameters from the brush cleaning process of the sapphire double-polished wafers to assess brush wear. Finally, a brush remaining lifespan evaluation model is constructed, importing brush wear amount, brush cleaning effect, and brush thickness to evaluate the remaining brush lifespan. This invention assesses brush wear in real-time by monitoring the cleaning effect and predicts brush lifespan based on the wear model, thereby reducing the negative impact of the brush on the double-polished wafer cleaning process.

[0061] Example 2

[0062] As shown in Figure 4, the cleaning control system for sapphire double-polished wafers is based on the aforementioned cleaning control method for sapphire double-polished wafers. Specifically, it includes a data acquisition module, a cleaning effect evaluation module, a brush wear evaluation module, and a brush remaining life evaluation module. The data acquisition module acquires images of the sapphire double-polished wafers and data before and after cleaning, and processes the images. The cleaning effect evaluation module evaluates the cleaning effect using scratch-related parameters and the number of surface particles before and after cleaning. The brush wear evaluation module evaluates the brush wear degree using process parameters of the brush cleaning process for the sapphire double-polished wafers. The brush remaining life evaluation module evaluates the remaining brush life using brush wear, brush cleaning effect, and brush thickness.

[0063] Example 3

[0064] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0065] The processor executes the cleaning control method described above for sapphire double polishing wafers by calling the computer program stored in memory.

[0066] This electronic device can vary considerably depending on its configuration or performance. It may include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the cleaning control method for sapphire double-polished wafers provided in the above-described method embodiments. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0067] Example 4

[0068] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0069] When the computer program runs on the computer device, it causes the computer device to perform the cleaning control method described above for sapphire double polishing wafers.

[0070] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0071] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0072] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0073] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

Claims

1. A cleaning control method for sapphire double-polished wafers, characterized in that, It includes the following specific steps: Acquire cleaning data, obtain images of sapphire double polished wafers and data before and after cleaning, and process the images; A cleaning effect evaluation model was constructed, and scratch-related parameters and the number of surface particles before and after cleaning were imported into the cleaning effect evaluation model for evaluation. A brush wear assessment model was constructed, and the process parameters of cleaning sapphire double polishing plates with brushes were imported into the brush wear assessment model to evaluate the degree of brush wear. A brush remaining life assessment model was constructed, and brush wear, brush cleaning effect, and brush thickness were imported into the brush remaining life assessment model to evaluate the remaining life of the brush.

2. The cleaning control method for sapphire double-polished wafers as described in claim 1, characterized in that, The process of acquiring cleaning data, obtaining images of the sapphire double-polished wafer and data before and after cleaning, and processing the images includes the following specific steps: S11. Acquire images of the sapphire double polishing wafer before and after double-sided cleaning using a high-resolution camera. Establish a rectangular coordinate system with the center of the sapphire double polishing wafer as the origin. Use a Gaussian filter to denoise the acquired images of the sapphire double polishing wafer before and after double-sided cleaning. S12. The image threshold is segmented using the maximum inter-class variance method. All possible grayscale thresholds are traversed, and the variance between the foreground and background classes is calculated at each threshold. The threshold corresponding to the largest variance is selected as the optimal segmentation threshold to separate the scratch area from the background. The background of the image is removed by image subtraction to highlight the scratch area. The absolute value of the subtraction of two two-dimensional image data is taken to obtain the difference between the two images, and a difference image data is output. S13. The depth, three-dimensional morphology and number distribution of scratches on the surface of the cleaned double polished plate are measured by a shape measurement laser microscopy system, and the scratch positions are obtained by establishing a rectangular coordinate system. S14. Detect the number of particles on the surface of the sapphire double polished wafer before and after cleaning using a particle detector to obtain the number of particles on the surface before and after cleaning.

3. The cleaning control method for sapphire double-polished wafers as described in claim 2, characterized in that, The construction of the cleaning effect evaluation model, which incorporates scratch-related parameters and the number of surface particles before and after cleaning, includes the following specific steps: S21. Substitute the scratch-related parameters into the scratch comprehensive evaluation index calculation formula to evaluate the degree of scratch. The degree of scratch after cleaning is comprehensively evaluated by the depth, length and number of scratches on both sides of the cleaned sapphire double polishing blade. The negative impact of the bristles during the cleaning process is also evaluated. S22. Substitute the number of particles on the surface of the sapphire double-polished wafer before and after cleaning into the particle removal rate calculation formula to calculate the particle removal rate, evaluate the particle removal effect during the brush cleaning process of the double-polished wafer, and reflect the change in cleanliness of the double-polished wafer after cleaning by the change in the number of particles before and after cleaning.

4. The cleaning control method for sapphire double-polished wafers as described in claim 3, characterized in that, The process of using a brush to clean sapphire double-polished wafers and then importing the parameters of the brush cleaning process into the brush wear assessment model includes the following specific steps: S31. Compare the comprehensive scratch assessment index with the scratch assessment threshold. If the comprehensive scratch assessment index is greater than the scratch assessment threshold, replace the brush immediately. At the same time, compare the particle removal rate with the minimum particle removal rate threshold. If the particle removal rate is less than the minimum particle removal rate threshold, replace the brush immediately. S32. If the scratch comprehensive evaluation index and particle removal rate are both within the threshold range, substitute the process parameters of cleaning the sapphire double polishing sheet with the brush into the brush wear calculation formula to calculate the brush wear. The brush wear calculation formula per unit time is based on the Arcard wear equation. The brush wear is quantified by brush material characteristics, pressure, sliding distance and the corrosiveness of the cleaning fluid to the brush.

5. The cleaning control method for sapphire double-polished wafers as described in claim 4, characterized in that, The construction of the remaining brush life assessment model, which incorporates brush wear, brush cleaning effect, and brush thickness into the model to assess the remaining brush life, includes the following specific steps: S41, substituting brush wear and brush thickness into the remaining brush life calculation formula to calculate the remaining brush life, wherein the remaining brush life calculation formula is: Where λ is the cleaning evaluation factor, H0 is the real-time thickness of the brush, and H min The minimum allowable thickness of the brush is given, where the formula for calculating the cleaning evaluation factor is: Where A is the overall scratch assessment index, A r P is the scratch assessment threshold, and P is the particle removal rate. r This is the threshold for particle removal rate; S42. Compare the remaining lifespan of the brush with the brush lifespan threshold. If it is less than or equal to the threshold, replace the brush immediately. If it is close to the threshold, issue a warning.

6. A cleaning control system for sapphire double-polished wafers, implemented based on the cleaning control method for sapphire double-polished wafers as described in any one of claims 1-5, characterized in that, Specifically, it includes: The data acquisition module is used to acquire images of sapphire double polished wafers and data before and after cleaning, and to process the images. The cleaning effect evaluation module is used to evaluate the cleaning effect by using scratch-related parameters and the number of particles on the surface before and after cleaning; The brush wear assessment module is used to assess the degree of brush wear through process parameters of cleaning sapphire double polishing plates with a brush. The remaining brush life assessment module is used to assess the remaining brush life based on brush wear, brush cleaning effect, and brush thickness.

7. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor is characterized in that it executes the cleaning control method for sapphire double-polished wafers as described in any one of claims 1-5 by calling a computer program stored in the memory.

8. A computer-readable storage medium, characterized in that, The device stores instructions that, when executed on a computer, cause the computer to perform the cleaning control method for sapphire double-polished wafers as described in any one of claims 1-5.

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