A method and system for adaptive optimization of passivated contact layer thickness for photovoltaic panels

Abstract

The application discloses a kind of photovoltaic panel passivation contact layer thickness self-adaptive optimization method and system, including S1.determine the thickness parameter space of photovoltaic cell passivation contact layer;S2.according to the thickness combination parameter, prepare corresponding photovoltaic cell sample in deposition equipment, and carry out electrical performance test and optical performance test to the sample.The photovoltaic panel passivation contact layer thickness self-adaptive optimization method and system, by generating multiple thickness combinations in thickness parameter space and obtaining corresponding electrical and optical performance data, establish the response relationship model between thickness parameter and cell performance index, and carry out comprehensive optimization solution under the constraint condition of parasitic absorption loss, to determine the optimal thickness combination of passivation contact layer, and convert the optimization result into process control parameter of deposition equipment for production control, so that thickness optimization result can be directly applied to actual production process, improve photovoltaic cell performance and production stability.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic technology, specifically to an adaptive optimization method and system for the thickness of the passivation contact layer in a photovoltaic panel. Background Technology

[0002] Passivated contact structures based on tunneling oxide layers and polycrystalline silicon layers are widely used in high-efficiency battery structures because they can significantly reduce carrier recombination and improve open-circuit voltage and battery efficiency. For example, in TOPCon battery structures, an ultrathin tunneling oxide layer is typically formed on the surface of a silicon substrate, and a polycrystalline silicon layer is deposited on it. Selective carrier transport is achieved through the tunneling effect, thereby effectively reducing interfacial recombination losses while ensuring good electrical contact. However, in actual manufacturing, the thickness parameter of the passivation contact layer has a significant impact on battery performance. For example, if the tunneling oxide layer is too thin, it may lead to an increase in leakage current, while if it is too thick, it will increase the tunneling impedance. If the polycrystalline silicon layer is too thick, it may cause an increase in parasitic absorption losses, while if it is too thin, it may affect the contact resistance and passivation effect.

[0003] In existing technologies, the thickness of the passivation contact layer is usually adjusted through empirical parameters or limited experiments. For example, within a certain thickness range, battery efficiency is tested with a small number of experimental samples, and the combination of thicknesses with better performance is selected.

[0004] For example, in the prior art, CN120076415A discloses a silicon-based heterojunction solar cell, its fabrication method, electrical equipment, and applications. The silicon-based heterojunction solar cell includes a silicon substrate, a first passivation layer, an N-type doped layer, and a first transparent electrode sequentially arranged along a first direction. The first direction is the thickness direction of the silicon substrate, pointing from the silicon substrate to the first transparent electrode. The N-type doped layer includes multiple sublayers sequentially arranged along the first direction: an oxygen-free seed layer, an oxygen-containing seed layer, an oxygen-containing host layer, and an oxygen-free contact layer. Through optimized design of the N-type doped layer, the conductivity of the N-type doped layer is not affected, and no additional carrier recombination is introduced. The overall process is simple, and the fabricated solar cell can improve current density while maintaining the fill factor, thus improving cell efficiency. This silicon-based heterojunction solar cell can utilize light energy efficiently in photovoltaic applications.

[0005] Because of the lack of a systematic parameter mechanism, it is difficult to fully reflect the coupling relationship between different thickness parameters and performance indicators such as battery efficiency, contact resistance and composite current density. When the thickness increases, the polycrystalline silicon layer may also generate parasitic light absorption, thereby reducing the photoelectric conversion efficiency of the battery. Traditional thickness optimization methods often fail to effectively constrain parasitic absorption losses. Summary of the Invention

[0006] The purpose of this invention is to provide an adaptive optimization method and system for the thickness of the passivation contact layer in a photovoltaic panel, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an adaptive optimization method for the thickness of the passivation contact layer of a photovoltaic panel, comprising the following steps: S1. Determine the thickness parameter space of the passivation contact layer of the photovoltaic cell, and generate multiple thickness combination parameters based on the experimental design method, wherein the thickness parameters include at least the tunneling oxide layer thickness parameter and the polycrystalline silicon passivation contact layer thickness parameter; S2. Prepare corresponding photovoltaic cell samples in a deposition equipment according to the thickness combination parameters, and perform electrical performance tests and optical performance tests on the samples to obtain corresponding performance test data; S3. Establish a response relationship model between the passivation contact layer thickness parameter and the battery performance index based on the performance test data; S4. Under the constraint of parasitic absorption loss, the thickness combination parameters are optimized according to the response relationship model to determine the optimal thickness combination of the passivation contact layer. S5. Generate process control parameters for the deposition equipment based on the optimal thickness combination, and use the process control parameters for production control of the photovoltaic panel passivation contact layer deposition process.

[0008] Preferably, in S2, the photovoltaic cell sample is subjected to electrical tests to obtain cell performance data, and the photovoltaic cell efficiency prediction value, contact resistance, and recombination current density are obtained based on the electrical test results. The photovoltaic cell efficiency prediction value is used to characterize the effect of thickness parameter changes on the cell's photoelectric conversion capability, the contact resistance is used to characterize the electrical contact characteristics between the passivation contact layer and the electrode, and the recombination current density is used to characterize the degree of carrier recombination loss, thereby providing an electrical performance evaluation basis for subsequent thickness optimization.

[0009] Preferably, in S3, by evaluating the model error and supplementing experimental points as needed, model distortion in local areas can be avoided, ensuring the effectiveness and reproducibility of subsequent optimization solutions. By fitting the thickness parameters of the experimental samples with the corresponding performance test data, a response relationship model between the thickness parameters and battery efficiency is established, the expression of which is: ,in This indicates the efficiency or predicted efficiency of a photovoltaic cell. Indicates the thickness of the tunneling oxide layer; Indicates the thickness of the polysilicon passivation contact layer; This function represents the response relationship between thickness parameters and battery efficiency.

[0010] Preferably, in step S4, the thickness combination parameters are optimized by constructing a comprehensive optimization objective function, which is: ,in: This represents the comprehensive optimization objective function; Indicates the efficiency of photovoltaic cells; This indicates the parasitic absorption loss generated by the passivation contact layer; Indicates contact resistance; Indicates the composite current density; , , , This represents the weighting coefficient corresponding to each performance indicator.

[0011] Preferably, the parasitic absorption loss The calculation expression is obtained based on the spectral absorption model. ,in This indicates that the passivated contact layer is at wavelength and thickness Absorption coefficient under the given conditions; Indicates the incident spectrum at wavelength Light intensity distribution at that location; Indicates the wavelength of light; This indicates the thickness of the passivation contact layer.

[0012] Preferably, in S1, the thickness combination parameters are generated using an experimental design method. By constructing an experimental sample matrix within the thickness parameter space, different thickness combinations can cover a preset parameter range, thereby obtaining the variation law between thickness parameters and performance indicators under a limited number of experiments.

[0013] Preferably, when optimizing the thickness combination parameters, constraints are set to limit the optimization range so that the determined optimal thickness combination simultaneously satisfies that the contact resistance is lower than a preset threshold, the composite current density is lower than a preset threshold, and the parasitic absorption loss is lower than a preset threshold.

[0014] Preferably, in step S5, the optimal thickness combination is converted into process control parameters of the deposition equipment, wherein the process control parameters include deposition time, deposition temperature, and reactive gas flow rate parameters, and the passivation contact layer thickness is precisely controlled by adjusting the process control parameters.

[0015] Preferably, new electrical and optical performance data are periodically acquired during the production process, and the response relationship model is updated based on the new performance data, thereby achieving dynamic optimization of the passivation contact layer thickness parameters. The response relationship model is obtained by fitting experimental data and is used to predict battery efficiency and parasitic absorption loss under different thickness combination parameters, so as to reduce the number of actual experiments and improve the efficiency of passivation contact layer thickness optimization.

[0016] An optimization system for implementing the above method steps includes a parameter generation module for determining the passivation contact layer thickness parameter space and generating thickness combination parameters; a data acquisition module for acquiring corresponding photovoltaic cell performance test data based on the thickness combination parameters; a model building module for establishing a response relationship model between the thickness parameters and cell performance indicators; an optimization calculation module for determining the optimal thickness combination of the passivation contact layer based on the response relationship model; and a process control module for generating process control parameters for the deposition equipment based on the optimal thickness combination and using them for production control.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: The adaptive optimization method and system for the passivation contact layer thickness of the photovoltaic panel generates multiple thickness combinations in the thickness parameter space and obtains the corresponding electrical and optical performance data, establishes a response relationship model between the thickness parameter and the battery performance index, and performs comprehensive optimization under the constraint of parasitic absorption loss, thereby determining the optimal thickness combination of the passivation contact layer, and converts the optimization results into process control parameters of the deposition equipment for production control, so that the thickness optimization results can be directly applied to the actual production process, improving the performance and production stability of photovoltaic cells, as shown in the following details.

[0018] 1. By generating thickness combinations using experimental design methods within the thickness parameter space, the main effects and interaction effects of the tunneling oxide layer thickness and the polycrystalline silicon passivation contact layer thickness can be covered with a limited number of experiments, thereby improving the systematicness and efficiency of thickness parameter exploration; 2. By establishing a response relationship model between thickness parameters and performance indicators such as battery efficiency, contact resistance, and composite current density, battery performance can be predicted even under thickness combinations that have not been actually fabricated, thereby reducing the number of experiments and improving the efficiency of thickness optimization. 3. By introducing parasitic absorption loss constraints during the optimization process and comprehensively optimizing multiple performance indicators such as cell efficiency, contact resistance, and composite current density, the determined thickness combination can balance electrical and optical performance. The optimal thickness combination is then converted into process control parameters for the deposition equipment, and the model is updated during production based on new performance data. This achieves adaptive optimization of thickness parameters, improving stability and applicability in mass production environments. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the process for organizing the present invention; Figure 2This is a schematic diagram of the specific process of S1 of the present invention; Figure 3 This is a schematic diagram of the S2 process of the present invention; Figure 4 This is a schematic diagram of the specific process of S3-5 of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figures 1-4 The present invention provides the following technical solution: The adaptive optimization method for the passivation contact layer thickness of photovoltaic panels includes the following steps: S1. Determine the thickness parameter space of the photovoltaic cell passivation contact layer, and generate multiple thickness combination parameters based on experimental design methods. These thickness parameters include at least the tunneling oxide layer thickness parameter and the polycrystalline silicon passivation contact layer thickness parameter. The thickness combination parameters are generated using experimental design methods, where an experimental sample matrix is ​​constructed within the thickness parameter space to ensure that different thickness combinations cover a preset parameter range. This allows for the acquisition of the variation law between thickness parameters and performance indicators under limited experimental conditions. The thickness parameter space is jointly defined by the target performance window and the process reachability boundary: the tunneling oxide layer thickness is denoted as... The thickness of the polysilicon passivation contact layer is denoted as ; , The upper and lower limits are given by the oxide layer formation process capability, such as thermal oxidation / chemical oxidation / plasma oxidation, and the polysilicon deposition process capability, such as LPCVD / PECVD, respectively, and are corrected by combining historical yield boundaries, such as leakage current, poor contact, and passivation failure boundaries. Experimental design methods generate a set of thickness combination parameters in the coverage thickness parameter space. These methods can employ orthogonal experimental design, full factorial experimental design, or central composite experimental design to achieve coverage with a finite number of samples. , The main effect and interaction effect.

[0022] To achieve the mapping from thickness combination parameters to equipment executable parameters, this embodiment establishes a thickness-process mapping table: for each thickness combination... The process settings for the corresponding oxide layer formation section and polysilicon deposition section are given, such as deposition time, temperature, gas flow rate, power, etc. The deposition / growth rate can be obtained by calibrating the equipment under steady-state conditions and used as the basis for subsequent conversion.

[0023] The core lies in transforming the problem of optimal thickness from empirical selection to controlled exploration within a parameter space. The thickness combinations generated by DOE can systematically cover... , The variation range of the thickness allows for the fitting of the influence trend of thickness on indicators such as efficiency, contact and composite with fewer samples, and provides a usable data foundation and traceable process input for optimization solutions.

[0024] S2. Based on the thickness combination parameters, prepare corresponding photovoltaic cell samples in the deposition equipment, and perform electrical and optical performance tests on the samples to obtain corresponding performance test data. The key is to convert the thickness combination into fitable data, and output a set of data for each thickness combination. In addition to optical characterization data, a thickness-performance dataset is formed. Thickness sampling and calibration are used to eliminate thickness deviations caused by equipment drift, so that model training is based on actual thickness rather than just process settings, thereby improving the reliability of subsequent response models and optimization solutions. In this step, photovoltaic cell samples are subjected to electrical tests to obtain cell performance data, and the photovoltaic cell efficiency prediction, contact resistance, and recombination current density are obtained based on the electrical test results. The photovoltaic cell efficiency prediction is used to characterize the impact of thickness parameter changes on the cell's photoelectric conversion capability, the contact resistance is used to characterize the electrical contact characteristics between the passivation contact layer and the electrode, and the recombination current density is used to characterize the degree of carrier recombination loss, thereby providing an electrical performance evaluation basis for subsequent thickness optimization.

[0025] In this embodiment, sample preparation is performed piece-by-piece or batch-by-batch according to the thickness combinations generated by the DOE, and key layer thicknesses are calibrated and sampled. , The actual thickness can be measured and calibrated on the sample using an ellipsometry (or cross-sectional measurement) to correct the mapping error between the "process setting value and the actual thickness". When the thickness deviation exceeds the preset tolerance, the deposition / growth rate calibration value is updated to ensure the consistency of subsequent data. Contact resistance is denoted as The preferred method is to extract the structure using TLM / CTLM testing; representative samples are selected from the same batch for measurement, and the measured values ​​are recorded. As a contact evaluation metric for this thickness combination, the composite current density is denoted as... The parameter used to characterize carrier recombination loss is preferably extracted by fitting the dark-state IV curve using a dual-diode model, or calculated from implicit parameters measured by Suns-Voc / QSSPC; the predicted photovoltaic cell efficiency is denoted as... It can be obtained from the sampled IV test, or when only near-line testing is possible, it can be calculated based on the implicit open-circuit voltage and short-circuit current proxy obtained from Suns-Voc, as well as the production line calibration coefficient, and is used to characterize the effect of thickness variation on photoelectric conversion capability.

[0026] Optical performance testing is used to obtain information related to parasitic absorption: preferably, reflectance / transmittance spectroscopy testing of the sample is performed, or spectral response characteristics related to the sensitive band of parasitic absorption are obtained for subsequent calculation of parasitic absorption loss or constraint determination.

[0027] S3. Based on performance test data, establish a response relationship model between the passivation contact layer thickness parameter and battery performance indicators. Essentially, this involves fitting discrete experimental points into a continuous and calculable performance surface. Within the parameter space covered by the DOE, the response surface model can reflect... , The main and interaction effects are analyzed to support performance prediction for thickness points that have not been actually fabricated. By evaluating model errors and supplementing experimental points as needed, model distortion in local regions can be avoided, ensuring the effectiveness and reproducibility of subsequent optimization solutions. By fitting the thickness parameters of experimental samples with corresponding performance test data, a response relationship model between thickness parameters and battery efficiency is established, the expression of which is: ,in This indicates the efficiency or predicted efficiency of a photovoltaic cell. Indicates the thickness of the tunneling oxide layer; Indicates the thickness of the polysilicon passivation contact layer; This function represents the response relationship between thickness parameters and battery efficiency.

[0028] It is preferable to use a response surface model (RSM) for fitting, such as a quadratic polynomial response surface: , The coefficients of the constant term; , These are the coefficients of the first-order terms; The coefficients of the interaction terms; , The coefficients are quadratic coefficients, which are obtained by fitting the DOE sample data.

[0029] Within the same model framework, separate models can also be established. , The response relationship with thickness parameters, for example , This is used for subsequent constraint judgment and comprehensive optimization.

[0030] Furthermore, the response relationship model is used to predict battery efficiency and parasitic absorption loss under different thickness combination parameters, thereby reducing the number of actual experiments and improving the efficiency of thickness optimization. The model prediction error can be evaluated by cross-validation or leaving a validation set, and the thickness region with error exceeding the threshold is taken as the key region for subsequent supplementary experiments.

[0031] S4. Under the constraint of parasitic absorption loss, the thickness combination parameters are optimized according to the response relationship model to determine the optimal thickness combination of the passivation contact layer. The optimization objective function is as follows: ,in: This represents the comprehensive optimization objective function; Indicates the efficiency of photovoltaic cells; This indicates the parasitic absorption loss generated by the passivation contact layer; Indicates contact resistance; Indicates the composite current density; , , , This represents the weighting coefficients corresponding to each performance index, and the parasitic absorption loss. The calculation expression is obtained based on the spectral absorption model. ,in This indicates that the passivated contact layer is at wavelength and thickness Absorption coefficient under the given conditions; Indicates the incident spectrum at wavelength Light intensity distribution at that location; Indicates the wavelength of light; Indicates the thickness of the passivation contact layer. This represents a small change in wavelength and is used to represent integral calculations over the entire spectral range. In practical calculations, for ease of engineering implementation, integral calculations are usually approximated using discrete wavelength sampling. When the calculation is in discretized form: in: These are discrete wavelength sampling points; The wavelength interval between adjacent sampling points; The number of sampling points, absorption coefficient The optical constant of each layer of material can be obtained through one of the following feasible paths. In this embodiment, the optical constant of the ellipsometer is preferred. The optical constant of each layer of material is measured by the ellipsometer. optical constants , , and by The material absorption coefficient is obtained; then combined with the thickness The absorption contribution within the layer is calculated, and a reflection correction coefficient can be introduced to adapt to the actual structure, wherein: The refractive index; Extinction coefficient; Pi is a constant.

[0032] Incident spectrum In this embodiment, a standard spectrum is preferably used as a unified comparison benchmark; when the production end needs to adapt to specific scenarios, the on-site spectrum can be obtained by a spectroradiometer and replaced. .

[0033] Weighting coefficient In this embodiment, the determination of the weight is preferably achieved using the normalization + entropy weight method or the scenario-preset weight method: Normalization: , , , These are mapped to dimensionless evaluation quantities, such as linear normalization based on the maximum / minimum values ​​of the samples; Entropy weighting method: Calculates weights based on the dispersion of each evaluation quantity in the DOE sample, so that indicators that are more distinguishable from fluctuations in the sample receive higher weights; Scenario preset: When the product objective is to prioritize efficiency or low degradation / high yield, the preset is... The relative size is determined and embedded in the control system as an optimization configuration file.

[0034] Meanwhile, when optimizing the thickness combination parameters, constraints are set to ensure that the determined optimal thickness combination satisfies the following conditions: contact resistance is lower than a preset threshold, composite current density is lower than a preset threshold, and parasitic absorption loss is lower than a preset threshold. These thresholds can be determined from the stable range of historical mass production data or product specification requirements.

[0035] In this step, maximizing single efficiency is upgraded to a multi-indicator controlled trade-off: the response relationship model provides... , , , And thickness prediction using other optional indicators, As an optical constraint, to avoid thickness combinations falling into regions where parasitic absorption increases significantly, a dual mechanism of weighting and thresholding is employed. This allows for switching optimization preferences under different product objectives and, through hard constraints, excluding thickness regions that are not suitable for mass production, thus obtaining a feasible optimal thickness combination. .

[0036] S5. Process control parameters for the deposition equipment are generated based on the optimal thickness combination, and these parameters are used for production control of the photovoltaic panel passivation contact layer deposition process. In this embodiment, the process control parameters for the deposition equipment include deposition time parameters, deposition temperature parameters, and reactive gas flow rate parameters. Precise control of the passivation contact layer thickness is achieved by adjusting these process control parameters. Specifically, the optimal thickness combination... When converting to device-executable parameters, deposition / growth rate conversion can be used: ,in The thickness of the polycrystalline silicon layer; This refers to the polycrystalline silicon deposition rate; Deposition temperature; For process power or pressure; This refers to the gas flow rate or flow ratio. Deposition time, tunneling oxide layer The formation of [something] can also be calculated by converting oxidation rate into time. The oxidation rate is preferably updated through periodic calibration and sampling thickness measurements to offset cavity state drift.

[0037] Furthermore, new electrical and optical performance data are periodically acquired during the production process, and the response relationship model is updated based on the new performance data, thereby achieving dynamic optimization of the passivation contact layer thickness parameters: periodicity can be triggered by batch, shift, or equipment status; when detected... , , , When the statistical distribution deviates from the preset control limit, the model is retrained or incrementally updated on the original model. The updated model is used to recalculate the optimal thickness combination and issue new process control parameters to achieve online calibration and adaptive optimization.

[0038] This step sets the optimal thickness to executable equipment parameters and ensures long-term stability through rate calibration and periodic model updates. The deposition rate varies with cavity contamination, gas source status, and temperature control drift. If only a one-time setting is used, it will lead to thickness drift. Through sampling thickness calibration and model update mechanisms, the drift can be fed back to the thickness, process mapping, and response models, thereby continuously maintaining the achievability and consistency of the optimal thickness combination under mass production conditions.

[0039] An optimization system for implementing the above method steps includes a parameter generation module for determining the passivation contact layer thickness parameter space and generating thickness combination parameters; a data acquisition module for acquiring corresponding photovoltaic cell performance test data based on the thickness combination parameters; a model building module for establishing a response relationship model between the thickness parameters and cell performance indicators; an optimization calculation module for determining the optimal thickness combination of the passivation contact layer based on the response relationship model; and a process control module for generating process control parameters for the deposition equipment based on the optimal thickness combination and using them for production control.

[0040] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for adaptive optimization of the passivation contact layer thickness in a photovoltaic panel, characterized in that: It includes the following steps: S1. Determine the thickness parameter space of the passivation contact layer of the photovoltaic cell, and generate multiple thickness combination parameters based on the experimental design method, wherein the thickness parameters include at least the tunneling oxide layer thickness parameter and the polycrystalline silicon passivation contact layer thickness parameter; S2. Prepare corresponding photovoltaic cell samples in a deposition equipment according to the thickness combination parameters, and perform electrical performance tests and optical performance tests on the samples to obtain corresponding performance test data; S3. Establish a response relationship model between the passivation contact layer thickness parameter and the battery performance index based on the performance test data; S4. Under the constraint of parasitic absorption loss, the thickness combination parameters are optimized according to the response relationship model to determine the optimal thickness combination of the passivation contact layer. S5. Generate process control parameters for the deposition equipment based on the optimal thickness combination, and use the process control parameters for production control of the photovoltaic panel passivation contact layer deposition process.

2. The method for adaptive optimization of the passivation contact layer thickness of a photovoltaic panel according to claim 1, characterized in that: In S2, electrical tests are performed on the photovoltaic cell sample to obtain cell performance data. Based on the electrical test results, the predicted photovoltaic cell efficiency, contact resistance, and recombination current density are obtained. The predicted photovoltaic cell efficiency is used to characterize the effect of thickness parameter changes on the cell's photoelectric conversion capability. The contact resistance is used to characterize the electrical contact characteristics between the passivation contact layer and the electrode. The recombination current density is used to characterize the degree of carrier recombination loss, thereby providing an electrical performance evaluation basis for subsequent thickness optimization.

3. The method for adaptive optimization of the passivation contact layer thickness of a photovoltaic panel according to claim 1, characterized in that: In S3, by evaluating model errors and supplementing experimental points as needed, model distortion in local areas can be avoided, ensuring the effectiveness and reproducibility of subsequent optimization solutions. By fitting the thickness parameters of the experimental samples with corresponding performance test data, a response relationship model between thickness parameters and battery efficiency is established, the expression of which is: ,in This indicates the efficiency or predicted efficiency of a photovoltaic cell. Indicates the thickness of the tunneling oxide layer; Indicates the thickness of the polysilicon passivation contact layer; This function represents the response relationship between thickness parameters and battery efficiency.

4. The method for adaptive optimization of the passivation contact layer thickness of a photovoltaic panel according to claim 3, characterized in that: In S4, the thickness combination parameters are optimized and solved by constructing a comprehensive optimization objective function, which is: ,in: This represents the comprehensive optimization objective function; Indicates the efficiency of photovoltaic cells; This indicates the parasitic absorption loss generated by the passivation contact layer; Indicates contact resistance; Indicates the composite current density; , , , This represents the weighting coefficient corresponding to each performance indicator.

5. The method for adaptive optimization of the passivation contact layer thickness of a photovoltaic panel according to claim 4, characterized in that: The parasitic absorption loss The calculation expression is obtained based on the spectral absorption model. ,in This indicates that the passivated contact layer is at wavelength and thickness Absorption coefficient under the given conditions; Indicates the incident spectrum at wavelength Light intensity distribution at that location; Indicates the wavelength of light; This indicates the thickness of the passivation contact layer.

6. The method for adaptive optimization of the passivation contact layer thickness of a photovoltaic panel according to claim 1, characterized in that: In S1, the thickness combination parameters are generated using an experimental design method. By constructing an experimental sample matrix within the thickness parameter space, different thickness combinations can cover a preset parameter range, thereby obtaining the variation law between thickness parameters and performance indicators under a limited number of experiments.

7. The method for adaptive optimization of the passivation contact layer thickness of a photovoltaic panel according to claim 4, characterized in that: When optimizing the thickness combination parameters, constraints are set to limit the optimization range so that the determined optimal thickness combination simultaneously satisfies that the contact resistance is lower than a preset threshold, the composite current density is lower than a preset threshold, and the parasitic absorption loss is lower than a preset threshold.

8. The method for adaptive optimization of the passivation contact layer thickness of a photovoltaic panel according to claim 1, characterized in that: In S5, the optimal thickness combination is converted into process control parameters for the deposition equipment, wherein the process control parameters include deposition time, deposition temperature, and reactive gas flow rate parameters, and the passivation contact layer thickness is precisely controlled by adjusting the process control parameters.

9. The adaptive optimization method for the passivation contact layer thickness of a photovoltaic panel according to claim 1, characterized in that: The process involves periodically acquiring new electrical and optical performance data during production, and updating the response relationship model based on the new performance data, thereby achieving dynamic optimization of the passivation contact layer thickness parameters. The response relationship model is obtained by fitting experimental data and is used to predict battery efficiency and parasitic absorption loss under different thickness combination parameters, so as to reduce the number of actual experiments and improve the efficiency of passivation contact layer thickness optimization.

10. An optimization system for implementing the method steps of any one of claims 1-9, characterized in that: It includes a parameter generation module for determining the passivation contact layer thickness parameter space and generating thickness combination parameters; and a data acquisition module for acquiring corresponding photovoltaic cell performance test data based on the thickness combination parameters. The model building module is used to establish a model of the response relationship between thickness parameters and battery performance indicators. The optimization calculation module is used to determine the optimal combination of passivation contact layer thicknesses based on the response relationship model. The process control module is used to generate process control parameters for the deposition equipment based on the optimal thickness combination and for production control.

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