A quality control method for photovoltaic cell manufacturing process

Abstract

This application discloses a quality control method for the manufacturing process of photovoltaic cells, comprising: dividing the cell manufacturing workshop into a grid-like structure, including a texturing workshop, a cleanroom, a coating workshop, and a printing workshop; establishing a quality analysis file for the cell manufacturing process to show the correspondence between contact points in the cell manufacturing process and their respective workshops; assigning the quality analysis file to the texturing workshop, cleanroom, coating workshop, and printing workshop according to the cell manufacturing process; performing defect detection on the cells and classifying the defect types, and assigning them to one or more of the texturing workshop, cleanroom, coating workshop, and printing workshop according to the defect types and the quality analysis file, thereby quickly identifying and adjusting abnormal workshops. This application achieves visualized monitoring of the quality yield of the manufacturing process, enables rapid identification of quality anomalies, greatly shortens the quality control cycle, and effectively reduces manufacturing costs.

Description

Technical Field

[0001] This application relates to the field of photovoltaic cell technology, and in particular to a quality control method for the manufacturing process of photovoltaic cells. Background Technology

[0002] Currently, the degree of automation in solar cell manufacturing in the photovoltaic industry is increasing, which in turn leads to more and more tools coming into contact with solar cells during the manufacturing process. At the same time, the number of process steps in solar cell manufacturing is also increasing, which in turn leads to a corresponding increase in the solar cell manufacturing chain.

[0003] Therefore, quality control during the solar cell manufacturing process has become increasingly difficult, resulting in problems such as difficulty in distinguishing the types of quality defects, difficulty in pinpointing the impact points of quality anomalies, long investigation cycles for quality anomalies, and difficulty in visually controlling the yield of the manufacturing process, which has caused significant losses to photovoltaic cell manufacturers. Summary of the Invention

[0004] The purpose of this application is to overcome the problems in the existing technology of difficult differentiation of quality defect types, difficulty in locking the impact points of quality anomalies, long investigation cycle of quality anomalies, and difficulty in visualizing and controlling the yield of the manufacturing process during the battery cell manufacturing process.

[0005] Therefore, this application provides a quality control method for the manufacturing process of photovoltaic cells. This method can quickly locate the abnormal location and cause of the abnormality in the cell and take action to address it, thereby achieving the effect of eradicating the abnormality.

[0006] The technical solution is as follows:

[0007] A quality control method for photovoltaic cell manufacturing includes the following steps: dividing the cell manufacturing workshop into a grid-like structure, comprising a texturing workshop, a cleanroom, a coating workshop, and a printing workshop; establishing a quality analysis file for the cell manufacturing process to show the correspondence between contact points in the cell manufacturing process and their respective workshops; assigning the quality analysis file to the texturing workshop, cleanroom, coating workshop, and printing workshop according to the cell manufacturing process; performing defect detection on the cells and classifying the defect types, and assigning them to one or more of the texturing workshop, cleanroom, coating workshop, and printing workshop according to the defect types and the quality analysis file, thereby enabling rapid identification and adjustment of abnormal workshops.

[0008] Optionally, a quality analysis archive for the cell manufacturing process can be established to show the correspondence between the contact points in the cell manufacturing process and the workshops they are located in. This includes collecting the types of defect images that appear during EL detection of cells in the manufacturing process, classifying each type of defect image, and assigning it to each workshop for reference.

[0009] Optionally, defect detection of the solar cells may include performing online EL inspection on the solar cells using online EL inspection equipment.

[0010] Optionally, online EL inspection can be categorized according to the type of defect detected in the battery cells as follows: belt marks, boat marks, microcracks, black spots, broken grids, scratches, pitted black spots, and suction cup marks.

[0011] Optionally, online EL testing is performed hourly on all cells on the production line, generating over-detection rate, under-detection rate, and yield and defect rate for each production line for various defect types. Based on the abnormal indicators of the cells, adjustments are made to the workshop containing the cells with abnormal indicators according to quality standards, and the cells are then tested online again after adjustment. The over-detection rate refers to the proportion of cells meeting quality standards among those with abnormal indicators; the under-detection rate refers to the proportion of cells with abnormal indicators among those meeting quality standards.

[0012] Optionally, defect detection of solar cells may also include offline EL testing using offline EL testing equipment, wherein offline EL testing involves separately packaging and testing solar cells that are defective after online EL testing.

[0013] Optionally, offline EL inspection is classified according to the defect type of the detected battery cell as follows: black spots, black dots, over-etching, scratches, ultra-clean suction cup marks, blackening of the top teeth, graphite boat marks, suction pen marks, coating equipment suction cup marks, broken grids, overprinting marks, and support leg marks.

[0014] Optionally, offline EL testing is performed every 3-5 hours to test defective products detected by online EL testing, generating the overall offline yield and offline defect rate.

[0015] Optionally, online and offline EL devices can be shared within a local area network to enable quick viewing of the EL image type of defective cells.

[0016] The technical solution of this invention has the following advantages:

[0017] The quality control method for photovoltaic cell manufacturing proposed in this invention adjusts the parameters or equipment of workshop processes according to quality standards for abnormal indicators of cells, and quickly adjusts the abnormal workshop based on the EL image of defective products and the quality analysis file. This achieves the effects of easy differentiation of quality defect types, easy identification of the impact points of quality abnormalities, and short quality abnormality investigation cycle. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the quality control method for the photovoltaic cell manufacturing process in this embodiment. Figure 1 ;

[0020] Figure 2 This is a flowchart illustrating the quality control method for the photovoltaic cell manufacturing process in this embodiment. Figure 2 ;

[0021] Figure 3 This is a schematic diagram of an EL test image of the suction cup of the monocrystalline automated equipment displayed in the texturing workshop file in this embodiment;

[0022] Figure 4 This is a schematic diagram of an EL test image showing the belt imprint of a single-crystal automated equipment in the texturing workshop file in this embodiment;

[0023] Figure 5 This is a schematic diagram of the EL test image cached by the laser automated equipment displayed in the file of the fabrication workshop in this embodiment;

[0024] Figure 6 This is a schematic diagram of an EL test image showing the suction cup print of the laser automated equipment in the file of the fabrication workshop in this embodiment;

[0025] Figure 7 This is a schematic diagram of the EL test image of the belt print of the laser automated equipment displayed in the file of the fabrication workshop in this embodiment;

[0026] Figure 8 This is a schematic diagram of an EL test image showing the roller print of the etching tank equipment in the fabrication workshop file in this embodiment;

[0027] Figure 9 This is a schematic diagram of an EL test image showing an asymmetric water-pressed shaft print in the fabrication workshop archive in this embodiment.

[0028] Figure 10 This is a schematic diagram of an EL test image showing symmetrical water-pressed shaft prints in the fabrication workshop archives in this embodiment;

[0029] Figure 11 This is a schematic diagram of an EL test image showing the etching automation equipment flipper and belt print in the fabrication workshop archive in this embodiment;

[0030] Figure 12This is a schematic diagram of the EL test image of the quartz boat print displayed in the cleanroom archive in this embodiment;

[0031] Figure 13 This is a schematic diagram of the EL test image of the wet flower basket print displayed in the cleanroom archive in this embodiment;

[0032] Figure 14 This is a schematic diagram of the EL test image showing the dry flower basket print in the cleanroom archive in this embodiment;

[0033] Figure 15 This is a schematic diagram of the EL test image of the contact print of the automated equipment for coated alumina in the cleanroom file in this embodiment;

[0034] Figure 16 This is a schematic diagram of an EL test image showing the contact print of the diffusion automation equipment in the cleanroom archive in this embodiment;

[0035] Figure 17 This is a schematic diagram of the EL test image of the contact print of the oxidation automation equipment displayed in the cleanroom file in this embodiment;

[0036] Figure 18 This is a schematic diagram of the EL test image showing the graphite boat card dot print in the coating workshop file in this embodiment;

[0037] Figure 19 This is a schematic diagram of an EL test image showing a graphite boat print in the coating workshop archive in this embodiment.

[0038] Figure 20 This is a schematic diagram of the EL test image displayed in the coating workshop file of this embodiment, showing the suction cup print of the coating equipment;

[0039] Figure 21 This is a schematic diagram of an EL test image showing a suction pen print in the coating workshop archive in this embodiment;

[0040] Figure 22 This is a schematic diagram of an EL test image showing the contact print of automated equipment in the coating workshop archive in this embodiment;

[0041] Figure 23 This is a schematic diagram of the EL test image displayed in the printing workshop archives of this embodiment, showing the printing laser turntable print.

[0042] Figure 24 This is a schematic diagram of the EL test image of the laser suction cup print displayed in the printing workshop archive in this embodiment;

[0043] Figure 25 This is a schematic diagram of the EL test image of the contact print from the automated laser printing equipment displayed in the printing workshop archives in this embodiment;

[0044] Figure 26 This is a schematic diagram of the EL test image of the printing line lifting platform printing shown in the printing workshop archive in this embodiment;

[0045] Figure 27 This is a schematic diagram of the EL test image showing the support leg print in the printing workshop archive in this embodiment;

[0046] Figure 28 This is a schematic diagram of the EL test image of the printing line buffer print displayed in the printing workshop archive in this embodiment;

[0047] Figure 29 This is a schematic diagram of the EL test image showing the belt print on the printing line in the printing workshop archive in this embodiment;

[0048] Figure 30 This is a schematic diagram of the EL test image printed by the printing line flipper, displayed in the printing workshop archive in this embodiment.

[0049] Explanation of reference numerals in the attached figures:

[0050] 1. Textile fabrication workshop; 11. Suction cup print of monocrystalline automated equipment; 12. Belt print of monocrystalline automated equipment; 13. Buffer print of laser automated equipment; 14. Suction cup print of laser automated equipment; 15. Belt print of laser automated equipment; 16. Roller print of etching tank equipment; 17. Asymmetric water pressure roller print; 18. Symmetric water pressure roller print; 19. Rotator and belt print of etching automated equipment; 2. Cleanroom; 21. Quartz boat print; 22. Wet basket print; 23. Dry basket print; 24. Contact print of coated alumina automated equipment. 25. Contact printing for automated diffusion equipment; 26. Contact printing for automated oxidation equipment; 3. Coating workshop; 31. Graphite boat dot printing; 32. Graphite boat printing; 33. Suction cup printing for coating equipment; 34. Suction pen printing; 35. Contact printing for automated equipment; 4. Printing workshop; 41. Printing laser turntable printing; 42. Printing laser suction cup printing; 43. Contact printing for automated laser equipment; 44. Printing line lifting platform printing; 45. Support leg printing; 46. Printing line buffer printing; 47. Printing line belt printing; 48. Printing line flipper printing. Detailed Implementation

[0051] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0052] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0053] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0054] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0055] Reference Figure 1 As shown in the embodiments of this application, a quality control method for the manufacturing process of photovoltaic cells is provided, including the following steps:

[0056] S101. The battery cell manufacturing workshop is divided into a grid-like structure, comprising a texturing workshop, an ultra-clean workshop, a coating workshop, and a printing workshop.

[0057] In this embodiment, it should be noted that, based on the battery cell manufacturing process, the entire life cycle of the battery cell is roughly divided into four workshops: texturing workshop 1, ultra-clean workshop 2, coating workshop 3, and printing workshop 4.

[0058] Texturing workshop 1 specifically utilizes the anisotropic etching of silicon to form millions of tetrahedral pyramids, or pyramid structures, on each square centimeter of silicon surface. Due to the multiple reflections and refractions of incident light on the surface, light absorption is increased, improving the short-circuit current and conversion efficiency of the cell. Furthermore, photovoltaic cells are divided into monocrystalline and polycrystalline cells. For monocrystalline cells, the different etching rates of alkaline solutions on the various crystal planes of monocrystalline silicon create a pyramid-like textured surface on the silicon wafer. For polycrystalline cells, the strong oxidizing properties of nitric acid and the complexing properties of hydrofluoric acid are used to oxidize and peel away the complexes on the silicon wafer, resulting in isotropic non-uniform etching of the silicon surface, thus forming a pit-like textured surface.

[0059] Cleanroom 2 specifically refers to maintaining the overall cleanliness of the relevant processes within the workshop. These processes mainly include diffusion, selective emitter laser doping, edge etching, and oxidation annealing. Specifically, the diffusion process involves penetrating a thin layer of phosphorus onto the surface of P-type silicon, transforming the surface into N-type and forming a PN junction. Then, a POCl3 solution is carried by gas into the diffusion furnace tube, where it reacts to form phosphorus precipitate on the surface. The phosphorus then penetrates into the silicon wafer at high temperature, forming the N-region. The edge etching process mainly involves insulating the back and four sides of the wafer to remove the PN junction and prevent short circuits between the positive and negative electrodes. This is primarily achieved using a high-frequency glow discharge reaction, which activates the reactive gas into active particles that diffuse to the edge of the silicon wafer and react with it to generate volatile silicon tetrafluoride, which is then removed. Other processes are not described in detail in this embodiment and are all conventional techniques in the field.

[0060] In coating workshop 3, the reflectivity of the polished silicon surface is 35%. To reduce surface reflection and improve the conversion efficiency of the battery, a silicon nitride antireflection film needs to be deposited. Process methods include vacuum coating, ion plating, sputtering, printing, plasma-enhanced chemical vapor deposition, and spraying, etc. Currently, sputtering is the most commonly used method. Its principle is that when high-energy particles bombard a solid surface, the particles on the solid surface gain energy and escape from the surface, depositing onto the substrate.

[0061] Printing workshop 4 specifically involves solar cells that, after texturing, diffusion, and coating processes, have formed PN junctions and can generate current under sunlight. To conduct this current, positive and negative electrodes need to be fabricated on the cell surface. There are many methods for manufacturing electrodes, but screen printing is currently the most common production process for solar cell electrodes. Screen printing uses an impression method to print a predetermined pattern onto a substrate. The equipment consists of three parts: silver-aluminum paste printing on the back of the cell, aluminum paste printing on the back of the cell, and silver paste printing on the front of the cell. Its working principle is as follows: The ink passes through the mesh of the screen's pattern section. A squeegee applies pressure to the ink section of the screen while moving towards the other end. During this movement, the ink is squeezed from the mesh of the pattern section onto the substrate by the squeegee. Due to the viscosity of the ink, the print adheres within a certain range. During printing, the squeegee maintains linear contact with the screen printing plate and the substrate, and the contact line moves with the squeegee, thus completing the printing process.

[0062] S102. Establish a quality analysis archive for the cell manufacturing process to show the correspondence between the contact points in the cell manufacturing process and the workshops in which they are located.

[0063] In this embodiment, it should be noted that as the level of automation in the solar cell manufacturing field increases, the number of tools and equipment that come into contact with the solar cells during the manufacturing process also increases. Furthermore, as the number of process steps in solar cell manufacturing increases, the solar cell manufacturing chain grows accordingly. Therefore, it is necessary to establish a quality analysis file for the solar cell manufacturing process. This quality analysis file is mainly used to show the correspondence between the contact points between the solar cells and the tools and equipment during the solar cell manufacturing process and the workshops in which they are located. The establishment of a quality analysis archive mainly involves collecting defect images of solar cells detected by EL (Electroluminescent) at each stage of the manufacturing process. Each defect image type is categorized and mapped to a specific workshop for reference. This defect image type reflects its correspondence with the contact between the solar cell and the equipment during the manufacturing process. For example, the suction cup of the monocrystalline automated equipment may leave a suction cup mark when it contacts the solar cell. This suction cup corresponds to the texturing workshop 1. Thus, by mapping the suction cup mark detected by the EL test of the solar cell to the suction cup of the monocrystalline automated equipment in the texturing workshop 1, the correspondence between the contact points of the equipment during the solar cell manufacturing process and the corresponding workshop can be shown. By referring to the defect image types of the solar cell EL test images in the quality analysis archive, the abnormal workshop can be quickly identified.

[0064] S103. The quality analysis files are divided into the texturing workshop, ultra-clean workshop, coating workshop and printing workshop according to the battery cell manufacturing process.

[0065] Figures 3-11 A schematic diagram of EL test images showing the types of defective battery cells in the fabrication workshop archives; Figures 12-17 A schematic diagram of EL test images showing the types of defective battery cells in the cleanroom archives; Figures 18-22 A schematic diagram of EL test images showing the types of defective battery cells in the coating workshop archives; Figures 23-30 This is a schematic diagram of EL test images showing the types of defective battery cells in the printing workshop archives.

[0066] In this embodiment, it should be noted that, in order to facilitate the rapid identification and handling of abnormal locations and causes during the battery cell manufacturing process, and to achieve the complete eradication of abnormalities, the quality analysis files are divided according to the battery cell manufacturing process into the texturing workshop 1, the cleanroom 2, the coating workshop 3, and the printing workshop 4. Specifically, the files are assigned to the corresponding workshops based on the type of defect images within the quality analysis files. For example, the quality analysis files can be further subdivided into texturing workshop files, cleanroom files, coating workshop files, and printing workshop files, as shown in the reference. Figures 3-11As shown, the main types of defect images collected in the texturing workshop archives include: suction cup marks from single-crystal automated equipment 11, belt marks from single-crystal automated equipment 12, buffer marks from laser automated equipment 13, suction cup marks from laser automated equipment 14, belt marks from laser automated equipment 15, roller marks from etching tank equipment 16, asymmetric water pressure roller marks 17, symmetric water pressure roller marks 18, and marks from the etch automation equipment flipper and belt 19; (Refer to...) Figures 12-17 As shown, the defect image types collected in the cleanroom archives include: quartz boat print 21, wet basket print 22, dry basket print 23, contact print from automated alumina coating equipment 24, contact print from automated diffusion equipment 25, and contact print from automated anodizing equipment 26; (Refer to...) Figures 18-22 As shown, the defect image types collected in the coating workshop archives include: graphite boat mark 31, graphite boat mark 32, coating equipment suction cup mark 33, suction pen mark 34, and automated equipment contact mark 35; (Refer to...) Figures 23-30 As shown, the defect image types collected in the printing workshop archives include: printing laser turntable prints 41, printing laser suction cup prints 42, printing laser automated equipment contact prints 43, printing line lifting table prints 44, support leg prints 45, printing line buffer prints 46, printing line belt prints 47, and printing line flipper prints 48. Therefore, each independent workshop can set inspection reference standards, and later, when defective cells are detected, the corresponding workshop can be quickly identified simply by comparing them with the quality analysis archives.

[0067] S104. Perform defect detection on the battery cells and classify the defect types. Based on the defect type, refer to the quality analysis file to classify them into one or more of the texturing workshop, ultra-clean workshop, coating workshop and printing workshop, so as to quickly locate the abnormal workshop and make rapid adjustments.

[0068] Reference Figure 1 and Figure 2 As shown in this embodiment, it should be noted that online and offline EL inspection equipment are used to perform online and offline EL inspections on the solar cells. Both online and offline EL inspection equipment use the principle of electroluminescence to detect internal defects in solar cell modules: whether there are microcracks, fragments, broken cells, poor soldering, black cores, black edges, broken grids, or abnormal phenomena such as individual cells with different conversion efficiencies due to module defects. The online and offline EL inspection equipment are configured to be shared within a local area network, and any computer terminal on the local area network can access them, thereby allowing for quick viewing of the EL image types of defective products.

[0069] The online EL inspection, categorized by defect type of the solar cell, includes the following: belt marks, boat marks, microcracks, black spots, broken grids, scratches, pitted black spots, and suction cup marks. Other defect types may exist in other embodiments, and the specific form is not limited in this embodiment. Black spots and pitted black spots are easily identified in the texturing workshop 1 based on the texturing workshop archives; scratches and suction cup marks are easily identified in the cleanroom 2 based on the cleanroom archives; boat marks and belt marks are easily identified in the coating workshop 3 based on the coating workshop archives; and microcracks and broken grids are easily identified in the printing workshop 4 based on the printing workshop archives. Black spots and pitted black spots mainly occur after the silicon wafers have undergone back polishing during the etching process. During the etching and unloading process, the silicon wafers are transported using a belt conveyor. This belt is a single-plane conveyor type. Over time, the belt rubs against the belt drive pulley, causing wear and powdery substances to form on the belt edges. When this powdery substance comes into contact with the back of the silicon wafer, it adheres to the back of the wafer, resulting in black spots or black patches under EL inspection. Furthermore, during the module manufacturing process, stringing machines, laminators, and framing machines directly apply force to the modules. Improper parameter settings or equipment malfunctions can cause microcracks in the cells. Similarly, using incorrect process parameters during cell production can also induce microcracks. For example, excessively high welding temperatures can easily cause microcracks, and unreasonable lamination parameter settings, excessively high or too rapid vacuum pressure can also lead to microcracks. Other types of defects will not be described in detail, as they are all common defects in the cell manufacturing process in this field.

[0070] In this embodiment, it should also be noted that online EL testing is performed hourly on all cells on the production line, generating various over-detection rates, under-detection rates, and online yield and defect rates for each production line. Based on the abnormal indicators of the cells, adjustments are made to the workshop containing the cells with abnormal indicators according to quality standards, and the cells are then tested online again after adjustment. The over-detection rate refers to the proportion of cells meeting quality standards among those with abnormal indicators; the under-detection rate refers to the proportion of cells with abnormal indicators among those meeting quality standards. Based on the over-detection and under-detection rates, process parameters or workshop hardware are improved according to quality standards.

[0071] The offline EL inspection equipment is used to separately package and test the battery cells that are found to be defective by the online EL inspection equipment. The offline EL inspection is classified according to the defect type of the battery cells as follows: black spots, black dots, over-etching, scratches, ultra-clean suction cup marks, blackening of the top teeth, graphite boat marks, suction pen marks, coating equipment suction cup marks, broken grids, overprinting marks, and support leg marks. Other defect types may exist in other embodiments, and the specific form is not limited in this embodiment.

[0072] Among them, black dots, black patches, and over-etching are easily identified in the velvet workshop archives and located in velvet workshop 1; scratches, ultra-clean suction cup prints, and blackening of the top teeth are easily identified in the ultra-clean workshop archives and located in ultra-clean workshop 2; graphite boat prints, suction pen prints, and coating equipment suction cup prints are easily identified in the coating workshop archives and located in coating workshop 3; and hidden cracks, overprints, and support leg prints are easily identified in the printing workshop archives and located in printing workshop 4.

[0073] In this embodiment, it should also be noted that offline EL testing generates the overall offline yield and offline defect rate in 3-5 hour increments. Optionally, offline EL testing generates the overall offline yield and offline defect rate of the solar cells in 4-hour increments.

[0074] The principle of this embodiment is as follows: First, the battery cell manufacturing workshop can be divided into four smaller workshops based on a grid: texturing workshop 1, ultra-clean workshop 2, coating workshop 3, and printing workshop 4. Then, a quality analysis file is established for the battery cell manufacturing process, showing the correspondence between the contact points in the battery cell manufacturing process and their respective workshops. Based on the quality analysis file, quality control is further subdivided into texturing workshop 1, ultra-clean workshop 2, coating workshop 3, and printing workshop 4 according to the battery cell manufacturing process. Online and offline EL detection are set up in printing workshop 4. By classifying the abnormality types through online and offline EL detection of the battery cells, and quickly locating the abnormal workshop based on the quality analysis file, both online and offline EL test images can be quickly located and their abnormalities identified by referring to the quality analysis file of the battery cell manufacturing process, thus enabling processing and achieving the root cause of the abnormality.

[0075] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A quality control method for the manufacturing process of photovoltaic cells, characterized in that, Includes the following steps: The battery cell manufacturing workshop is divided into a grid-like structure, comprising a texturing workshop, an ultra-clean workshop, a coating workshop, and a printing workshop. Establish a quality analysis archive for the battery cell manufacturing process. The establishment of the quality analysis archive includes: collecting defect image types that appear during EL detection of battery cells during the manufacturing process, classifying each defect image type and corresponding it to each workshop for reference, so as to show the correspondence between the contact points in the battery cell manufacturing process and the workshop in which they are located. The defect image type reflects its correspondence with the contact between the battery cell and the equipment during the production process. The quality analysis files are divided into the texturing workshop, ultra-clean workshop, coating workshop and printing workshop according to the battery cell manufacturing process; Defect detection is performed on the battery cells and the defect types are classified. Based on the defect type, the cells are classified into one or more of the texturing workshop, cleanroom, coating workshop and printing workshop according to the quality analysis file. This allows for rapid identification of the abnormal workshop and quick adjustment.

2. The quality control method according to claim 1, characterized in that, Defect detection of solar cells includes online EL testing of solar cells using online EL testing equipment.

3. The quality control method according to claim 2, characterized in that, Online EL inspection is classified according to the type of defect in the tested solar cells as follows: belt marks, boat marks, microcracks, black spots, broken grids, scratches, pitted black spots, and suction cup marks.

4. The quality control method according to claim 3, characterized in that, Online EL inspection is performed hourly on all cells on the production line, generating over-detection rate, under-detection rate, online yield rate, and online defect rate for each production line, based on various defect types. For cells with abnormal indicators, adjustments are made to the workshop containing those cells according to quality standards, and the cells are then inspected online again after adjustment. The over-detection rate refers to the proportion of cells meeting quality standards among those with abnormal indicators; the under-detection rate refers to the proportion of cells with abnormal indicators among those meeting quality standards.

5. The quality control method according to claim 3, characterized in that, Defect detection of solar cells also includes offline EL testing using offline EL testing equipment. Offline EL testing involves packaging and testing defective solar cells that were detected by online EL testing.

6. The quality control method according to claim 5, characterized in that, Offline EL inspection is classified according to the defect type of the detected battery cells as follows: black spots, black dots, over-etching, scratches, ultra-clean suction cup marks, blackening of the top teeth, graphite boat marks, suction pen marks, coating equipment suction cup marks, broken grids, overprinting marks, and support leg marks.

7. The quality control method according to claim 6, characterized in that, Offline EL testing is conducted every 3-5 hours, testing defective products detected by online EL testing to generate the overall offline yield and offline defect rate.

8. The quality control method according to claim 5, characterized in that, Online and offline EL devices are shared within a local area network to enable quick viewing of the EL image type of defective battery cells.

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