Method, system and device for detecting the quality of a coating

Abstract

The present disclosure provides a glue uniformity detection method, system and device, and belongs to the field of quality detection. The detection method comprises: acquiring a gray scale image, the gray scale image having a product to be detected; dividing the gray scale image into multiple regions and determining the gray scale value of each region; determining the area of the product region and the area of the abnormal region, the product region being the region occupied by the product in the gray scale image, and the abnormal region being the region in the product region whose difference between the gray scale value and the average gray scale value of the product region exceeds a preset threshold; calculating the ratio between the area of the abnormal region and the area of the product region, if the ratio is greater than a preset ratio, determining that the product is a defective product, and if the ratio is not greater than the preset ratio, determining that the product is a good product. The present disclosure can timely detect products with poor glue uniformity, effectively reducing the cost of rework.

Description

Technical Field

[0001] This disclosure pertains to the field of quality inspection, and specifically relates to a method, system, and equipment for detecting the quality of homogenized adhesive. Background Technology

[0002] Photolithography is an important process in the manufacturing of semiconductor chips, in which photoresist needs to be evenly coated on the surface of the product.

[0003] In related technologies, photoresist is applied using a spin coater. The spin coater pumps the photoresist into a tube and sprays it onto the surface of the product while it rotates horizontally at a uniform speed, thus achieving the purpose of photoresist application.

[0004] However, issues such as unstable air volume in the adhesive pump, excessively high viscosity of the photoresist, insufficient adhesive volume, and excessively high or low distance between the adhesive tube and the product surface can all lead to substandard homogenization quality. Failure to promptly identify poorly homogenized products, resulting in their submission to subsequent processes, will increase rework costs. Summary of the Invention

[0005] This disclosure provides a method, system, and equipment for detecting the quality of homogenized coating, which can promptly detect products with poor homogenized coating quality and effectively reduce rework costs. The technical solution is as follows:

[0006] On one hand, embodiments of this disclosure provide a method for detecting the quality of spin coating, including:

[0007] Obtain a grayscale image, wherein the grayscale image contains the product to be detected;

[0008] The grayscale image is divided into multiple regions, and the grayscale value of each region is determined;

[0009] Determine the area of ​​the product region and the area of ​​the abnormal region. The product region is the area occupied by the product in the grayscale image. The abnormal region is the area in the product region where the difference between the grayscale value and the average grayscale value of the product region exceeds a preset threshold.

[0010] Calculate the ratio between the area of ​​the abnormal region and the area of ​​the product region. If the ratio is greater than a preset ratio, the product is determined to be a defective product. If the ratio is not greater than the preset ratio, the product is determined to be a good product.

[0011] In yet another implementation of this disclosure, before obtaining the grayscale image, the following steps are included:

[0012] A first light source is provided to illuminate the surface of the article.

[0013] The brightness of the first light source is adjusted to adjust the average grayscale value of the surface of the product, so that the average grayscale value of the surface of the product is 100 to 200.

[0014] In another implementation of this disclosure, the grayscale image is divided into multiple regions, including:

[0015] Based on the resolution of the grayscale image, the grayscale image is divided into multiple regions, with each pixel as the smallest region.

[0016] In another implementation of this disclosure, determining the area of ​​the article region includes:

[0017] A second light source is provided to illuminate the back of the article;

[0018] The brightness of the second light source is adjusted to adjust the grayscale value of the product outline and the grayscale value of the background, so that the difference between the grayscale value of the product outline and the grayscale value of the background is greater than 100. The background is the area in the grayscale image excluding the product.

[0019] The area occupied by the region within the outline of the product is defined as the area of ​​the product region.

[0020] On the other hand, embodiments of this disclosure provide a system for detecting the quality of homogenization, including: an imaging module for acquiring a grayscale image, wherein the grayscale image contains the product to be detected;

[0021] The region division module is used to divide the grayscale image into multiple regions and determine the grayscale value of each region;

[0022] The area determination module is used to determine the area of ​​the product area and the area of ​​the abnormal area. The product area is the area occupied by the product in the grayscale image, and the abnormal area is the area in the product area where the difference between the grayscale value and the average grayscale value of the product area exceeds a preset threshold.

[0023] The calculation module is used to calculate the ratio between the area of ​​the abnormal region and the area of ​​the product region. If the ratio is greater than a preset ratio, the product is determined to be a defective product. If the ratio is not greater than the preset ratio, the product is determined to be a good product.

[0024] In another implementation of this disclosure, the detection system further includes:

[0025] The first lighting module is used to provide a first light source to illuminate the surface of the product.

[0026] The first brightness adjustment module is used to adjust the brightness of the first light source in order to adjust the average grayscale value of the product surface, so that the average grayscale value of the product surface is 100 to 200.

[0027] In another implementation of this disclosure, the region division module is further configured to divide the grayscale image into multiple regions, with each pixel as the smallest region, based on the resolution of the grayscale image.

[0028] In yet another implementation of this disclosure, the area determination module includes:

[0029] The second lighting module is used to provide a second light source to illuminate the back of the product.

[0030] The second brightness adjustment module is used to adjust the brightness of the second light source to adjust the grayscale value of the product outline and the grayscale value of the background, so that the difference between the grayscale value of the product outline and the grayscale value of the background is greater than 100, wherein the background is the area in the grayscale image excluding the product.

[0031] The area determination submodule is used to determine the area occupied by the region within the outline of the product as the area of ​​the product region.

[0032] In another aspect, embodiments of this disclosure provide a computer device including a processor and a memory configured to store executable instructions of the processor; the processor is configured to perform the aforementioned method for detecting the quality of homogenized adhesive.

[0033] In another aspect, embodiments of this disclosure provide a computer storage medium storing computer instructions, characterized in that the computer instructions, when executed by a processor, implement the aforementioned method for detecting the quality of homogenized glue.

[0034] The beneficial effects of the technical solutions provided in this disclosure are:

[0035] The detection method provided in this disclosure enables timely and accurate detection of the uniform coating quality of photoresist applied to a product. First, a grayscale image is acquired, containing the product to be inspected. Next, the grayscale image is divided into multiple regions, and the grayscale value of each region is determined. This facilitates the identification of the product region and any abnormal regions within the product region within the grayscale image. Then, the areas of the product region and the abnormal regions are determined. The product region is the area occupied by the product within the grayscale image, and the abnormal region is the area within the product region where the difference between its grayscale value and the average grayscale value of the product region exceeds a preset threshold. Finally, the ratio between the area of ​​the abnormal region and the area of ​​the product region is calculated. If the ratio is greater than a preset ratio, the product is determined to be defective; if the ratio is not greater than the preset ratio, the product is determined to be good.

[0036] In other words, by calculating the ratio between the area of ​​the abnormal region and the area of ​​the product region, if the ratio is too high, it indicates that there are too many abnormal regions, and the product's coating quality is unqualified, making it a defective product. Conversely, if the ratio is low, it indicates that there are few or no abnormal regions, and the product's coating quality is qualified, making it a good product. Therefore, the detection method provided in this embodiment can detect the coating quality of the product in a timely and accurate manner, preventing defective products from entering subsequent processes and effectively reducing rework costs. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart of a method for detecting the quality of homogenization provided in an embodiment of this disclosure;

[0039] Figure 2 This is a flowchart of another method for detecting the quality of homogenization provided in this embodiment of the present disclosure;

[0040] Figure 3 This is a schematic diagram of grayscale image capture provided in an embodiment of this disclosure;

[0041] Figure 4 This is a grayscale image provided in the embodiments of this disclosure;

[0042] Figure 5 This is a region division diagram provided in an embodiment of this disclosure;

[0043] Figure 6 This is a block diagram of a system for detecting the quality of homogenization provided in an embodiment of this disclosure;

[0044] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0046] Photolithography is an important process in the manufacturing of semiconductor chips, in which photoresist needs to be evenly coated on the surface of the product.

[0047] In related technologies, photoresist is applied using a spin coater. The spin coater pumps the photoresist into a tube and sprays it onto the surface of the product while it rotates horizontally at a uniform speed, thus achieving the purpose of photoresist application.

[0048] However, issues such as unstable air volume in the adhesive pump, excessively high viscosity of the photoresist, insufficient adhesive volume, and excessively high or low distance between the adhesive tube and the product surface can all lead to substandard homogenization quality. Failure to promptly identify poorly homogenized products, resulting in their submission to subsequent processes, will increase rework costs.

[0049] To address the aforementioned technical problems, this disclosure provides a method for detecting the quality of spin coating. Figure 1 For a flowchart of the detection method, see [link / reference]. Figure 1 In this embodiment, the detection method includes:

[0050] Step 101: Obtain a grayscale image containing the product to be inspected.

[0051] Step 102: Divide the grayscale image into multiple regions and determine the grayscale value of each region.

[0052] Step 103: Determine the area of ​​the product region and the area of ​​the abnormal region. The product region is the area occupied by the product in the grayscale map, and the abnormal region is the area in the product region where the difference between the grayscale value and the average grayscale value of the product region exceeds a preset threshold.

[0053] Step 104: Calculate the ratio between the area of ​​the abnormal area and the area of ​​the product area. If the ratio is greater than the preset ratio, the product is determined to be a defective product. If the ratio is not greater than the preset ratio, the product is determined to be a good product.

[0054] The detection method provided in this disclosure enables timely and accurate detection of the uniform coating quality of photoresist applied to a product. First, a grayscale image is acquired, containing the product to be inspected. Next, the grayscale image is divided into multiple regions, and the grayscale value of each region is determined. This facilitates the identification of the product region and any abnormal regions within the product region within the grayscale image. Then, the areas of the product region and the abnormal regions are determined. The product region is the area occupied by the product within the grayscale image, and the abnormal region is the area within the product region where the difference between its grayscale value and the average grayscale value of the product region exceeds a preset threshold. Finally, the ratio between the area of ​​the abnormal region and the area of ​​the product region is calculated. If the ratio is greater than a preset ratio, the product is determined to be defective; if the ratio is not greater than the preset ratio, the product is determined to be good.

[0055] In other words, by calculating the ratio between the area of ​​the abnormal region and the area of ​​the product region, if the ratio is too high, it indicates that there are too many abnormal regions, and the product's coating quality is unqualified, making it a defective product. Conversely, if the ratio is low, it indicates that there are few or no abnormal regions, and the product's coating quality is qualified, making it a good product. Therefore, the detection method provided in this embodiment can detect the coating quality of the product in a timely and accurate manner, preventing defective products from entering subsequent processes and effectively reducing rework costs.

[0056] Figure 2 Another method for detecting the quality of spin coating provided in this disclosure embodiment, combined with Figure 2 In this embodiment, the detection method includes:

[0057] Step 201: The finished product is sent from the coating mechanism to the shooting mechanism.

[0058] In step 201, the product that has been coated in the coating mechanism is sent to the imaging mechanism by a robotic arm, so as to facilitate the acquisition of grayscale images in subsequent steps.

[0059] Step 202: Provide a first light source 10 to illuminate the surface of the article 100 (see...). Figure 3 ).

[0060] For example, the first light source 10 is an LED lamp, which can illuminate the surface of the product 100 and facilitate brightness adjustment.

[0061] Step 203: Adjust the brightness of the first light source 10 to adjust the average grayscale value of the surface of the product 100 so that the average grayscale value of the surface of the product 100 is 100-200.

[0062] In step 203, increasing the brightness of the first light source 10 increases the average grayscale value of the surface of the product 100, while decreasing the brightness of the first light source 10 decreases the average grayscale value of the surface of the product 100. In other words, by adjusting the brightness of the first light source 10, the average grayscale value of the surface of the product 100 can be adjusted to the desired appropriate value, ensuring that the average grayscale value of the grayscale image is appropriate when acquiring the grayscale image in subsequent steps.

[0063] For example, the average grayscale value of the surface of product 100 is adjusted to 140. Adjusting the average grayscale value of the surface of product 100 to the above value makes it easier to distinguish the product area in the grayscale image and the abnormal area in the product area in subsequent steps, effectively improving the detection accuracy.

[0064] Step 204: Obtain the grayscale image (see...) Figure 4The grayscale image contains the product to be detected.

[0065] In step 204, the product to be inspected is photographed by a camera in the imaging mechanism to obtain a grayscale image.

[0066] Step 205: Divide the grayscale image into multiple regions (see...) Figure 5 ), and determine the grayscale value for each region.

[0067] In step 205, the grayscale image is divided into multiple regions based on the resolution of the grayscale image, with each pixel as the smallest region.

[0068] In other words, each pixel is divided into a region, which can effectively improve the detection accuracy.

[0069] Step 206: Determine the area of ​​the product region, which is the area occupied by the product in the grayscale image.

[0070] Step 206 is achieved through the following steps:

[0071] First, a second light source 20 is provided to illuminate the back of the article 100 (see...). Figure 3 ).

[0072] For example, the second light source 20 is an LED light, which can illuminate the back of the product 100 and make it easy to adjust the brightness.

[0073] Next, the brightness of the second light source 20 is adjusted to adjust the grayscale value of the product outline and the grayscale value of the background, so that the difference between the grayscale value of the product outline and the grayscale value of the background is greater than 100. The background is the area in the grayscale image excluding the product (100), that is, the blank area or the irrelevant area.

[0074] In the above implementation, the greater the brightness of the second light source 20, the greater the difference between the grayscale values ​​of the product outline and the background. By using the second light source 20 to illuminate the back of the product 100, the difference between the grayscale values ​​of the product outline and the background can be effectively increased, thereby highlighting the product outline and making it easier to determine the product area in the grayscale image.

[0075] Finally, the area occupied by the region within the outline of the product is determined as the area of ​​the product region.

[0076] In the above implementation, the area within the outline of the product is defined as the product area, and the number of pixels in the product area is determined to determine the area of ​​the product area.

[0077] Step 207: Determine the area of ​​the abnormal region. The abnormal region is the area in the work-in-process area where the difference between the grayscale value and the average grayscale value of the work-in-process area exceeds a preset threshold.

[0078] For example, the preset threshold is a manually set value that can be selected according to needs.

[0079] For example, if the average grayscale value of the product area is 140 and the preset threshold is 10, then areas with grayscale values ​​less than 130 ( Figure 5 Darker areas and areas with grayscale values ​​greater than 150 are identified as abnormal areas. Figure 5 (The brighter areas in the middle). By determining the number of pixels in the abnormal area, the area of ​​the abnormal area can be determined.

[0080] To improve the efficiency of identifying outlier regions, the grayscale values ​​of each region can be fuzzed using a mean. First, a fixed calculation unit for grayscale is determined. Then, based on this fixed unit, the grayscale value corresponding to each region is fuzzed. For example, if the fixed calculation unit is 10, then the grayscale value of each region in the grayscale map is taken from the set (0, 10, 20, 30…230, 240, 250). If the grayscale value of a region is 14, then its grayscale value is fuzzed to 10; if the grayscale value of a region is 15, then its grayscale value is fuzzed to 20. As another example, if the fixed calculation unit is 20, then the grayscale value of each region in the grayscale map is taken from the set (0, 20, 40, 60…220, 240, 250). If the grayscale value of a region is 29, then its grayscale value is fuzzed to 20; if the grayscale value of a region is 30, then its grayscale value is fuzzed to 40.

[0081] Step 208: Calculate the ratio between the area of ​​the abnormal region and the area of ​​the product region. If the ratio is greater than a preset ratio, the product is determined to be defective, and step 209 is executed. If the ratio is not greater than the preset ratio, the product is determined to be good.

[0082] For example, the preset ratio is a manually set value that can be selected according to needs.

[0083] For example, if the area of ​​the product area is 100 and the area of ​​the abnormal area is 4, if the preset ratio is 3 / 100, then the product is a good product; if the preset ratio is 2 / 100, then the product is a defective product.

[0084] Step 209: Rework the product and re-spread the adhesive.

[0085] In step 209, the product that has completed inspection in the imaging mechanism is sent to the rework mechanism via a robotic arm. After rework fluid is sprayed in the rework mechanism, it is sent again to the coating mechanism via the robotic arm for coating. After coating is completed, steps 201-208 are repeated to inspect the product again.

[0086] The detection method provided in this disclosure enables timely and accurate detection of the uniform coating quality of photoresist applied to a product. First, a grayscale image is acquired, containing the product to be inspected. Next, the grayscale image is divided into multiple regions, and the grayscale value of each region is determined. This facilitates the identification of the product region and any abnormal regions within the product region within the grayscale image. Then, the areas of the product region and the abnormal regions are determined. The product region is the area occupied by the product within the grayscale image, and the abnormal region is the area within the product region where the difference between its grayscale value and the average grayscale value of the product region exceeds a preset threshold. Finally, the ratio between the area of ​​the abnormal region and the area of ​​the product region is calculated. If the ratio is greater than a preset ratio, the product is determined to be defective; if the ratio is not greater than the preset ratio, the product is determined to be good.

[0087] In other words, by calculating the ratio between the area of ​​the abnormal region and the area of ​​the product region, if the ratio is too high, it indicates that there are too many abnormal regions, and the product's coating quality is unqualified, making it a defective product. Conversely, if the ratio is low, it indicates that there are few or no abnormal regions, and the product's coating quality is qualified, making it a good product. Therefore, the detection method provided in this embodiment can detect the coating quality of the product in a timely and accurate manner, preventing defective products from entering subsequent processes and effectively reducing rework costs.

[0088] Figure 6 This is a block diagram of a system for detecting the quality of homogenization provided in an embodiment of the present disclosure, combined with... Figure 6 In this embodiment, the detection system includes an image capture module 1, a region division module 2, an area determination module 3, and a calculation module 4.

[0089] The imaging module 1 acquires a grayscale image containing the product to be inspected. The region division module 2 divides the grayscale image into multiple regions and determines the grayscale value of each region. The area determination module 3 determines the area of ​​the product region and the area of ​​the abnormal region. The product region is the area occupied by the product in the grayscale image, and the abnormal region is the region where the difference between the grayscale value and the average grayscale value of the product region exceeds a preset threshold. The calculation module 4 calculates the ratio between the area of ​​the abnormal region and the area of ​​the product region. If the ratio is greater than a preset ratio, the product is determined to be defective; if the ratio is not greater than the preset ratio, the product is determined to be good.

[0090] In this embodiment, the detection system further includes a first lighting module and a first brightness adjustment module.

[0091] The first lighting module provides a first light source to illuminate the surface of the product. The first brightness adjustment module adjusts the brightness of the first light source to adjust the average grayscale value of the product surface, making the average grayscale value of the product surface between 100 and 200.

[0092] In this embodiment, the region division module 2 is further configured to divide the grayscale image into multiple regions based on the resolution of the grayscale image, with each pixel as the smallest region.

[0093] In this embodiment, the area determination module 3 includes: a second lighting module, a second brightness adjustment module, and an area determination submodule.

[0094] The second lighting module provides a second light source to illuminate the back of the product. The second brightness adjustment module adjusts the brightness of the second light source to regulate the grayscale values ​​of the product outline and the background, ensuring that the difference between their grayscale values ​​is greater than 100. The background is the area in the grayscale image excluding the product. The area determination submodule determines the area occupied by the region within the product outline as the area of ​​the product region.

[0095] The above detection systems have the same beneficial effects as the detection methods described above, and will not be repeated here.

[0096] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure, combined with... Figure 7 The computer device 700 may include one or more of the following components: processor 701, memory 702, communication interface 703, and bus 704.

[0097] The processor 701 includes one or more processing cores. The processor 701 executes various functional applications and information processing by running software programs and modules. The memory 702 and the communication interface 703 are connected to the processor 701 via a bus 704. The memory 702 can be used to store at least one instruction, which the processor 701 uses to execute to implement the various steps in the aforementioned detection method.

[0098] Furthermore, the memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but not limited to: magnetic disks or optical disks, electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), read-only memory (ROM), magnetic storage, flash memory, and programmable read-only memory (PROM).

[0099] This disclosure also provides a computer storage medium in which the above detection method is implemented when computer instructions are executed by a processor.

[0100] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, but do not exclude other elements or objects.

[0101] The above description is merely an optional embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A method for detecting the quality of homogenized glue, characterized in that, include: A first light source is provided to illuminate the surface of the product to be inspected, wherein the product is a product whose surface is coated with photoresist in the photolithography process of manufacturing semiconductor chips; The brightness of the first light source is adjusted to adjust the average grayscale value of the product surface, so that the average grayscale value of the product surface is 100~200, and the brightness of the first light source and the average grayscale value of the product surface are positively correlated. The surface of the article is photographed to obtain a grayscale image, the grayscale image containing the article; The grayscale image is divided into multiple regions, and the grayscale value of each region is determined; A second light source is provided to illuminate the back of the article, wherein the back of the article and the surface of the article are opposite sides of the article, and the second light source and the first light source are located on opposite sides of the article, respectively. The brightness of the second light source is adjusted to adjust the grayscale value of the outline of the product and the grayscale value of the unrelated area, so that the difference between the grayscale value of the outline of the product and the grayscale value of the unrelated area is greater than 100, and the brightness of the second light source and the difference between the grayscale value of the outline of the product and the grayscale value of the unrelated area are positively correlated. In the grayscale image, the product area and the irrelevant area are determined, and the area of ​​the product area is determined. The product area is the area occupied by the product in the grayscale image, and the irrelevant area is the remaining area in the grayscale image other than the product area. An abnormal region is identified in the product area, and the area of ​​the abnormal region is determined. The abnormal region is the area in the product area where the difference between the grayscale value and the average grayscale value of the product area exceeds a preset threshold. Calculate the ratio between the area of ​​the abnormal region and the area of ​​the product region. If the ratio is greater than a preset ratio, the product is determined to be a defective product. If the ratio is not greater than the preset ratio, the product is determined to be a good product. If the product is determined to be defective, it shall be reworked by spraying rework liquid onto the product and then re-coating it.

2. The detection method according to claim 1, characterized in that, The grayscale image is divided into multiple regions, including: Based on the resolution of the grayscale image, the grayscale image is divided into multiple regions, with each pixel as the smallest region.

3. A system for detecting the quality of homogenized glue application, characterized in that, include: The first lighting module is used to provide a first light source to illuminate the surface of the product to be inspected. The product is a product whose surface is coated with photoresist in the photolithography process of manufacturing semiconductor chips. The first brightness adjustment module is used to adjust the brightness of the first light source to adjust the average grayscale value of the product surface, so that the average grayscale value of the product surface is 100~200, and the brightness of the first light source and the average grayscale value of the product surface are positively correlated. The imaging module is used to acquire a grayscale image, wherein the grayscale image contains the product; The region division module is used to divide the grayscale image into multiple regions and determine the grayscale value of each region; An area determination module is used to determine the area of ​​the product area and the area of ​​the abnormal area. The product area is the area occupied by the product in the grayscale image. The abnormal area is the area where the difference between the grayscale value and the average grayscale value of the product area exceeds a preset threshold. The area determination module includes a second lighting module for providing a second light source to illuminate the back of the product. The back of the product and the surface of the product are the opposite sides of the product. The second light source and the first light source are located on opposite sides of the product, respectively. The second brightness adjustment module is used to adjust the brightness of the second light source to adjust the grayscale value of the outline of the product and the grayscale value of the irrelevant area, so that the difference between the grayscale value of the outline of the product and the grayscale value of the irrelevant area is greater than 100, and the brightness of the second light source and the difference between the grayscale value of the outline of the product and the grayscale value of the irrelevant area are positively correlated. The calculation module is used to calculate the ratio between the area of ​​the abnormal area and the area of ​​the product area. If the ratio is greater than a preset ratio, the product is determined to be a defective product. If the ratio is not greater than the preset ratio, the product is determined to be a good product. The rework module is used to rework the product when it is determined to be a defective product. The rework module sprays rework liquid onto the product and then re-coats the product with adhesive.

4. The detection system according to claim 3, characterized in that, The region division module is further configured to divide the grayscale image into multiple regions, with each pixel as the smallest region, based on the resolution of the grayscale image.

5. A computer device, characterized in that, The computer device includes a processor and a memory configured to store executable instructions of the processor; the processor is configured to perform the method for detecting the quality of homogenization as described in any one of claims 1 to 2.

6. A computer storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, they implement the method for detecting the homogenization quality according to any one of claims 1 to 2.

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