Sapphire substrate cleaning data processing system and method based on image recognition

Abstract

The invention discloses a sapphire substrate cleaning data processing system and a sapphire substrate cleaning data processing method based on image recognition, and belongs to the field of semiconductor processing. And importing the obtained defect cleaning anomaly analysis result and the dirty cleaning anomaly analysis result into a cleaning water pressure analysis model to analyze the cleaning water pressure, and adjusting the cleaning water pressure data in the cleaning process according to the analyzed cleaning water pressure. According to the method, dirt and defects on the surface of the cleaned sapphire are accurately distinguished through an image processing means, the cleaning water pressure is accurately adjusted according to defect change data and dirt cleaning change data in the cleaning process, and damage to the sapphire substrate in the cleaning process is reduced while the cleaning efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the field of semiconductor processing, and in particular relates to a sapphire substrate cleaning data processing system and method based on image recognition. Background Art

[0002] Sapphire substrate is an important material used in semiconductor manufacturing. It is mainly made of synthetic sapphire crystals. In the field of LED (light emitting diode) and optoelectronics, sapphire substrate is widely used due to its unique physical and chemical properties. Sapphire substrate is a base for epitaxial growth of semiconductor materials, usually used as a substrate for LED chips. It is made of high-purity aluminum oxide crystals through a specific processing process and has extremely high hardness and thermal stability. The manufacture of sapphire substrate involves melting high-purity aluminum oxide and growing crystals through specific methods (such as pulling or guided mold method). These crystals are then cut, polished and cleaned to obtain the required size and surface quality. It plays a key role in LED manufacturing and other optoelectronics applications with its unique physical and chemical properties. After the sapphire substrate is produced, it is necessary to use a cleaning device to clean the dirt on the sapphire substrate. However, during the cleaning process, it is impossible to accurately distinguish the dirt and defects on the sapphire surface, and it is impossible to accurately adjust the cleaning water pressure according to the defect change data and the dirt cleaning change data during the cleaning process, which reduces the cleaning efficiency and causes damage to the sapphire substrate during the cleaning process. Most of the existing technologies have the above problems. For example, a Chinese patent with application publication number CN105931949A discloses a single-chip cleaning method for reworked wafers on a patterned sapphire substrate, which solves the problem that the photoresist remaining on the surface of the patterned sapphire wafer is chemically stable, has strong surface adsorption capacity, and is not easily dissolved by common acids and alkalis, which makes it difficult to remove the photoresist using existing cleaning methods that wash multiple wafers at the same time. The specific steps of this method are as follows: (1) Select a reworked wafer on a patterned sapphire substrate and use a high-temperature degumming agent for rotational cleaning; (2) Use isopropyl alcohol to rotate and clean the wafer; (3) Use deionized water to rinse the wafer; (4) Brush the wafer; (5) Rotate and dry. The steps (1) to (5) are all performed in a clean room standard with a dust particle content of 100 levels. The invention avoids the use of corrosive chemicals such as strong acids and alkalis, reduces damage to front-line employees and equipment pipelines, and multiple single-chip cycle cleanings ensure the cleaning quality of each sapphire substrate wafer; The above patents all have the problems raised by this background technology. In order to solve the problems raised by this background technology, this application designs a sapphire substrate cleaning data processing system and method based on image recognition. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention proposes a sapphire substrate cleaning data processing system and method based on image recognition. The present invention obtains image data of the sapphire substrate during the cleaning process, and simultaneously obtains cleaning water pressure data during the cleaning process, distinguishes between defects and contamination on the sapphire surface, obtains defect change data of the sapphire substrate during the cleaning process, and imports them into a defect cleaning anomaly analysis strategy to perform defect cleaning anomaly analysis, obtains contamination change data on the sapphire substrate during the cleaning process, and imports them into a contamination cleaning anomaly analysis strategy to perform contamination cleaning anomaly analysis, imports the obtained defect cleaning anomaly analysis results and contamination cleaning anomaly analysis results into a cleaning water pressure analysis model to perform cleaning water pressure analysis, adjusts the cleaning water pressure data during the cleaning process according to the cleaning water pressure obtained by the analysis, accurately distinguishes contamination and defects on the cleaned sapphire surface through image processing means, and accurately adjusts the cleaning water pressure according to the defect change data and the contamination cleaning change data during the cleaning process, thereby improving the cleaning efficiency and reducing the damage of the sapphire substrate during the cleaning process.

[0004] To achieve the above object, the present invention provides the following technical solution: a sapphire substrate cleaning data processing method based on image recognition, which comprises the following specific steps: Acquire image data of the sapphire substrate during the cleaning process, and simultaneously acquire cleaning water pressure data during the cleaning process to distinguish defects and contamination on the sapphire surface; Obtain the sapphire substrate defect change data during the cleaning process and import it into the defect cleaning anomaly analysis strategy to perform defect cleaning anomaly analysis; Obtain the contamination change data of the sapphire substrate surface during the cleaning process and import it into the contamination cleaning abnormality analysis strategy to perform contamination cleaning abnormality analysis; Importing the acquired defect cleaning abnormality analysis results and contamination cleaning abnormality analysis results into the cleaning water pressure analysis model to analyze the cleaning water pressure; The cleaning water pressure data during the cleaning process is adjusted according to the cleaning water pressure obtained by analysis.

[0005] It should be noted here that as a preferred technical solution of the sapphire substrate cleaning data processing method based on image recognition, the specific steps of obtaining the image data of the sapphire substrate during the cleaning process and simultaneously obtaining the cleaning water pressure data during the cleaning process to distinguish the sapphire surface defects and contamination are as follows: S11, obtaining the set water pressure data during the cleaning process, obtaining the image data of the sapphire substrate during the cleaning process in real time, importing the image processing software to obtain the pixel value data of each pixel in the image, and obtaining the average pixel value data of the corresponding area of ​​the sapphire substrate at the same time, obtaining the pixel points whose absolute value of the difference between the pixel value of the pixel point in the image and the average pixel value of the corresponding area is greater than or equal to the difference threshold, setting them as abnormal pixel points, setting the pixel value gradient, obtaining the contour formed by the abnormal pixel points in the same pixel value gradient, setting it as the judgment contour, and obtaining the contour data of several judgment contours and the pixel value data of the pixel points; S12, obtaining contour data and pixel value data of the judgment contour, and simultaneously obtaining contours and average pixel value data that have been historically identified as defects and contamination, and substituting them into the judgment value calculation formula to calculate the judgment value of the judgment contour, wherein the judgment value calculation formula is: , where a is the contour similarity ratio coefficient, c() is the area of ​​the contour in the brackets, Si is the i-th contour in the set of historical defect and dirty contours, S is the contour of the judgment contour, xi is the average pixel value of the contour in the set of historical defect and dirty contours, and x is the average pixel value data of the judgment contour; S13, obtaining the types of historical defects and contamination corresponding to the maximum judgment value as the types of judgment contours, obtaining type data of all judgment contours, and classifying the judgment contours into defects and contamination.

[0006] It should be noted here that as a preferred technical solution of the sapphire substrate cleaning data processing method based on image recognition, the acquisition of sapphire substrate defect change data during the cleaning process and importing it into the defect cleaning anomaly analysis strategy for defect cleaning anomaly analysis includes the following specific steps: S21, obtaining an image of defect changes during the cleaning process and an image of the defect at the start time, and obtaining defect area and pixel value change data of the pixel points in the image; S22, obtaining an image of the defect at the starting time and importing it into a defect abnormal value calculation formula to calculate the starting defect abnormal value, wherein the defect abnormal value calculation formula is: , where n is the number of defects, mz is the number of pixels of the z-th defect, sz is the area of ​​the z-th defect, S is the area of ​​the sapphire substrate, c is the distance standard value, czj is the distance from the j-th pixel of the z-th defect to the center of the defect, where the center of the defect is the point with the smallest distance from the other pixels of the corresponding defect, kzj is the pixel value of the j-th pixel of the z-th defect, and k is the average pixel value data of the corresponding area of ​​the sapphire substrate; S23, obtaining real-time defect images during the cleaning process and importing them into a defect abnormality value calculation formula to calculate a cleaning defect abnormality value; S24, substituting the obtained initial defect abnormal value and cleaning defect abnormal value into the defect cleaning abnormal analysis value calculation formula to calculate the defect cleaning abnormal analysis value, wherein the defect cleaning abnormal analysis value calculation formula is: , where xm is the cleaning defect abnormal value, T is the time standard value, and tx is the cleaning time.

[0007] It should be noted here that, as a preferred technical solution of the sapphire substrate cleaning data processing method based on image recognition, the acquisition of the sapphire substrate surface contamination change data during the cleaning process and importing it into the contamination cleaning abnormality analysis strategy for contamination cleaning abnormality analysis includes the following specific steps: S31, obtaining an image of the change of dirt during the cleaning process and an image of the dirt at the start time, and obtaining data on the change of the dirt area and pixel values ​​of the pixels in the image; S32, substituting the obtained pollution image at the starting time into the pollution analysis value calculation formula to calculate the pollution analysis value at the starting time, wherein the pollution analysis value calculation formula is: , where P is the number of dirty contours at the starting time, mp is the number of pixels of the p-th dirty contour, and Qpt is the pixel value of the t-th pixel of the p-th dirty contour; S33, the obtained real-time pollution image is imported into the pollution analysis value calculation formula to calculate the real-time pollution analysis value; S34, substituting the calculated real-time pollution analysis value and the starting time pollution analysis value into the pollution cleaning value calculation formula to calculate the pollution cleaning value, wherein the pollution cleaning value calculation formula is: , where w is the pollution analysis safety value, is the cleaning difference ratio, and wx is the real-time dirt analysis value.

[0008] It should be noted here that, as a preferred technical solution of the sapphire substrate cleaning data processing method based on image recognition, the method of importing the obtained defect cleaning abnormality analysis results and the contamination cleaning abnormality analysis results into the cleaning water pressure analysis model to analyze the cleaning water pressure includes the following specific contents: S41, determine whether the turbidity analysis value is less than the turbidity analysis safety value. If the turbidity analysis value is less than or equal to the turbidity analysis safety value, it means that the cleaning is complete; if the turbidity analysis value is greater than the turbidity analysis safety value, it means that cleaning needs to be continued, and S42 is performed; S42, obtain the calculated defect cleaning abnormality analysis value, the turbidity cleaning value and the set water pressure data, and substitute them into the cleaning water pressure calculation formula to calculate the cleaning water pressure, wherein the cleaning water pressure calculation formula is: , where fm is the set water pressure data, ln() is the logarithm with the natural constant e as the base, and C is the compensation coefficient.

[0009] A sapphire substrate cleaning data processing system based on image recognition, which is implemented based on the above-mentioned sapphire substrate cleaning data processing method based on image recognition, and specifically comprises an image acquisition module, an image differentiation module, a defect cleaning abnormality analysis module, a contamination cleaning abnormality analysis module, a cleaning water pressure analysis module and a water pressure adjustment module, wherein the image acquisition module is used to acquire the image data of the sapphire substrate during the cleaning process, and simultaneously acquire the cleaning water pressure data during the cleaning process; The image distinguishing module is used to distinguish sapphire surface defects and contamination by image processing means; The defect cleaning anomaly analysis module is used to obtain the sapphire substrate defect change data during the cleaning process and import it into the defect cleaning anomaly analysis strategy to perform defect cleaning anomaly analysis; The dirt cleaning abnormality analysis module is used to obtain the dirt change data of the sapphire substrate surface during the cleaning process and import it into the dirt cleaning abnormality analysis strategy to perform dirt cleaning abnormality analysis; The cleaning water pressure analysis module is used to import the acquired defect cleaning abnormality analysis results and the contamination cleaning abnormality analysis results into the cleaning water pressure analysis model to analyze the cleaning water pressure; The water pressure regulating module is used to regulate the cleaning water pressure data during the cleaning process according to the cleaning water pressure obtained by analysis.

[0010] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned sapphire substrate cleaning data processing method based on image recognition by calling the computer program stored in the memory.

[0011] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned sapphire substrate cleaning data processing method based on image recognition.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains image data of a sapphire substrate during a cleaning process, and simultaneously obtains cleaning water pressure data during the cleaning process, distinguishes defects and contamination on the sapphire surface, obtains defect change data of the sapphire substrate during the cleaning process, and imports the data into a defect cleaning abnormality analysis strategy to perform defect cleaning abnormality analysis, obtains contamination change data on the sapphire substrate surface during the cleaning process, and imports the data into a contamination cleaning abnormality analysis strategy to perform contamination cleaning abnormality analysis, imports the obtained defect cleaning abnormality analysis results and contamination cleaning abnormality analysis results into a cleaning water pressure analysis model to perform cleaning water pressure analysis, adjusts the cleaning water pressure data during the cleaning process according to the cleaning water pressure obtained by the analysis, accurately distinguishes contamination and defects on the cleaned sapphire surface by image processing means, and accurately adjusts the cleaning water pressure according to the defect change data and contamination cleaning change data during the cleaning process, thereby improving the cleaning efficiency and reducing damage to the sapphire substrate during the cleaning process. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the overall process of the sapphire substrate cleaning data processing method based on image recognition of the present invention; Figure 2 This is a schematic diagram of step S2 of the sapphire substrate cleaning data processing method based on image recognition of the present invention; Figure 3 A schematic diagram of the steps for distinguishing defects and contamination in the sapphire substrate cleaning data processing method based on image recognition of the present invention; Figure 4 It is a schematic diagram of the sapphire substrate cleaning data processing system based on image recognition of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use.

[0015] Example 1 To solve the technical problem raised in the background technology: the prior art cannot accurately distinguish the dirt and defects on the sapphire surface during the cleaning process, and cannot accurately adjust the cleaning water pressure according to the defect change data and the dirt cleaning change data during the cleaning process, which reduces the cleaning efficiency and causes damage to the sapphire substrate during the cleaning process; the present invention provides a preferred embodiment: Figure 1-Figure 2 As shown, a sapphire substrate cleaning data processing method based on image recognition includes the following specific steps: S1. Acquire image data of the sapphire substrate during the cleaning process, and simultaneously acquire cleaning water pressure data during the cleaning process, so as to distinguish defects and dirt on the sapphire surface; In this embodiment, the image data of the sapphire substrate during the cleaning process is obtained, and the cleaning water pressure data during the cleaning process is obtained at the same time. The specific steps for distinguishing the surface defects and contamination of the sapphire are as follows: S11, obtaining the set water pressure data during the cleaning process, obtaining the image data of the sapphire substrate during the cleaning process in real time, importing the image processing software to obtain the pixel value data of each pixel in the image, and obtaining the average pixel value data of the corresponding area of ​​the sapphire substrate at the same time, obtaining the pixel points whose absolute value of the difference between the pixel value of the pixel point in the image and the average pixel value of the corresponding area is greater than or equal to the difference threshold, setting them as abnormal pixel points, setting the pixel value gradient, obtaining the contour formed by the abnormal pixel points in the same pixel value gradient, setting it as the judgment contour, and obtaining the contour data of several judgment contours and the pixel value data of the pixel points; S12, obtaining contour data and pixel value data of the judgment contour, and simultaneously obtaining contours and average pixel value data that have been historically identified as defects and contamination, and substituting them into the judgment value calculation formula to calculate the judgment value of the judgment contour, wherein the judgment value calculation formula is: , where a is the contour similarity ratio coefficient, c() is the area of ​​the contour in the brackets, Si is the i-th contour in the set of historical defect and dirty contours, S is the contour of the judgment contour, xi is the average pixel value of the contour in the set of historical defect and dirty contours, and x is the average pixel value data of the judgment contour; S13, obtaining the types of historical defects and contamination corresponding to the maximum judgment value as the types of judgment contours, obtaining type data of all judgment contours, and classifying the judgment contours into defects and contamination; In this step, image processing and image segmentation methods are used to accurately identify and classify defects and contamination; S2, obtaining the sapphire substrate defect change data during the cleaning process and importing it into the defect cleaning abnormality analysis strategy to perform defect cleaning abnormality analysis; In this embodiment, obtaining the sapphire substrate defect change data during the cleaning process and importing it into the defect cleaning abnormality analysis strategy for defect cleaning abnormality analysis includes the following specific steps: S21, obtaining an image of defect changes during the cleaning process and an image of the defect at the start time, and obtaining defect area and pixel value change data of the pixel points in the image; S22, obtaining an image of the defect at the starting time and importing it into a defect abnormal value calculation formula to calculate the starting defect abnormal value, wherein the defect abnormal value calculation formula is: , where n is the number of defects, mz is the number of pixels of the z-th defect, sz is the area of ​​the z-th defect, S is the area of ​​the sapphire substrate, c is the distance standard value, czj is the distance from the j-th pixel of the z-th defect to the center of the defect, where the center of the defect is the point with the smallest distance from the other pixels of the corresponding defect, kzj is the pixel value of the j-th pixel of the z-th defect, and k is the average pixel value data of the corresponding area of ​​the sapphire substrate; S23, obtaining real-time defect images during the cleaning process and importing them into a defect abnormal value calculation formula to calculate a cleaning defect abnormal value; S24, substituting the obtained initial defect abnormal value and cleaning defect abnormal value into the defect cleaning abnormal analysis value calculation formula to calculate the defect cleaning abnormal analysis value, wherein the defect cleaning abnormal analysis value calculation formula is: , where xm is the cleaning defect abnormal value, T is the time standard value, and tx is the cleaning time; In this step, the defect variation data during the cleaning process is analyzed to accurately evaluate the damage of the sapphire substrate during the cleaning process; S3, obtaining the contamination change data of the sapphire substrate surface during the cleaning process and importing it into the contamination cleaning abnormality analysis strategy to perform contamination cleaning abnormality analysis; In this embodiment, obtaining the contamination change data of the sapphire substrate surface during the cleaning process and importing it into the contamination cleaning abnormality analysis strategy to perform contamination cleaning abnormality analysis includes the following specific steps: S31, obtaining an image of the change of dirt during the cleaning process and an image of the dirt at the start time, and obtaining data on the change of the dirt area and pixel values ​​of the pixels in the image; S32, substituting the obtained pollution image at the starting time into the pollution analysis value calculation formula to calculate the pollution analysis value at the starting time, wherein the pollution analysis value calculation formula is: , where P is the number of dirty contours at the starting time, mp is the number of pixels of the p-th dirty contour, and Qpt is the pixel value of the t-th pixel of the p-th dirty contour; S33, the obtained real-time pollution image is imported into the pollution analysis value calculation formula to calculate the real-time pollution analysis value; S34, substituting the calculated real-time pollution analysis value and the starting time pollution analysis value into the pollution cleaning value calculation formula to calculate the pollution cleaning value, wherein the pollution cleaning value calculation formula is: , where w is the pollution analysis safety value, is the cleaning difference ratio coefficient, wx is the real-time contamination analysis value; the contamination analysis safety value here is set by manual experience and is set according to the required substrate surface contamination level standard; S4, importing the obtained defect cleaning abnormality analysis results and the contamination cleaning abnormality analysis results into the cleaning water pressure analysis model to analyze the cleaning water pressure; In this embodiment, the obtained defect cleaning abnormality analysis results and dirty cleaning abnormality analysis results are imported into the cleaning water pressure analysis model to analyze the cleaning water pressure, including the following specific contents: S41, determine whether the turbidity analysis value is less than the turbidity analysis safety value. If the turbidity analysis value is less than or equal to the turbidity analysis safety value, it means that the cleaning is complete; if the turbidity analysis value is greater than the turbidity analysis safety value, it means that cleaning needs to be continued, and S42 is performed; S42, obtain the calculated defect cleaning abnormality analysis value, the turbidity cleaning value and the set water pressure data, and substitute them into the cleaning water pressure calculation formula to calculate the cleaning water pressure, wherein the cleaning water pressure calculation formula is: , where fm is the set water pressure data, ln() is the logarithm with the natural constant e as the base, and C is the compensation coefficient; S5. Adjust the cleaning water pressure data during the cleaning process according to the cleaning water pressure obtained by analysis.

[0016] It should be noted here that the compensation coefficient and the cleaning difference ratio coefficient here are obtained according to the experience of technical personnel in this field, and the preferred value selection method of the compensation coefficient and the cleaning difference ratio coefficient here is: obtain several groups of image data of the sapphire substrate in the cleaning process and the cleaning water pressure data in the cleaning process, substitute the image data of the sapphire substrate in the cleaning process and the cleaning water pressure data in the cleaning process into the cleaning water pressure calculation formula to calculate the cleaning water pressure, hire experts to score the cleaned sapphire substrate, substitute the scoring results and the cleaning water pressure calculation results into the fitting software to output the compensation coefficient and the cleaning difference ratio coefficient values ​​that meet the highest scoring sorting accuracy; The benefits of the present embodiment need to be specifically explained here. The benefits of the present embodiment over the prior art are as follows: acquiring image data of the sapphire substrate during the cleaning process, acquiring cleaning water pressure data during the cleaning process, distinguishing between defects and contamination on the sapphire surface, acquiring defect change data of the sapphire substrate during the cleaning process, and importing the data into the defect cleaning anomaly analysis strategy to perform defect cleaning anomaly analysis, acquiring contamination change data on the sapphire substrate surface during the cleaning process, and importing the data into the contamination cleaning anomaly analysis strategy to perform contamination cleaning anomaly analysis, importing the acquired defect cleaning anomaly analysis results and contamination cleaning anomaly analysis results into the cleaning water pressure analysis model to perform cleaning water pressure analysis, adjusting the cleaning water pressure data during the cleaning process according to the cleaning water pressure obtained by the analysis, accurately distinguishing contamination and defects on the cleaned sapphire surface through image processing means, and accurately adjusting the cleaning water pressure according to the defect change data and contamination cleaning change data during the cleaning process, thereby improving the cleaning efficiency and reducing the damage to the sapphire substrate during the cleaning process.

[0017] Example 2 like Figure 4 As shown, a sapphire substrate cleaning data processing system based on image recognition is implemented based on the above-mentioned sapphire substrate cleaning data processing method based on image recognition, and specifically includes an image acquisition module, an image distinction module, a defect cleaning abnormality analysis module, a contamination cleaning abnormality analysis module, a cleaning water pressure analysis module and a water pressure adjustment module, wherein the image acquisition module is used to acquire the image data of the sapphire substrate during the cleaning process, and simultaneously acquire the cleaning water pressure data during the cleaning process; Image differentiation module, used to differentiate sapphire surface defects and contamination through image processing; The defect cleaning anomaly analysis module is used to obtain the sapphire substrate defect change data during the cleaning process and import it into the defect cleaning anomaly analysis strategy to perform defect cleaning anomaly analysis; A dirt cleaning anomaly analysis module is used to obtain the dirt change data on the surface of the sapphire substrate during the cleaning process and import it into the dirt cleaning anomaly analysis strategy to perform dirt cleaning anomaly analysis; A cleaning water pressure analysis module is used to import the acquired defect cleaning abnormality analysis results and the contamination cleaning abnormality analysis results into the cleaning water pressure analysis model to analyze the cleaning water pressure; A water pressure regulating module, used to adjust the cleaning water pressure data during the cleaning process according to the cleaning water pressure obtained by analysis; The connecting lines in the figure represent the signal transmission relationship between each module, and each module is controlled and operated by the server.

[0018] Example 3 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; The processor executes the above-mentioned sapphire substrate cleaning data processing method based on image recognition by calling the computer program stored in the memory.

[0019] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the sapphire substrate cleaning data processing method based on image recognition provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0020] Example 4 This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program runs on a computer device, the computer device is enabled to execute the above-mentioned sapphire substrate cleaning data processing method based on image recognition.

[0021] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0022] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. 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 computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

Claims

1. A sapphire substrate cleaning data processing method based on image recognition, characterized in that: It includes the following specific steps: Acquire image data of the sapphire substrate during the cleaning process, and simultaneously acquire cleaning water pressure data during the cleaning process to distinguish defects and contamination on the sapphire surface; Obtain the sapphire substrate defect change data during the cleaning process and import it into the defect cleaning anomaly analysis strategy to perform defect cleaning anomaly analysis; Obtain the contamination change data of the sapphire substrate surface during the cleaning process and import it into the contamination cleaning abnormality analysis strategy to perform contamination cleaning abnormality analysis; Importing the obtained defect cleaning abnormality analysis results and contamination cleaning abnormality analysis results into the cleaning water pressure analysis model to analyze the cleaning water pressure; The cleaning water pressure data during the cleaning process is adjusted according to the cleaning water pressure obtained by analysis.

2. The sapphire substrate cleaning data processing method based on image recognition according to claim 1, characterized in that: The specific steps of obtaining the image data of the sapphire substrate during the cleaning process and obtaining the cleaning water pressure data during the cleaning process to distinguish the surface defects and contamination of the sapphire are: S11, obtaining the set water pressure data during the cleaning process, obtaining the image data of the sapphire substrate during the cleaning process in real time, importing the image processing software to obtain the pixel value data of each pixel in the image, and obtaining the average pixel value data of the corresponding area of ​​the sapphire substrate at the same time, obtaining the pixel points whose absolute value of the difference between the pixel value of the pixel point in the image and the average pixel value of the corresponding area is greater than or equal to the difference threshold, setting them as abnormal pixel points, setting the pixel value gradient, obtaining the contour formed by the abnormal pixel points in the same pixel value gradient, setting it as the judgment contour, and obtaining the contour data of several judgment contours and the pixel value data of the pixel points; S12, obtaining contour data and pixel value data of the judgment contour, and simultaneously obtaining contours and average pixel value data that have been historically identified as defects and contamination, and substituting them into the judgment value calculation formula to calculate the judgment value of the judgment contour, wherein the judgment value calculation formula is: , where a is the contour similarity ratio coefficient, c() is the area of ​​the contour in the brackets, Si is the i-th contour in the set of historical defect and dirty contours, S is the contour of the judgment contour, xi is the average pixel value of the contour in the set of historical defect and dirty contours, and x is the average pixel value data of the judgment contour; S13, obtaining the types of historical defects and contamination corresponding to the maximum judgment value as the types of judgment contours, obtaining type data of all judgment contours, and classifying the judgment contours into defects and contamination.

3. The sapphire substrate cleaning data processing method based on image recognition according to claim 2, characterized in that: The method of obtaining the sapphire substrate defect change data during the cleaning process and importing it into the defect cleaning abnormality analysis strategy for defect cleaning abnormality analysis includes the following specific steps: S21, obtaining an image of defect changes during the cleaning process and an image of the defect at the start time, and obtaining defect area and pixel value change data of the pixel points in the image; S22, obtaining an image of the defect at the starting time and importing it into a defect abnormal value calculation formula to calculate the starting defect abnormal value, wherein the defect abnormal value calculation formula is: , where n is the number of defects, mz is the number of pixels of the z-th defect, sz is the area of ​​the z-th defect, S is the area of ​​the sapphire substrate, c is the distance standard value, czj is the distance from the j-th pixel of the z-th defect to the center of the defect, where the center of the defect is the point with the smallest distance from the other pixels of the corresponding defect, kzj is the pixel value of the j-th pixel of the z-th defect, and k is the average pixel value data of the corresponding area of ​​the sapphire substrate; S23, obtaining real-time defect images during the cleaning process and importing them into a defect abnormal value calculation formula to calculate a cleaning defect abnormal value; S24, substituting the obtained initial defect abnormal value and cleaning defect abnormal value into the defect cleaning abnormal analysis value calculation formula to calculate the defect cleaning abnormal analysis value, wherein the defect cleaning abnormal analysis value calculation formula is: , where xm is the cleaning defect abnormal value, T is the time standard value, and tx is the cleaning time.

4. The sapphire substrate cleaning data processing method based on image recognition according to claim 3, characterized in that: The method of obtaining the sapphire substrate surface contamination change data during the cleaning process and importing it into the contamination cleaning abnormality analysis strategy to perform contamination cleaning abnormality analysis includes the following specific steps: S31, obtaining an image of the change of dirt during the cleaning process and an image of the dirt at the start time, and obtaining data on the change of the dirt area and pixel values ​​of the pixels in the image; S32, substituting the obtained pollution image at the starting time into the pollution analysis value calculation formula to calculate the pollution analysis value at the starting time, wherein the pollution analysis value calculation formula is: , where P is the number of dirty contours at the starting moment, mp is the number of pixels of the pth dirty contour, and Qpt is the pixel value of the tth pixel of the pth dirty contour.

5. The sapphire substrate cleaning data processing method based on image recognition according to claim 4, characterized in that: The method of obtaining the sapphire substrate surface contamination change data during the cleaning process and importing it into the contamination cleaning abnormality analysis strategy for performing contamination cleaning abnormality analysis also includes the following specific steps: S33, the obtained real-time pollution image is imported into the pollution analysis value calculation formula to calculate the real-time pollution analysis value; S34, substituting the calculated real-time pollution analysis value and the starting time pollution analysis value into the pollution cleaning value calculation formula to calculate the pollution cleaning value, wherein the pollution cleaning value calculation formula is: , where w is the pollution analysis safety value, is the cleaning difference ratio, and wx is the real-time dirt analysis value.

6. The sapphire substrate cleaning data processing method based on image recognition according to claim 5, characterized in that: The method of importing the obtained defect cleaning abnormality analysis results and the dirty cleaning abnormality analysis results into the cleaning water pressure analysis model to analyze the cleaning water pressure includes the following specific contents: S41, determine whether the turbidity analysis value is less than the turbidity analysis safety value. If the turbidity analysis value is less than or equal to the turbidity analysis safety value, it means that the cleaning is complete; if the turbidity analysis value is greater than the turbidity analysis safety value, it means that cleaning needs to be continued, and S42 is performed; S42, obtain the calculated defect cleaning abnormality analysis value, the turbidity cleaning value and the set water pressure data, and substitute them into the cleaning water pressure calculation formula to calculate the cleaning water pressure, wherein the cleaning water pressure calculation formula is: , where fm is the set water pressure data, ln() is the logarithm with the natural constant e as the base, and C is the compensation coefficient.

7. A sapphire substrate cleaning data processing system based on image recognition, which is implemented based on the sapphire substrate cleaning data processing method based on image recognition as claimed in any one of claims 1 to 6, characterized in that: It specifically includes an image acquisition module, an image differentiation module, a defect cleaning abnormality analysis module, a contamination cleaning abnormality analysis module, a cleaning water pressure analysis module and a water pressure adjustment module, wherein the image acquisition module is used to acquire image data of the sapphire substrate during the cleaning process and simultaneously acquire cleaning water pressure data during the cleaning process; The image distinguishing module is used to distinguish sapphire surface defects and contamination by image processing means; The defect cleaning anomaly analysis module is used to obtain the sapphire substrate defect change data during the cleaning process and import it into the defect cleaning anomaly analysis strategy to perform defect cleaning anomaly analysis; The dirt cleaning abnormality analysis module is used to obtain the dirt change data of the sapphire substrate surface during the cleaning process and import it into the dirt cleaning abnormality analysis strategy to perform dirt cleaning abnormality analysis; The cleaning water pressure analysis module is used to import the acquired defect cleaning abnormality analysis results and the contamination cleaning abnormality analysis results into the cleaning water pressure analysis model to analyze the cleaning water pressure; The water pressure regulating module is used to regulate the cleaning water pressure data during the cleaning process according to the cleaning water pressure obtained by analysis.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the sapphire substrate cleaning data processing method based on image recognition as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the sapphire substrate cleaning data processing method based on image recognition as described in any one of claims 1 to 6.

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