Damage depth detection method, solar cell and production line thereof

Abstract

The application relates to a damage depth detection method, a solar cell and a production line thereof, and relates to the technical field of solar cells. The damage depth detection method comprises the following steps: obtaining a target damage depth calibration model matched with a to-be-detected cell piece intermediate body; obtaining a detection resistivity of a laser irradiation area of the to-be-detected cell piece intermediate body and a detection resistivity of a non-laser irradiation area of the to-be-detected cell piece intermediate body; and determining a detection damage depth of the laser irradiation area according to the detection resistivity of the laser irradiation area, the detection resistivity of the non-laser irradiation area and the target damage depth calibration model. According to the application, the detection resistivity of the to-be-detected cell piece intermediate body is detected online, the detection resistivity and the target damage depth calibration model are used to detect the laser damage depth of the to-be-detected cell piece intermediate body, and the problems of sample destructiveness, slow detection speed and high equipment cost in the existing detection technology are solved, thereby meeting the demand of damage depth detection for cell piece mass production.

Description

Technical Field

[0001] This application relates to the field of solar cell technology, and in particular to a method for detecting damage depth, a solar cell, and a production line thereof. Background Technology

[0002] Damage depth directly reflects the precision of laser energy control. If the damage depth is too shallow, cutting / drilling will fail; if it is too deep, energy will be wasted and material damage will be exacerbated. For example, the subsurface damage layer formed inside a silicon wafer during laser processing significantly weakens the material's mechanical strength, causing the thin wafer to fracture in subsequent processes. Therefore, by quantitatively detecting the damage depth, the irradiation parameters of the laser equipment (pulse width, energy density, focus position) can be reversely calibrated to compress the damage depth within a safe threshold, achieving efficient and non-destructive processing.

[0003] Currently, laser damage depth is mainly detected through spectral detection or morphological cross-sectional detection techniques. However, existing damage depth detection methods have the problem of damaging the sample being tested. Summary of the Invention

[0004] Therefore, it is necessary to provide a method for detecting damage depth without damaging the sample under test, as well as a solar cell and its production line, to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for detecting damage depth, including:

[0006] A target damage depth calibration model matching the intermediate of the solar cell under test is obtained; the target damage depth calibration model characterizes the correlation between the damage depth of the intermediate of the solar cell and the resistivity of the damaged region;

[0007] The resistivity of the laser-irradiated region of the intermediate body of the battery cell under test and the resistivity of the non-laser-irradiated region of the intermediate body of the battery cell under test are obtained.

[0008] The detection damage depth of the laser-irradiated area is determined based on the detected resistivity of the laser-irradiated area, the detected resistivity of the non-laser-irradiated area, and the target damage depth calibration model.

[0009] In one embodiment, the damage depth detection method further includes:

[0010] The detected damage depth of the laser irradiation area is fed back to the control module; the control module compares the difference between the detected damage depth of the laser irradiation area and the set damage depth, and adjusts the irradiation parameters of the laser device according to the difference between the detected damage depth of the laser irradiation area and the set damage depth.

[0011] In one embodiment, obtaining the target damage depth calibration model that matches the intermediate of the battery cell under test includes:

[0012] Based on the intermediate information of the intermediate solar cell to be tested, a target damage depth calibration model matching the intermediate solar cell to be tested is selected from multiple damage depth calibration models; the intermediate information of the intermediate solar cell includes at least one of the silicon wafer type, doping state, and doping concentration corresponding to the intermediate solar cell.

[0013] The step of selecting a target damage depth calibration model that matches the intermediate battery cell under test from multiple damage depth calibration models based on the intermediate battery cell information includes:

[0014] From the multiple damage depth calibration models, select multiple first candidate damage depth calibration models that match the silicon wafer type of the intermediate cell to be tested;

[0015] From multiple first candidate damage depth calibration models, a second candidate damage depth calibration model that matches the doping state of the intermediate of the cell under test is selected.

[0016] From multiple second candidate damage depth calibration models, a target damage depth calibration model that matches the doping concentration of the intermediate of the cell under test is selected.

[0017] In one embodiment, determining the detected damage depth of the laser-irradiated area based on the detected resistivity of the laser-irradiated area, the detected resistivity of the non-laser-irradiated area, and the target damage depth calibration model includes:

[0018] By comparing the detected resistivity of the laser-irradiated area with that of the non-laser-irradiated area, the difference in detected resistivity between the laser-irradiated area and the non-laser-irradiated area is obtained.

[0019] The detected resistivity difference and the detected resistivity of the non-laser irradiated area are substituted into the target damage depth calibration model to calculate the detected damage depth of the laser irradiated area.

[0020] In one embodiment, obtaining the resistivity of the laser-irradiated region of the intermediate cell under test includes:

[0021] The resistivity of the laser-irradiated region of the intermediate cell under test and the ambient temperature of the intermediate cell under test are obtained.

[0022] The measured resistivity is corrected based on the ambient temperature to obtain the corrected resistivity; the corrected resistivity is then used as the detected resistivity of the laser irradiation area.

[0023] In one embodiment, the step of correcting the measured resistivity based on the ambient temperature to obtain the corrected resistivity further includes:

[0024] Determine the difference between the ambient temperature and the calibration temperature;

[0025] The measured resistivity is corrected based on the difference between the ambient temperature and the calibration temperature, and the resistivity temperature coefficient of the intermediate of the battery cell under test, to obtain the corrected resistivity.

[0026] In one embodiment, the damage depth detection method further includes:

[0027] The test cell intermediate is irradiated by the laser device to form a test area on the surface of the test cell intermediate;

[0028] The resistivity of the test region, the depth of the test damage in the test region, and the resistivity of the non-test region of the intermediate test cell are obtained.

[0029] A damage depth calibration model is established based on the test resistivity of the test area of ​​the intermediate test cell, the test damage depth of the test area, and the resistivity of the non-test area.

[0030] In one embodiment, establishing the damage depth calibration model based on the test resistivity and test damage depth of the test battery cell intermediate includes:

[0031] The difference in resistivity between the test area and the non-test area of ​​the intermediate test cell is obtained based on the test resistivity of the test area and the resistivity of the non-test area.

[0032] By fitting the test resistivity difference of the test area and the test damage depth of the test area, a first fitting parameter and a second fitting parameter are obtained.

[0033] The damage depth calibration model is established based on the first fitting parameter, the second fitting parameter, and the resistivity of the non-test area.

[0034] Secondly, this application also provides a solar cell, including a cell intermediate;

[0035] The intermediate battery cell is irradiated by a laser device. During the irradiation of the intermediate battery cell by the laser device, the damage depth of the laser-irradiated area of ​​the intermediate battery cell is detected by the damage depth detection method as described in any one of the first aspects. The detected damage depth of the laser-irradiated area of ​​the intermediate battery cell is fed back to the control module. The control module adjusts the irradiation parameters of the laser device according to the detected damage depth.

[0036] Thirdly, this application also provides a solar cell production line, comprising:

[0037] Battery cell intermediate conveyor belt;

[0038] A laser irradiation module is fixed above the transport belt of the intermediate battery cell;

[0039] A resistivity detection module is integrated downstream of the laser irradiation module along the transport direction of the intermediate conveyor belt of the battery cell.

[0040] The processing module is integrated into the intermediate conveyor belt of the battery cell. The processing module is data-connected to the resistivity detection module. The processing module has a built-in damage depth calibration module that stores a target damage depth calibration model. The processing module is control-connected to the laser irradiation module.

[0041] In one embodiment, the resistivity detection module includes:

[0042] Detection probe array;

[0043] A support mechanism, wherein the detection probe array is fixed to the support mechanism;

[0044] A driving mechanism is connected to the supporting mechanism, and the driving mechanism drives the supporting mechanism to move in order to adjust the relative position of the detection probe array and the battery cell intermediate conveyor belt.

[0045] In one embodiment, the resistivity detection module further includes a temperature detection and compensation unit, integrated on the support structure.

[0046] In one embodiment, it includes: a control module, which is data-connected to the processing module and control-connected to the laser irradiation module.

[0047] Fourthly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method steps provided in the first aspect.

[0048] Fifthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method steps provided in the first aspect.

[0049] Sixthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method steps provided in the first aspect.

[0050] The aforementioned damage depth detection method, solar cell, and its production line obtain a target damage depth calibration model matching the intermediate of the solar cell under test, acquire the detected resistivity of the laser-irradiated area and the detected resistivity of the non-laser-irradiated area of ​​the intermediate of the solar cell under test, and determine the detected damage depth of the laser-irradiated area based on the detected resistivity of the laser-irradiated area, the detected resistivity of the non-laser-irradiated area, and the target damage depth calibration model. The target damage depth calibration model characterizes the correlation between the damage depth of the intermediate of the solar cell and the resistivity of the damaged area. This application embodiment detects the laser damage depth of the intermediate of the solar cell under test by online detection of the detected resistivity and by using the detected resistivity and the target damage depth calibration model. This solves the problems of sample destructiveness, slow detection speed, and high equipment cost in existing detection technologies, and meets the needs of mass production of solar cells for damage depth detection. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating a damage depth detection method in one embodiment;

[0053] Figure 2 This is a flowchart illustrating a method for establishing a damage depth calibration model in one embodiment;

[0054] Figure 3 This is a schematic diagram of the damage depth in one embodiment;

[0055] Figure 4 This is a schematic diagram of the damage depth in another embodiment;

[0056] Figure 5 This is a schematic diagram of a damage depth calibration model in one embodiment;

[0057] Figure 6This is a structural block diagram of a damage depth detection device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0060] Currently, damage depth detection is mainly achieved using scanning electron microscopy (SEM), optical profilometry / confocal microscopy, photoluminescence, or spectroscopy (such as Raman spectroscopy). However, SEM primarily relies on the morphology of the cut surface to determine damage depth, making it a destructive detection method. It is also slow, requires complex sample preparation, expensive equipment, high maintenance costs, and is difficult to mass-produce. Optical profilometry / confocal microscopy utilizes optical interference or confocal principles to scan the surface morphology of the damaged area and calculate the damage depth. However, it is susceptible to noise interference, has a slow detection speed, is difficult to integrate with high-speed production lines, and cannot detect subsurface defects (such as lattice damage). Photoluminescence or spectroscopy (such as Raman spectroscopy) uses lasers to excite the silicon wafer's emission or vibration modes and analyzes the signal changes caused by damage to determine the damage depth. However, it suffers from complex equipment, slow speed, sensitivity to surface contamination, and poor anti-interference capabilities.

[0061] In one exemplary embodiment, such as Figure 1 As shown, a damage depth detection method is provided, including the following steps S101 to S103. Wherein:

[0062] S101, Obtain the target damage depth calibration model that matches the intermediate of the solar cell under test; the target damage depth calibration model characterizes the correlation between the damage depth of the intermediate of the solar cell and the resistivity of the damaged area.

[0063] Due to the introduction of deep-level traps by lattice defects, the carrier concentration (b) and mobility (μ) decrease, leading to a decrease in resistivity ( The damage depth was observed using a transmission electron microscope (TEM) to increase the resistivity, and the resistivity was measured simultaneously. A logarithmic relationship (R²>0.95) was obtained through fitting. It can be seen that there is a strong correlation between resistivity and damage depth. Therefore, by obtaining a target damage depth calibration model that identifies the correlation between resistivity and the damage depth and resistivity of the damaged region in the intermediate cell of the solar cell, the damage depth can be detected.

[0064] In this embodiment, a target damage depth calibration model matching the intermediate of the cell under test can be determined based on the intermediate information and mapping relationship table of the intermediate of the cell under test. The mapping relationship table characterizes the relationship between different intermediate information and damage depth calibration models. The intermediate information of the cell includes the silicon wafer type, doping state and doping concentration corresponding to the intermediate of the cell.

[0065] In one possible implementation, the intermediate information of the battery cell to be tested is input into a preset matching model, and the target damage depth calibration model that matches the intermediate of the battery cell to be tested is output using the matching model.

[0066] S102, obtain the detection resistivity of the laser-irradiated area of ​​the intermediate body of the battery cell under test and the detection resistivity of the non-laser-irradiated area of ​​the intermediate body of the battery cell under test.

[0067] The intermediate cell under test in this embodiment can be an intermediate cell for forming a solar cell. Optionally, the intermediate cell under test can be a texturized silicon wafer, a silicon wafer after high-temperature phosphorus diffusion, a silicon wafer after removing the phosphorosilicon glass layer generated during diffusion, and a silicon wafer after coating the surface of the silicon wafer. This embodiment does not limit the type of solar cell; the solar cell can be a tunnel oxide passivated contact (TOPCON) cell or an interdigitated back contact (IBC) cell, etc.

[0068] The laser irradiation area of ​​the intermediate cell under test can be: the laser irradiation area generated by the laser equipment cutting the intermediate cell under test; marking information such as QR codes and batch numbers on the surface of the intermediate cell under test for tracing the production process; after ion implantation, the intermediate cell under test needs to be annealed to activate the doping elements and repair lattice damage; and etching micron-level trenches on the surface of the intermediate cell under test for isolating devices or forming interconnect structures.

[0069] It is understood that the damage depth detection method of this embodiment can be performed at any node after laser treatment of the silicon wafer during the fabrication of solar cells. For example, laser heating can be used to heat a local area of ​​the surface of a texturized silicon wafer, causing doping elements such as phosphorus to diffuse and form highly doped regions (reducing contact resistance) and low-doped regions (reducing light absorption), thereby improving the cell conversion efficiency. The damage depth detection method of this embodiment can be performed on the silicon wafer after a tunneling oxide layer is formed on the surface of the silicon wafer, or after a passivation layer is formed on the surface of the silicon wafer and the passivation layer is etched to form grid trenches, the damage depth detection method of this embodiment can be performed on the silicon wafer.

[0070] In this embodiment, within a preset time after the laser equipment operates, a non-contact detection probe is triggered to measure the resistivity of the laser-irradiated area of ​​the intermediate cell under test. The measured resistivity of the laser-irradiated area is used as the detected resistivity of the laser-irradiated area. For example, the laser equipment and the non-contact detection probe can be triggered synchronously. The non-contact detection probe completes the detection within 10ms of the laser equipment irradiating the intermediate cell, avoiding the natural oxidation of the damaged surface of the intermediate cell to form an oxide layer, thereby preventing the oxide layer from reducing the electrical performance of the solar cell. Optionally, the detection probe can be a high-frequency eddy current probe (high-frequency excitation, 1MHz-10MHz), a microwave resonant probe, a terahertz time-domain spectroscopy, etc. For example, using a high-frequency eddy current probe array (the spacing between the eddy current probes can be 50μm) to non-contactly scan the laser-irradiated area of ​​the intermediate cell under test and obtain the measured resistivity of the laser-irradiated area can avoid damage to the intermediate cell under test. The high-frequency eddy current can penetrate the oxide layer on the surface of the intermediate cell and detect subsurface defects. For example, to match the speed of the production line conveyor belt, the scanning speed of the high-frequency eddy current probe array is greater than or equal to 100mm / s. In this embodiment, the scanning speed of the high-frequency eddy current probe array is equal to the transmission speed of the production line conveyor belt.

[0071] Optionally, the above-mentioned detection probe can be applied to scenarios requiring high precision / transparent film detection.

[0072] In one possible implementation, the ambient temperature of the intermediate cell under test can be monitored in real time using a temperature sensor. The measured resistivity of the laser-irradiated area can be corrected using the ambient temperature to obtain the corrected resistivity, which can then be used as the detected resistivity of the laser-irradiated area.

[0073] The resistivity of the non-laser irradiated area of ​​the intermediate cell under test can be obtained by detection probe, or it can be obtained by routine offline sampling verification of the same type of intermediate cell before laser irradiation.

[0074] S103. Based on the detected resistivity of the laser-irradiated area, the detected resistivity of the non-laser-irradiated area, and the target damage depth calibration model, determine the detected damage depth of the laser-irradiated area.

[0075] In this embodiment, the detected resistivity of the laser-irradiated area and the non-laser-irradiated area can be compared to obtain the difference in detected resistivity between the laser-irradiated area and the non-laser-irradiated area. The difference in detected resistivity and the detected resistivity of the non-laser-irradiated area are then input into the target damage depth calibration model to obtain the detected damage depth of the laser-irradiated area.

[0076] In one possible implementation, it can be assumed that the detected resistivity in the non-laser-irradiated area corresponds to a known, relatively shallow damage depth, which typically originates from the phosphorus diffusion and / or phosphosilicate glass etching process in the preceding steps and is a relatively fixed and small value. Based on the detected resistivity difference and the target damage depth calibration model, the additional damage depth corresponding to the detected resistivity difference is determined. Then, the detected damage depth in the laser-irradiated area is determined by summing the damage depth corresponding to the detected resistivity in the non-laser-irradiated area and the additional damage depth.

[0077] Table 1 shows the detected damage depth obtained based on the embodiments of this application and the standard damage depth obtained based on scanning electron microscopy (SEM). It can be seen that the error range between the detected damage depth and the standard damage depth obtained by this application based on the detection resistivity of the laser irradiated area and the target damage depth calibration model is very small, and it can be applied to the detection of damage depth on the actual production line.

[0078] Table 1. Correspondence between Standard Damage Depth and Detected Damage Depth

[0079]

[0080] like Figure 2 and Figure 3 As shown, SEM is used to detect the damage depth in the laser-irradiated area. After laser irradiation, wet cleaning and etching are required. Then, SEM is used to measure the height difference between the laser-irradiated area and the non-laser-irradiated area to determine the standard damage depth. Figure 2 and Figure 3 The areas with lower relative heights are those wet-cleaned after laser treatment, while the areas with higher relative heights are non-laser-irradiated areas. The standard damage depths in the two images are 5.42 μm and 3.55 μm, respectively. The error range between the detected damage depth obtained through the embodiments of this application and the standard damage depth obtained by SEM detection is very small.

[0081] In the aforementioned damage depth detection method, a target damage depth calibration model matching the intermediate of the solar cell under test is obtained. The resistivity of the laser-irradiated area and the resistivity of the non-laser-irradiated area of ​​the intermediate are also obtained. Based on the resistivity of the laser-irradiated area, the resistivity of the non-laser-irradiated area, and the target damage depth calibration model, the detection damage depth of the laser-irradiated area is determined. The target damage depth calibration model characterizes the correlation between the damage depth of the intermediate and the resistivity of the damaged area. This embodiment of the application detects the laser damage depth of the intermediate by online detection of its resistivity and by using the resistivity and the target damage depth calibration model. This solves the problems of sample destructiveness, slow detection speed, and high equipment cost in existing detection technologies, meeting the needs of mass production of solar cells for damage depth detection.

[0082] In some embodiments, the damage depth detection method further includes: feeding back the detected damage depth of the laser irradiation area to the control module; the control module compares the difference between the detected damage depth of the laser irradiation area and the set damage depth, and adjusts the irradiation parameters of the laser device according to the difference between the detected damage depth of the laser irradiation area and the set damage depth.

[0083] In this embodiment, the damage depth is set to 0.5 μm, and the initial irradiation parameters of the laser device are set to a wavelength of 532 nm, a power of 12 W, a scanning speed of 100 mm / s, and a pulse frequency of 50 kHz. The laser device irradiates the first intermediate solar cell under test based on the initial irradiation parameters, obtaining a detected damage depth of 0.3 μm, which is then fed back to the control module. The control module adjusts the initial irradiation parameters based on the difference between the detected damage depth and the set damage depth, increasing the power to 15 W and decreasing the scanning speed to 80 mm / s (extending the single-point irradiation time). The adjusted irradiation parameters are then used to irradiate the second intermediate solar cell under test, obtaining a detected damage depth of 0.65 μm. If the detected damage depth and the set damage depth are inconsistent, further adjustments are made.

[0084] In this embodiment, the detected damage depth of the laser irradiation area is fed back to the control module. The control module compares the difference between the detected damage depth of the laser irradiation area and the set damage depth, and adjusts the irradiation parameters of the laser equipment according to the difference between the detected damage depth of the laser irradiation area and the set damage depth. By feeding back the detected damage depth, the irradiation parameters of the laser equipment are continuously adjusted to achieve fully automatic continuous online detection, thereby improving the monitoring of laser process quality.

[0085] In some embodiments, obtaining a target damage depth calibration model that matches the intermediate of the solar cell under test includes: selecting a target damage depth calibration model that matches the intermediate of the solar cell under test from multiple damage depth calibration models based on the intermediate information of the intermediate of the solar cell under test; the intermediate information of the solar cell includes at least one of the silicon wafer type, doping state, and doping concentration corresponding to the intermediate of the solar cell.

[0086] Multiple damage depth calibration models can include damage depth calibration models obtained based on logarithmic models, which can be represented as follows: Where d is the depth of the detected damage. To detect the resistivity difference, Here, represents the resistivity detected in the non-laser irradiated region, and k and C are parameters obtained from simulations of the test damage depth based on the intermediate substrate of the test cell. It is assumed that the intermediate substrate of the test cell is typical photovoltaic crystalline silicon (…). ~1Ω·cm), under nanosecond laser processing conditions, the variation range of the tested damage depth is typically 0.5μm-5μm. / It could increase from 1.1 (a 10% increase) to 10 (a 1000% increase). Substitute this into the model. Therefore, it can be deduced that the value of k typically ranges from 0.1 to 2.0. When / When C = 1 (i.e., no change), the theoretical damage depth should be 0, meaning the C-theoretical value should be 0. However, in actual calibration, due to factors such as measurement noise and system sensitivity threshold, ... / It may be slightly greater than 1, which makes C not necessarily 0. C compensates for the systematic bias of the damage depth calibration model when the damage depth is very small, ensuring that the overall error between the predicted value and the true value of the damage depth calibration model is minimized throughout the entire calibration data range. It is generally on the same order of magnitude as the minimum measurable depth, and is taken as 0μm±0.5μm.

[0087] Alternatively, multiple damage depth calibration models can also include damage depth calibration models based on power function models. Where d is the depth of the detected damage. To detect the resistivity difference, The resistivity is measured in the non-laser irradiated area.

[0088] Damage depth calibration model It is applicable to the case where the density and depth of damage defects show a non-linear gradient distribution. The parameter C (usually 0 < C < 1) in this damage depth calibration model reflects the saturation effect of the resistivity change with the increase of damage depth. When the damage depth is very large, the influence of newly added defects on the resistivity change near the surface will gradually decrease, and it is applicable to the deeper damage area caused by medium-energy lasers. It can also include a damage calibration model obtained from finite element simulation. In a multi-physics simulation software (such as COMSOL), input the irradiation parameters (energy, pulse width) of the laser device, material parameters (thermal conductivity, absorption coefficient, carrier lifetime, etc.), and through simulation calculation, obtain the temperature field, melting area, and defect distribution, and then derive the measured resistivity. Finally, couple and calculate the resistivity obtained from the simulation with the detection signal (such as the eddy current field response), and inversely deduce the damage depth, so as to obtain the target damage depth calibration model, which is applicable to dealing with extremely complex damage morphologies; the damage depth calibration model obtained by learning based on a learning model extracts the characteristic information of the measured resistivity (such as maximum value, average value, gradient, distribution width, etc.) from the measured resistivity, and inputs the characteristic information and the measured damage depth into the learning model. The learning model can automatically learn the non-linear mapping relationship therein without constructing complex physical formulas, and is applicable to the case where there is sufficient data, and the damage modes are diverse and difficult to calculate with simple analytical formulas.

[0089] Optionally, the type of silicon wafer can be single-crystalline silicon, polycrystalline silicon, or amorphous silicon; the doping state of polycrystalline silicon or amorphous silicon can be doped with doping elements or not doped with doping elements, etc.; the doping concentration of the doping elements can be 10 15 cm -3 、10 16 cm -3 and so on. It can be understood that in this embodiment, the doping elements and their doping concentrations are not specifically limited, and the doping elements and their doping concentrations do not constitute a limitation to this embodiment. The doping elements can be phosphorus, boron, antimony, etc., and the doping concentration of the doping elements can be flexibly selected according to the design requirements of the solar cell.

[0090] Optionally, the information of the solar cell intermediate can also include thickness, size, curvature / warpage, surface roughness, the doping concentration of the doping elements in the solar cell intermediate, etc. Therefore, the information of the solar cell intermediate involved in the embodiments of the present application is only an example, and is not limited to the examples provided in the embodiments of the present application. Therefore, in the case of different information of the solar cell intermediate, the target damage depth calibration model matched by the待测 solar cell intermediate is different.

[0091] In the embodiments of the present application, the information of the solar cell intermediate is input into a preset screening model, and the preset screening model screens a target damage depth calibration model that matches the待测 solar cell intermediate from multiple damage depth calibration models.

[0092] Specifically, based on the information of the intermediate battery cell under test, a target damage depth calibration model matching the intermediate battery cell under test is selected from multiple damage depth calibration models. This may include:

[0093] From multiple damage depth calibration models, several first candidate damage depth calibration models matching the silicon wafer type of the intermediate cell to be tested are selected; from multiple first candidate damage depth calibration models, second candidate damage depth calibration models matching the doping state of the intermediate cell to be tested are selected; from multiple second candidate damage depth calibration models, a target damage depth calibration model matching the doping concentration of the intermediate cell to be tested is selected.

[0094] In this embodiment, when the information of the intermediate solar cell under test includes silicon wafer type, doping state, and doping concentration, a target damage depth calibration model matching the intermediate solar cell under test is sequentially selected from multiple damage depth calibration models using silicon wafer type, doping state, and doping concentration. This embodiment improves the accuracy of target damage depth calibration model acquisition by combining multiple pieces of information from the intermediate solar cell information, and the method for acquiring the target damage depth calibration model is simple, thus improving the efficiency of target damage depth calibration model acquisition.

[0095] In some embodiments, the detection depth of the laser-irradiated area is determined based on the detected resistivity of the laser-irradiated area, the detected resistivity of the non-laser-irradiated area, and a target damage depth calibration model, including:

[0096] By comparing the detected resistivity of the laser-irradiated area with that of the non-laser-irradiated area, the difference in detected resistivity between the laser-irradiated area and the non-laser-irradiated area is obtained. The difference in detected resistivity and the detected resistivity of the non-laser-irradiated area are then substituted into the target damage depth calibration model to calculate the detected damage depth of the laser-irradiated area.

[0097] In this embodiment, the target damage depth calibration model can be expressed as described above. .

[0098] In this embodiment, the resistivity of the laser-irradiated area is compared with that of the non-laser-irradiated area to obtain the difference in resistivity between the laser-irradiated area and the non-laser-irradiated area. The difference in resistivity and the resistivity of the non-laser-irradiated area are substituted into the target damage depth calibration model to calculate the damage depth of the laser-irradiated area. The damage depth of the laser-irradiated area can be obtained without damaging the intermediate body of the battery cell under test, which improves the detection efficiency of damage depth and meets the mass production requirements of the production line.

[0099] In some embodiments, obtaining the resistivity of the laser-irradiated region of the intermediate cell under test includes:

[0100] The resistivity of the laser-irradiated area of ​​the intermediate of the solar cell under test and the ambient temperature of the intermediate are obtained; the measured resistivity is corrected according to the ambient temperature to obtain the corrected resistivity; the corrected resistivity is used as the detection resistivity of the laser-irradiated area.

[0101] Since silicon has a significant negative temperature coefficient of resistivity, its resistivity decreases as temperature increases. Therefore, it is necessary to correct the measured resistivity based on the ambient temperature to obtain the corrected resistivity.

[0102] In this embodiment, the measured resistivity is corrected based on the ambient temperature to obtain the corrected resistivity. , can be adopted The calculation is performed, where T is the ambient temperature, T0 is the calibration temperature (25℃), and n is the temperature index, the value of which is determined by the relationship between the mobility of charge carriers (electrons or holes) in the silicon material of the intermediate cell to be tested and the temperature. For example, n is about 2.4 ~ 2.6 for N-type silicon and about 2.2 ~ 2.4 for P-type silicon.

[0103] Furthermore, the measured resistivity is corrected based on the ambient temperature to obtain the corrected resistivity, which also includes:

[0104] Determine the difference between the ambient temperature and the calibration temperature; based on the difference between the ambient temperature and the calibration temperature and the resistivity temperature coefficient of the intermediate of the battery cell to be tested, correct the measured resistivity to obtain the corrected resistivity.

[0105] In the embodiments of this application, according to The measured resistivity is corrected to obtain the corrected resistivity. Where a is the temperature coefficient of resistivity of the intermediate cell under test, and T0 is the calibration temperature (25℃). T represents the measured resistivity of the intermediate cell of the battery under test, and T represents the ambient temperature.

[0106] In this embodiment, the measured resistivity of the laser-irradiated area of ​​the intermediate of the battery cell under test and the ambient temperature of the intermediate are obtained, and the difference between the ambient temperature and the calibration temperature is determined. Based on the difference between the ambient temperature and the calibration temperature and the resistivity temperature coefficient of the intermediate of the battery cell under test, the measured resistivity is corrected to obtain the corrected resistivity. In this embodiment, the measured resistivity measured at different ambient temperatures is corrected to the same standard temperature through temperature compensation. This makes the detected resistivity truly reflect the electrical properties of the intermediate of the battery cell under test, further improving the accuracy and reliability of the detected resistivity.

[0107] Figure 4 This is a flowchart illustrating a method for establishing a damage depth calibration model in one embodiment, as shown below. Figure 4 As shown, it includes the following steps:

[0108] S201, a test area is formed on the surface of the intermediate battery cell by irradiating the intermediate battery cell with a laser device.

[0109] In this embodiment of the application, a test area can be formed on the surface of the intermediate body of the test cell by grooving, drilling, etc., using a laser device.

[0110] Optionally, a laser device irradiates the intermediate part of the test cell with a laser wavelength of 200nm-2500nm and a pulse width of 10ns-1ps, creating a test area on the surface of the intermediate part. For example, the laser device is set to a wavelength of 532nm, a pulse width of 20ns, and an energy density of 2J / cm² to irradiate the intermediate part of the test cell to form the test area.

[0111] Optionally, the test cell intermediate may include multiple intermediates, with a corresponding test area formed on each intermediate, or multiple test areas may be formed on one intermediate, with each test area corresponding to a different test damage depth.

[0112] S202, obtain the test resistivity of the test area, the test damage depth of the test area, and the resistivity of the non-test area of ​​the intermediate body of the test cell.

[0113] Optionally, the resistivity test and the resistivity of the non-test area can be achieved using the method described in S102 above.

[0114] In the embodiments of this application, the test damage depth of the test area can be detected by SEM, or by optical profilometer, confocal microscope, or by photoluminescence or spectroscopy (such as Raman).

[0115] S203. Based on the test resistivity of the test area of ​​the intermediate battery cell, the test damage depth of the test area, and the resistivity of the non-test area, a damage depth calibration model is established.

[0116] In this embodiment, the test resistivity and test damage depth can be fitted to obtain fitting coefficients. Based on the fitting coefficients and the resistivity of the non-test area, a damage depth calibration model is obtained. The damage depth calibration model characterizes the correlation between the resistivity and damage depth of the battery cell intermediate.

[0117] When establishing a damage depth calibration model, it is necessary to consider the film parameters of the intermediate body of the test cell, the irradiation parameters of the laser equipment, the detection parameters, etc. Different film parameters, irradiation parameters and detection parameters will correspond to different damage depth calibration models.

[0118] Film parameters may include film material, doping type (N-type / P-type), doping concentration, carrier lifetime, mobility, defect data of intermediates of the test cell (dislocations, grain boundaries, etc.), thickness of the natural oxide layer, and surface roughness.

[0119] The irradiation parameters of a laser device include at least the wavelength, pulse width, energy density / power, frequency, and scan rate. Wavelength determines the energy absorption depth (e.g., the absorption depth of 532nm on the surface of the intermediate layer of a tested solar cell is much smaller than that of 1064nm); pulse width (nanosecond / picosecond / femtosecond) determines the primary damage mechanism (whether thermal or non-thermal effects dominate), with femtosecond lasers potentially producing steeper defect density gradients; energy density / power directly determines the degree of damage. Frequency and scan rate affect the thermal accumulation effect and may alter the damage morphology.

[0120] Detection parameters can include detection frequency and probe size. Taking eddy current detection as an example, high-frequency eddy currents have a shallow skin effect and are sensitive to surface damage, while low-frequency eddy currents have a deep penetration depth and can detect deeper damage.

[0121] In another possible implementation, Figure 5 This is a schematic diagram of a damage depth calibration model in one embodiment, such as... Figure 5 As shown, a damage depth calibration model is established based on the tested resistivity and tested damage depth of the intermediate battery cell, including:

[0122] Based on the resistivity of the test area and the resistivity of the non-test area of ​​the intermediate battery cell, the resistivity difference between the test area and the non-test area is obtained; the resistivity difference of the test area and the test damage depth of the test area are fitted to obtain the first fitting parameter and the second fitting parameter; based on the first fitting parameter, the second fitting parameter and the resistivity of the non-test area, a damage depth calibration model is established.

[0123] In the embodiments of this application, such as Figure 5As shown, a first fitting parameter and a second fitting parameter are obtained by fitting the resistivity difference as the x-axis and the damage depth as the y-axis. Based on the first fitting parameter, the second fitting parameter, and the resistivity of the non-test area, a damage depth calibration model is established. For example, a test area is generated by irradiating the intermediate body of the test cell with a laser device wavelength of 532nm, a pulse width of 20ns, and an energy density of 2J / cm². In this embodiment, the test area of ​​the intermediate body of the test cell can be scanned. For example, an eddy current probe with a frequency of 5MHz and a scanning speed of 200mm / s can be used to scan the test area of ​​the intermediate body of the test cell to obtain the test resistivity. The damage depth of the test area is detected using SEM. The type of damage depth calibration model to be calibrated is determined according to the test scenario. For example, if the laser energy of the laser device is medium, the damage depth calibration model is determined to be based on a power function model. Polynomial fitting is used to fit the resistivity difference and the damage depth to obtain the damage depth calibration model.

[0124] If the test scenario involves testing an intrinsic solar cell intermediate, then the damage depth calibration model is established using the test resistivity of the test area, the test damage depth of the test area, and the resistivity of the non-test area. Where d is the depth of the detected damage (in μm). To detect the resistivity difference, The resistivity of the non-laser irradiated area is k=0.15, C=0.8μm.

[0125] Optionally, when fitting the test resistivity difference and the test damage depth of the test area, methods such as exponential decay model, polynomial fitting, and physical simulation (such as COMSOL multiphysics coupling) can be used. This embodiment does not impose specific restrictions on the fitting method.

[0126] In this embodiment, a test area is formed on the surface of a test cell intermediate by irradiating it with a laser device. The resistivity of the test area, the test damage depth of the test area, and the resistivity of the non-test area of ​​the test cell intermediate are obtained. Based on the resistivity of the test area and the resistivity of the non-test area, the difference in resistivity between the test area and the non-test area is obtained. The difference in resistivity and the test damage depth of the test area are fitted to obtain a first fitting parameter and a second fitting parameter. Based on the first fitting parameter, the second fitting parameter, and the resistivity of the non-test area, a damage depth calibration model is established, laying the foundation for subsequent determination of the detection damage depth of the intermediate cell under test based on the damage depth calibration model.

[0127] In some embodiments, a solar cell is provided, including a cell intermediate. The cell intermediate is irradiated by a laser device. During the irradiation of the cell intermediate by the laser device, the damage depth of the laser-irradiated area of ​​the cell intermediate is detected by the aforementioned damage depth detection method. The detected damage depth of the laser-irradiated area of ​​the cell intermediate is fed back to the control module, and the control module adjusts the irradiation parameters of the laser device according to the detected damage depth.

[0128] The aforementioned solar cells can be TOPCon cells, bifacial TOPCon cells, IBC cells, or cells formed by combining TOPCon and IBC technologies (TOPCon Back Contact, TBC), etc.

[0129] In some embodiments, a solar cell production line is provided, comprising: a cell intermediate conveyor belt; a laser irradiation module fixed above the cell intermediate conveyor belt; a resistivity detection module integrated downstream of the laser irradiation module along the conveying direction of the cell intermediate conveyor belt; a processing module integrated on the cell intermediate conveyor belt, the processing module being data-connected to the resistivity detection module, the processing module having a built-in damage depth calibration module storing a target damage depth calibration model, and the processing module being controllably connected to the laser irradiation module.

[0130] The intermediate solar cell under test is placed on a conveyor belt. A laser irradiation module is fixed above the conveyor belt. As the conveyor belt transports the intermediate solar cell under test to the area below the laser irradiation module, the laser irradiation module irradiates the intermediate solar cell under test, creating an irradiated area. A resistivity detection module is integrated downstream of the laser irradiation module. It detects the resistivity of the irradiated area within a preset time to obtain the measured resistivity.

[0131] The processing module is integrated into the conveyor belt of the intermediate battery cell. An embedded detection station is set up on the conveyor belt of the intermediate battery cell. The measured resistivity is used as the detection resistivity of the laser irradiated area. The difference in detection resistivity is obtained based on the detection resistivity and the detection resistivity of the non-laser irradiated area of ​​the intermediate battery cell to be tested. The difference in detection resistivity and the detection resistivity of the non-laser irradiated area are input into the target damage depth calibration model that matches the intermediate battery cell to be tested to obtain the detection damage depth, which meets the requirements of damage depth detection in the mass production of battery cells.

[0132] Optionally, the resistivity detection module may include a non-contact eddy current probe array and a signal conditioning circuit (the signal conditioning circuit may include an amplifier circuit, a filter circuit, an analog-to-digital converter circuit, etc.). The signal conditioning circuit converts the detected optical signal into an electrical signal and transmits it to the processing module.

[0133] Optionally, the resistivity detection module may include a non-contact eddy current probe array, a signal conditioning circuit, and a temperature sensor. The temperature sensor monitors the ambient temperature in real time and transmits the ambient temperature to the processing module. The processing module corrects the measured resistivity based on the ambient temperature to obtain the corrected resistivity.

[0134] Optionally, in complex noise environments / high dynamic range measurement scenarios, the processing module uses lock-in amplification technology and wavelet transform to reduce noise and extract features from the detected resistivity to ensure the accuracy of the detected resistivity data.

[0135] In one embodiment, the resistivity detection module includes a detection probe array, a support mechanism, and a drive mechanism; the detection probe array is fixed to the support mechanism; the drive mechanism is driven to move the support mechanism to adjust the relative position of the detection probe array and the battery cell intermediate transport belt.

[0136] The supporting mechanism can be a robotic arm, which carries a detection probe array. The driving mechanism drives the robotic arm to move, so as to adjust the relative position of the detection probe array and the intermediate conveyor belt of the battery cell.

[0137] In one embodiment, the resistivity detection module further includes a temperature detection and compensation unit, which is integrated on the support structure.

[0138] Optionally, the temperature detection and compensation unit may include a temperature sensor and a compensation unit. The temperature sensor is used to monitor the ambient temperature in real time; the compensation unit is used to correct the measured resistivity based on the ambient temperature to obtain the corrected resistivity and transmit the corrected resistivity to the processing module.

[0139] In one embodiment, the solar cell production line further includes a control module, which is connected to the processing module for data transfer and to the laser irradiation module for control.

[0140] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0141] Based on the same technical concept, this application also provides a damage depth detection device for implementing the damage depth detection method described above. The solution provided by this device is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more damage depth detection device embodiments provided below can be found in the limitations of the damage depth detection method described above, and will not be repeated here.

[0142] In one exemplary embodiment, such as Figure 6 As shown, this embodiment provides a damage depth detection device, which includes a first acquisition unit 11, a second acquisition unit 12, and a determination unit 13. Wherein:

[0143] The first acquisition unit 11 is used to acquire a target damage depth calibration model that matches the intermediate body of the battery cell under test; the target damage depth calibration model characterizes the correlation between the damage depth of the intermediate body of the battery cell and the resistivity of the damaged area.

[0144] The second acquisition unit 12 is used to acquire the detection resistivity of the laser-irradiated area of ​​the intermediate body of the battery cell under test and the detection resistivity of the non-laser-irradiated area of ​​the intermediate body of the battery cell under test.

[0145] The determination unit 13 is used to determine the detection damage depth of the laser-irradiated area based on the detection resistivity of the laser-irradiated area, the detection resistivity of the non-laser-irradiated area, and the target damage depth calibration model.

[0146] In one embodiment, the damage depth detection device further includes:

[0147] The adjustment unit is used to feed back the detected damage depth of the laser irradiation area to the control module; the control module compares the difference between the detected damage depth of the laser irradiation area and the set damage depth, and adjusts the irradiation parameters of the laser equipment according to the difference between the detected damage depth of the laser irradiation area and the set damage depth.

[0148] In one embodiment, the first acquisition unit 11 is used to select a target damage depth calibration model that matches the intermediate of ...

[0149] In one embodiment, the first acquisition unit 11 is configured to select a target damage depth calibration model that matches the intermediate battery cell under test from multiple damage depth calibration models based on the intermediate battery cell information, including:

[0150] From multiple damage depth calibration models, several first-candidate damage depth calibration models that match the silicon wafer type of the intermediate cell to be tested are selected.

[0151] From multiple first-candidate damage depth calibration models, a second-candidate damage depth calibration model that matches the doping state of the intermediate in the cell under test is selected.

[0152] From multiple second-candidate damage depth calibration models, a target damage depth calibration model that matches the doping concentration of the intermediate in the cell under test is selected.

[0153] In one embodiment, the determining unit 13 is used to compare the detection resistivity of the laser-irradiated area with the detection resistivity of the non-laser-irradiated area to obtain the difference in detection resistivity between the laser-irradiated area and the non-laser-irradiated area; the difference in detection resistivity and the detection resistivity of the non-laser-irradiated area are substituted into the target damage depth calibration model to calculate the detection damage depth of the laser-irradiated area.

[0154] In one embodiment, the second acquisition unit 12 is used to acquire the measured resistivity of the laser-irradiated area of ​​the intermediate of the battery cell under test and the ambient temperature of the intermediate of the battery cell under test; to correct the measured resistivity according to the ambient temperature to obtain the corrected resistivity; and to use the corrected resistivity as the detected resistivity of the laser-irradiated area.

[0155] In one embodiment, the second acquisition unit 12 is further configured to determine the difference between the ambient temperature and the calibration temperature; and to correct the measured resistivity based on the difference between the ambient temperature and the calibration temperature and the resistivity temperature coefficient of the intermediate of the battery cell to be tested, thereby obtaining the corrected resistivity.

[0156] In one embodiment, the damage depth detection device further includes:

[0157] The irradiation unit is used to irradiate the intermediate battery cell with a laser device to form a test area on the surface of the intermediate battery cell.

[0158] The third acquisition unit is used to acquire the test resistivity of the test area, the test damage depth of the test area, and the resistivity of the non-test area of ​​the intermediate body of the test cell.

[0159] A unit is established to create a damage depth calibration model based on the test resistivity of the test area of ​​the intermediate battery cell, the test damage depth of the test area, and the resistivity of the non-test area.

[0160] In one embodiment, a unit is configured to obtain the difference in resistivity between the test area and the non-test area based on the test resistivity of the test area and the resistivity of the non-test area of ​​the test cell intermediate; fit the difference in resistivity of the test area and the test damage depth of the test area to obtain a first fitting parameter and a second fitting parameter; and establish a damage depth calibration model based on the first fitting parameter, the second fitting parameter, and the resistivity of the non-test area.

[0161] Each unit in the aforementioned damage depth detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0162] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above-described damage depth detection method embodiments.

[0163] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described damage depth detection method embodiments.

[0164] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the above-described damage depth detection method embodiments.

[0165] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0166] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, computation-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0167] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0168] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for detecting damage depth, characterized in that, The method includes: A target damage depth calibration model matching the intermediate of the solar cell under test is obtained from multiple damage depth calibration models; the target damage depth calibration model characterizes the correlation between the damage depth of the intermediate of the solar cell and the resistivity of the damaged region; The resistivity of the laser-irradiated region of the intermediate body of the battery cell under test and the resistivity of the non-laser-irradiated region of the intermediate body of the battery cell under test are obtained. The detection damage depth of the laser-irradiated area is determined based on the detected resistivity of the laser-irradiated area, the detected resistivity of the non-laser-irradiated area, and the target damage depth calibration model. The resistivity of the test area formed by laser irradiation of the intermediate body of the test cell, the test damage depth of the test area, and the resistivity of the non-test area are obtained; based on the test resistivity and the resistivity of the non-test area, the test resistivity difference between the test area and the non-test area is obtained; the test resistivity difference and the test damage depth are fitted to obtain a first fitting parameter and a second fitting parameter; based on the first fitting parameter, the second fitting parameter, and the resistivity of the non-test area, a damage depth calibration model is established.

2. The damage depth detection method according to claim 1, characterized in that, The damage depth detection method further includes: The detected damage depth of the laser irradiation area is fed back to the control module; the control module compares the difference between the detected damage depth of the laser irradiation area and the set damage depth, and adjusts the irradiation parameters of the laser device according to the difference between the detected damage depth of the laser irradiation area and the set damage depth.

3. The damage depth detection method according to claim 1, characterized in that, The step of obtaining a target damage depth calibration model that matches the intermediate of the battery cell under test from multiple damage depth calibration models includes: Based on the intermediate information of the intermediate solar cell to be tested, a target damage depth calibration model matching the intermediate solar cell to be tested is selected from multiple damage depth calibration models; the intermediate information of the intermediate solar cell includes at least one of the silicon wafer type, doping state, and doping concentration corresponding to the intermediate solar cell.

4. The damage depth detection method according to claim 3, characterized in that, The step of selecting a target damage depth calibration model that matches the intermediate battery cell under test from multiple damage depth calibration models based on the intermediate battery cell information includes: From the multiple damage depth calibration models, select multiple first candidate damage depth calibration models that match the silicon wafer type of the intermediate cell to be tested; From multiple first candidate damage depth calibration models, a second candidate damage depth calibration model that matches the doping state of the intermediate of the cell under test is selected. From multiple second candidate damage depth calibration models, a target damage depth calibration model that matches the doping concentration of the intermediate of the cell under test is selected.

5. The damage depth detection method according to claim 1, characterized in that, The step of determining the detected damage depth of the laser-irradiated area based on the detected resistivity of the laser-irradiated area, the detected resistivity of the non-laser-irradiated area, and the target damage depth calibration model includes: By comparing the detected resistivity of the laser-irradiated area with that of the non-laser-irradiated area, the difference in detected resistivity between the laser-irradiated area and the non-laser-irradiated area is obtained. The detected resistivity difference and the detected resistivity of the non-laser irradiated area are substituted into the target damage depth calibration model to calculate the detected damage depth of the laser irradiated area.

6. The damage depth detection method according to claim 1, characterized in that, The process of obtaining the resistivity of the laser-irradiated region of the intermediate cell of the battery under test includes: The resistivity of the laser-irradiated region of the intermediate cell under test and the ambient temperature of the intermediate cell under test are obtained. The measured resistivity is corrected based on the ambient temperature to obtain the corrected resistivity; the corrected resistivity is then used as the detected resistivity of the laser irradiation area.

7. The damage depth detection method according to claim 6, characterized in that, The step of correcting the measured resistivity based on the ambient temperature to obtain the corrected resistivity further includes: Determine the difference between the ambient temperature and the calibration temperature; The measured resistivity is corrected based on the difference between the ambient temperature and the calibration temperature, and the resistivity temperature coefficient of the intermediate of the battery cell under test, to obtain the corrected resistivity.

8. A solar cell, characterized in that, Including battery cell intermediates; The intermediate battery cell is irradiated by a laser device. During the irradiation of the intermediate battery cell by the laser device, the damage depth of the laser-irradiated area of ​​the intermediate battery cell is detected by the damage depth detection method as described in any one of claims 1-7. The detected damage depth of the laser-irradiated area of ​​the intermediate battery cell is fed back to the control module. The control module adjusts the irradiation parameters of the laser device according to the detected damage depth.

9. A production line for solar cells, characterized in that, include: Battery cell intermediate conveyor belt; A laser irradiation module is fixed above the transport belt of the intermediate battery cell; A resistivity detection module is integrated downstream of the laser irradiation module along the transport direction of the intermediate conveyor belt of the battery cell. A processing module is integrated into the intermediate transport belt of the battery cell. The processing module is data-connected to the resistivity detection module. The processing module has a built-in damage depth calibration module that stores a target damage depth calibration model. The processing module is controllably connected to the laser irradiation module. The target damage depth calibration model is used to execute the damage depth detection method according to any one of claims 1-7.

10. The solar cell production line according to claim 9, characterized in that, The resistivity detection module includes: Detection probe array; A support mechanism, wherein the detection probe array is fixed to the support mechanism; A driving mechanism is connected to the supporting mechanism, and the driving mechanism drives the supporting mechanism to move in order to adjust the relative position of the detection probe array and the battery cell intermediate conveyor belt.

11. The solar cell production line according to claim 10, characterized in that, The resistivity detection module further includes: The temperature detection and compensation unit is integrated on the support structure.

12. The solar cell production line according to claim 9, characterized in that, include: The control module is connected to the processing module via data connection and to the laser irradiation module via control connection.

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