Method for predicting performance of perovskite solar device in non-contact mode
Through the non-contact differential lifetime-photoluminescence characterization analysis method, the performance prediction problem of perovskite solar cells in the semi-device stage was solved, efficient and accurate device performance prediction and material screening were achieved, and R&D efficiency was improved.
Patent Information
- Application Number
- CN202510496564.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-21
- Publication Date
- 2025-09-12
AI Technical Summary
Existing technologies make it difficult to extract key parameters such as carrier lifetime and interface recombination rate in the semi-device stage of perovskite solar cells, and are unable to effectively predict device performance, resulting in a large number of experimental iterations and low R&D efficiency.
A non-contact differential lifetime-photoluminescence characterization and analysis method is used to extract carrier dynamics parameters through TRPL test parameter setting, data fitting and differential lifetime-photoluminescence function relationship, and the device performance is predicted in combination with the current-voltage simulation model.
It has achieved accurate prediction of perovskite solar cell performance at the semi-device stage, shortened the experimental cycle, provided a basis for interface material screening, improved R&D efficiency, and ensured that device performance is highly consistent with actual conditions.
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Figure CN120640885A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of transient photoluminescence characterization means and perovskite solar cells, and in particular to a method for predicting the performance of perovskite solar devices based on non-contact differential lifetime-photoluminescence characterization analysis. Background Art
[0002] The carrier dynamics behavior of perovskite films has a decisive influence on the performance of their devices. Perovskite materials show significant differences in the kinetic processes of carrier generation, recombination, and transport, and these processes are directly related to the photoelectric conversion efficiency and stability of the device. Therefore, a deep understanding of the kinetic behavior of carriers in perovskites of different compositions is crucial for optimizing material design and device performance. Time-resolved photoluminescence technology is a powerful tool that can reveal the recombination mechanism and lifetime distribution of carriers by monitoring the fluorescence decay process of photogenerated carriers. Compared with other characterization methods (such as transient absorption spectroscopy or electrochemical impedance spectroscopy), TRPL has the advantages of high time resolution (picosecond to nanosecond level) and non-destructive measurement, and can directly reflect key information such as defect states, interface recombination, and carrier extraction efficiency in perovskite films.
[0003] Furthermore, during the fabrication of perovskite solar cells, the layer-by-layer deposition of different functional layers (e.g., electron transport layer, perovskite active layer, hole transport layer, and electrodes) introduces complex interface effects and changes in carrier dynamics. Traditional characterization methods (e.g., current density-voltage testing and external quantum efficiency measurements) typically require the fabrication of a complete device and are unable to provide effective feedback during intermediate process stages. In recent years, numerical simulation methods (e.g., current-voltage models) have been widely used to predict the performance of perovskite solar cells. By inputting material parameters (e.g., carrier mobility, defect density, energy level alignment), JV curves can be simulated and compared with experimental data to optimize the device structure. However, existing simulations typically rely on test data from complete devices and are unable to make predictions based solely on contactless characterization of semi-devices. If key parameters (e.g., carrier lifetime, quasi-Fermi level splitting, interface recombination rate, etc.) could be extracted at the semi-device stage (e.g., when only the perovskite layer or a portion of the transport layer has been deposited) and combined with physical models to predict the final JV characteristics, the number of experimental iterations would be significantly reduced, improving R&D efficiency. Summary of the Invention
[0004] The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art and provide a method for predicting the performance of perovskite solar cell devices based on non-contact differential lifetime-photoluminescence characterization analysis. Through a new differential lifetime-PL analysis method framework, a correlation between carrier dynamics and device efficiency is established, defining a key quantitative indicator with special physical significance. Not only can the influence of the hole transport layer on the perovskite carrier extraction and recombination mechanism be analyzed, but a series of carrier characteristic parameters can also be systematically generated to serve as key variables for voltage-current characteristic simulation to predict the performance of perovskite solar cells. Practice has demonstrated that this analysis method can show a strong positive correlation with thin film structure and device performance parameters, providing an experimental basis for the rational design of interface material screening and theoretical guidance for the preparation of efficient and stable perovskite devices.
[0005] The purpose of the present invention is achieved through the following technical solutions.
[0006] A method for predicting the performance of perovskite solar devices based on non-contact differential lifetime-photoluminescence characterization analysis comprises the following steps:
[0007] S1. Set the time-resolved photoluminescence (TRPL) test parameters, including laser wavelength, laser power, test time, etc.
[0008] S2. Test the transparent electrode / hole transport layer / perovskite stacked film structure and perform high-precision fitting on the data results, such as double exponential fitting, triple exponential fitting, and polynomial fitting;
[0009] S3. Use the following formula to fit the data:
[0010]
[0011] Converted into a functional relationship between differential life and time, where t is time, is the normalized TRPL signal data;
[0012] S4, taking the differential lifespan τ in S3 diff As the vertical axis, Right now Make a curve graph for the horizontal axis and establish the differential lifetime-photoluminescence function relationship;
[0013] S5. In the differential lifetime-photoluminescence function relationship, find the point where the slope suddenly changes. This characteristic point divides the curve into a rising region with a certain slope and a platform region;
[0014] S6. Using the ordinate of the characteristic point as the differential lifetime parameter, calculate the total radiative recombination rate and the interface defect concentration;
[0015] S7. Using the total radiative recombination rate and interface defect concentration as key variables, input the current-voltage simulation model to obtain the complete current-voltage characteristic curve of the perovskite solar cell and complete the prediction of the perovskite solar cell performance.
[0016] Preferably, the TRPL test uses a laser power of 1 sun intensity, and the test time must be sufficient for the TRPL signal of the test sample to reach complete stability.
[0017] Preferably, the method can be applied to a transparent electrode / / hole transport layer / perovskite stacked film structure, wherein the hole transport layer includes but is not limited to the following materials: PEDOT:PSS, PTAA, 2PACz, MeO-2PACzACz, MeO-4PACz and Me-4PACz.
[0018] Preferably, the method can be applied to a transparent electrode / hole transport layer / perovskite stacked film structure, wherein the perovskite includes but is not limited to the following components: CH3NH3PbI3 and Cs 0.05 FA 0.9 MA 0.05 PbI 2.85 Br 0.15 .
[0019] Preferably, the high-precision fitting of the TRPL signal requires a fitting accuracy R 2 Greater than 0.95 and normalized.
[0020] Preferably, when establishing the differential lifetime-photoluminescence function relationship, the tail signal in the original TRPL data needs to be discarded to avoid interference with the analysis of the differential lifetime.
[0021] Preferably, there may be more than one slope mutation point in the differential lifetime-photoluminescence function relationship, but the last slope mutation point where the slope curve changes from a variable slope curve to a platform region tending to a constant value is the characteristic point.
[0022] Preferably, the total recombination rate is defined as the reciprocal of the ordinate of the characteristic point, in units of (s -1 ).
[0023] Preferably, the interface defect concentration N trap (Unit is cm -3 ) is calculated by the following formula:
[0024]
[0025] where v th is the carrier thermal motion velocity, take 10 7 cm / s, σ is the defect capture cross section, which is 10-19 cm 2 .
[0026] Preferably, other parameters need to be input when performing the current-voltage simulation model simulation, such as the energy level and thickness of each functional layer material.
[0027] Compared with the prior art, the present invention has the following advantages and beneficial results:
[0028] (1) This paper introduces a new analytical framework of differential equations to explain the TRPL decay of perovskite and its stacked thin film samples, establishes a differential lifetime-photoluminescence relationship, and identifies physically meaningful characteristic points as quantitative indicators for evaluating the carrier dynamics at the interface between perovskite and hole transport layer.
[0029] (2) Currently, most TRPL-based analyses of perovskite carrier lifetimes use single-exponential or double-exponential models for fitting, and simply analyze the fast and slow lifetimes obtained by fitting. This limitation makes it difficult to accurately quantify interface-related losses and extract meaningful parameters. The differential lifetime-photoluminescence analysis method proposed in this paper allows a clear distinction between the complex first-order Shockley-Read-Hall (SRH) recombination, radiative recombination, higher-order Auger recombination, and carrier extraction processes.
[0030] (3) The present invention also uses differential lifetime to convert key carrier kinetic parameters, including the recombination rate of each recombination mechanism and the interface defect density, and successfully achieves the JV characteristics of the simulated device that are almost consistent with the actual device performance. A non-contact characterization technology has been successfully established to evaluate the kinetic mechanism of carriers in the transport layer / perovskite semi-device thin film structure and predict device performance. Compared with the traditional trial-and-error method of preparing a complete device to obtain performance information, this method not only does not require the preparation of a complete device, greatly shortening the experimental cycle, but also provides a reliable analytical method for screening and optimizing interface materials, providing theoretical guidance for the development and commercialization of high-efficiency PSCs. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] Figure 1 Schematic diagram of the differential lifetime-PL analysis framework flow of the present invention.
[0032] Figure 2 ITO / PTAA / Cs in the embodiment 0.05 FA 0.9 MA 0.05 PbI 2.85 Br 0.15 Schematic diagram of TRPL decay.
[0033] Figure 3 ITO / PTAA / Cs in the embodiment0.05 FA 0.9 MA 0.05 PbI 2.85 Br 0.15 Schematic diagram of the relationship between TRPL differential lifetime and photoluminescence.
[0034] Figure 4 Schematic diagram of the simulated JV curve and the actual JV curve in the embodiment. DETAILED DESCRIPTION
[0035] The following examples further illustrate the specific embodiments of the present invention. Obviously, the embodiments described are only some of the embodiments of the present invention, and the embodiments of the present invention are not limited thereto. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0036] Figure 1 The differential lifetime-PL analysis framework flow diagram of the present invention, where Differential timeτ diff is the differential lifetime, The performance of the prepared device will be predicted in conjunction with the specific examples below.
[0037] Example 1
[0038] (1) Preparation of thin film samples: The ITO substrate was ultrasonically cleaned in detergent, deionized water and isopropyl alcohol for 40 minutes. After cleaning, it was placed in an oven at 60 degrees Celsius for drying. -3 Plasma treatment was performed for 20 minutes using a plasma oxygen microwave instrument under a near-vacuum state of mbar, which made the ITO conductive glass substrate have stronger wettability and lower work function.
[0039] (2) Depositing a hole transport layer on the ITO surface by solution processing: The ITO substrate in step (1) was transferred to a nitrogen-filled glove box, where subsequent preparations were performed. A PTAA solution (2 mg / mL, dissolved in chlorobenzene) was spin-coated on the ITO conductive glass substrate at 3000 rpm for 30 seconds and annealed at 100 degrees Celsius for 10 minutes. Subsequently, an aluminum oxide solution (diluted in isopropanol solution, volume ratio 1:80) was spin-coated on the PTAA film at 3000 rpm for 30 seconds and annealed at 100 degrees Celsius for 5 minutes.
[0040] (3) Deposit the perovskite active layer on the PTAA surface by solution processing: 1.6M MAI, FAI, MABr, PbBr2 and PbI2 were dissolved in 1mL of a mixed solvent of DMF and DMSO (volume ratio = 4:1), and 20% mass fraction of MACl was added and stirred overnight until completely dissolved to obtain a perovskite precursor solution. The precursor solution was spin-coated on an ITO substrate with a hole transport layer at a speed of 1000rpm for 5 seconds, then spin-coated at a speed of 4000rpm for 30 seconds, and 350μL of chlorobenzene solution was added as an anti-solvent in the last 5 seconds, followed by annealing at 120 degrees Celsius for 10 minutes. This is ITO / PTAA / Cs 0.05 FA 0.9 MA 0.05 PbI 2.85 Br 0.15 Preparation of thin film structures.
[0041] (4) Preparation of complete perovskite solar cell devices: on ITO / PTAA / Cs 0.05 FA 0.9 MA 0.05 PbI 2.85 Br 0.15 On the film, PCBM (concentration of 20 mg / mL, dissolved in chlorobenzene) was spin-coated on the perovskite layer at 2000 rpm for 30 seconds and annealed at 80 degrees Celsius for 2 minutes. Then, BCP (concentration of 0.5 mg / mL, dissolved in isopropyl alcohol) was spin-coated at 4000 rpm for 30 seconds. Finally, the semi-device was placed on a mask with an electrode pattern and transferred to a vacuum evaporation chamber. -6 100nm silver electrodes were deposited by thermal evaporation under mbar pressure and assembled into complete battery devices.
[0042] (5) Time-resolved photoluminescence spectroscopy: Transient photoluminescence measurements were performed using a time-correlated single photon counting (TCSPC) system equipped with a 637 nm laser source and detector, with a time resolution of less than 10 nanoseconds. The measurements were performed at room temperature, with the test frequency adjusted from 5000 Hz to 1 MHz. The laser spot diameter was 1.2 mm, and the laser intensity was set to 1 sun. 0.05 FA 0.9 MA 0.05 PbI 2.85 Br 0.15 The film was tested, and the data were collected, and a three-exponential fitting was performed, and then normalized, such as Figure 2 As shown, PL is the normalized TRPL signal data, and Time is the time.
[0043] (6) The normalized fitting data is converted into differential lifetime using formula (1). With the normalized TRPL signal as the horizontal axis and the differential lifetime as the vertical axis, the differential lifetime-photoluminescence function relationship is drawn, and the characteristic point of the slope mutation is found, such as Figure 3 As shown, where Differential time τ diff is the differential lifetime, and PL is the normalized TRPL signal data. It's worth noting that in the differential lifetime-photoluminescence function graph, the curve starts at PL = 1, i.e., on the right, and extends over time toward PL < 1. This characteristic point divides the differential lifetime-photoluminescence curve into two ideal phases: ① The curve extends from the starting PL point to the upper left to the characteristic point, during which charge extraction dominates the sample's carrier dynamics for a short period of time; ② After reaching the characteristic point, the curve plateaus, indicating that the differential lifetime does not change with PL, and non-radiative recombination dominates the sample's carrier dynamics.
[0044] (7) Assuming that under ideal conditions the ordinate of the differential lifetime-photoluminescence curve does not change after passing the characteristic point, and the differential lifetime reaches a constant value, then this value represents the carrier lifetime τ determined by SRH recombination. diff Since SRH recombination is essentially related to the defect state, the greater the defect density, the more serious the SRH recombination, and the faster the transient photoluminescence spectrum obtained by TRPL test decays, and the lower the differential lifetime is. diff The smaller it is. Therefore, τ diff It can directly reflect the defect density of the sample. Therefore, the characteristic point is a key feature for evaluating material quality and identifying areas with high defect concentration. In addition, the horizontal coordinate of the point is represented by n in this embodiment. diff It reflects the carrier concentration in this equilibrium state. When the charge extraction rate is high, the point moves to the left. Conversely, the characteristic point moves to the right.
[0045] (8) Extraction of ITO / PTAA / Cs 0.05 FA 0.9 MA 0.05 PbI 2.85 Br 0.15 Differential lifetime of thin film structure - photoluminescence characteristic point vertical coordinate τ diff = 15.90 μs. The total recombination rate is defined as τ diff The total recombination rate is calculated to be 6.29×10 4 (s -1 ). According to formula (2), the interface defect density is calculated to be 2.48×10 13 (cm -2 ).
[0046] (9) Test to obtain Cs0.05 FA 0.9 MA 0.05 PbI 2.85 Br 0.15 The HOMO energy level of the thin film is -5.69 eV and the thickness is 600 nm. The HOMO energy level of the PTAA thin film is -5.02 eV and the thickness is 10 nm. Combining the parameters in step (8) and the relevant simulation parameters such as PCBM and Ag electrode, the current-voltage simulation model is input and the voltage-current characteristic curve is output. Figure 4 Demonstrated simulation of ITO / PTAA / Cs 0.05 FA 0.9 MA 0.05 PbI 2.85 Br 0.15 The JV curves obtained for the PCB / PCBM / BCP / Ag structure are compared with the measured JV curves of actual solar cells, where "Current density" refers to current density and "Voltage" refers to voltage. It can be seen that the simulated cell characteristics obtained by this method are nearly identical to the actual cell characteristics, with the relative errors of the four major performance indicators, open-circuit voltage, short-circuit current, fill factor, and photoelectric conversion efficiency, all within 2%.
[0047] Example 2
[0048] (1) Preparation of thin film samples: The ITO substrate was ultrasonically cleaned in detergent, deionized water, and isopropyl alcohol for 40 minutes. After cleaning, it was placed in an oven at 60 degrees Celsius for drying. The dry and clean ITO conductive glass substrate was taken out and placed in a plasma oxygen microwave in a near-vacuum state for plasma treatment for 20 minutes to make the ITO conductive glass substrate more wettable.
[0049] (2) Depositing a hole transport layer on the ITO surface using solution processing: The ITO substrate in step (1) was transferred to a nitrogen-filled glove box, where subsequent preparations were performed. A MeO-2PACz solution (0.3 mg / mL in anhydrous ethanol) was spin-coated on the ITO conductive glass substrate at 3000 rpm for 30 seconds and annealed at 100 degrees Celsius for 10 minutes. Subsequently, an aluminum oxide solution (diluted in isopropanol solution, volume ratio 1:80) was spin-coated on the MeO-2PACz film at 3000 rpm for 30 seconds and annealed at 100 degrees Celsius for 5 minutes.
[0050] (3) Depositing the perovskite active layer on the MeO-2PACz surface using solution processing: 1.2 M MAPI (MAI:PbI2 = 1:1) was dissolved in 1 mL of a DMF and DMSO mixed solvent (volume ratio = 9:1), stirred overnight until completely dissolved, and then spin-coated on the glass substrate and hole transport layer at 3000 rpm for 30 seconds. At the 10th second, 200 μL of chlorobenzene was added as an antisolvent, and then annealed at 100 degrees Celsius for 10 minutes. This is the preparation of the ITO / MeO-2PACz / MAPbI3 thin film structure.
[0051] (4) Preparation of complete perovskite solar cell device: On the ITO / MeO-2PACz / MAPbI3 film, PCBM (concentration of 20 mg / mL, dissolved in chlorobenzene) was spin-coated on the perovskite layer at a speed of 2000 rpm for 30 seconds and annealed at 80 degrees Celsius for 2 minutes. Then, BCP (concentration of 0.5 mg / mL, dissolved in isopropanol) was spin-coated at a speed of 4000 rpm for 30 seconds. Finally, the semi-device was placed on a mask with an electrode pattern and transferred to a vacuum evaporation chamber. -6 100nm silver electrodes were deposited by thermal evaporation under mbar pressure and assembled into complete battery devices.
[0052] (5) Time-resolved photoluminescence spectroscopy: Transient photoluminescence measurements were performed using a time-correlated single-photon counting (TCSPC) system equipped with a 637 nm laser source and detector, with a temporal resolution of less than 10 nanoseconds. The measurements were performed at room temperature, with the test frequency adjusted from 5000 Hz to 1 MHz. The laser spot diameter was 1.2 mm, and the laser intensity was set to 1 sun. The ITO / MeO-2PACz / MAPbI3 film was tested, and the data were collected, fitted with a three-exponential function, and then normalized.
[0053] (6) The normalized fitting data is converted into differential lifetime using formula (1). With the normalized TRPL signal as the horizontal axis and the differential lifetime as the vertical axis, a differential lifetime-photoluminescence function relationship is drawn and the characteristic point of the slope mutation is found.
[0054] (7) Assuming that under ideal conditions the ordinate of the differential lifetime-photoluminescence curve does not change after passing the characteristic point, and the differential lifetime reaches a constant value, then this value represents the carrier lifetime τ determined by SRH recombination. diff Since SRH recombination is essentially related to the defect state, the greater the defect density, the more serious the SRH recombination, and the faster the transient photoluminescence spectrum obtained by TRPL test decays, and the lower the differential lifetime is. diff The smaller it is. Therefore, τ diffIt can directly reflect the defect density of the sample. Therefore, the characteristic point is a key feature for evaluating material quality and identifying high defect concentration areas. In addition, the horizontal coordinate of the point is represented by n in this embodiment. diff It reflects the carrier concentration in this equilibrium state. When the charge extraction rate is high, the point moves to the left. Conversely, the characteristic point moves to the right.
[0055] (8) Extract the differential lifetime of the ITO / MeO-2PACz / MAPbI3 film structure - the vertical coordinate τ of the photoluminescence characteristic point diff = 2.56 microseconds. The total recombination rate is defined as τ diff The total recombination rate is calculated to be 3.90×10 5 (s -1 ). According to formula (2), the interface defect density is calculated to be 4.73×10 15 (cm -2 ).
[0056] (9) The HOMO energy level of the MAPbI3 film was tested to be -5.58 eV and the thickness was 430 nm. The HOMO energy level of the MeO-2PACz film was -5.45 eV and the thickness was 10 nm. Combining the parameters in step (8) and the simulation parameters of the PCBM and Ag electrodes, the current-voltage simulation model was input and the voltage-current characteristic curve was output. Table 1 lists the JV curves obtained by simulating the ITO / MeO-2PACz / MAPbI3 / PCBM / BCP / Ag structure and the actual measured JV curves of the actual solar cell. It can be seen that the simulated battery characteristics obtained by this method are almost consistent with the actual battery characteristics. The relative errors of the four major performance indicators of open circuit voltage, short circuit current, fill factor and photoelectric conversion efficiency are all within 1.5%.
[0057] Table 1
[0058] Actual device data Analog device data Relative error (%) Open circuit voltage (V) 1.06 1.05 0.71 <![CDATA[Short-circuit current (mA / cm 2 )]]> 21.90 22.22 1.46 Fill factor (%) 77.13 77.68 0.71 efficiency(%) 17.83 18.18 1.93
[0059] Example 2 uses a hole transport layer and perovskite active layer different from those of Example 1. However, the analysis method of the present invention can still obtain device performance predictions that are highly consistent with the actual device conditions.
[0060] The above is merely an example of the implementation of the present invention. The present invention can also be used to predict other existing perovskite solar devices, which will not be described in detail here.
Claims
1. A non-contact method for predicting the performance of a perovskite solar device, characterized in that: The following steps are involved: S1. Set the time-resolved photoluminescence (TRPL) test parameters, including laser wavelength, laser power, and test time; S2. Testing the transparent electrode / hole transport layer / perovskite stacked film structure and performing high-precision fitting on the data results; the high-precision fitting includes bi-exponential fitting, triple-exponential fitting, or polynomial fitting; S3. Fit the data according to the following formula: Converted into a functional relationship between differential life and time t, is the normalized TRPL signal data; S4, taking the differential lifespan τ in S3 diff As the vertical axis, Right now Make a curve graph for the horizontal axis and establish the differential lifetime-photoluminescence function relationship; S5. In the differential lifetime-photoluminescence function relationship, find the point where the slope suddenly changes. This characteristic point divides the curve into a rising region with a certain slope and a platform region; S6. Using the ordinate of the characteristic point as the differential lifetime parameter, calculate the total radiative recombination rate and the interface defect concentration; S7. Take the total radiative recombination rate and interface defect concentration as key variables, input them into the voltage-current characteristic curve simulation platform, obtain the current-voltage characteristic curve of the complete perovskite solar cell, and complete the prediction of the perovskite solar cell performance.
2. The method for predicting the performance of a perovskite solar device based on a non-contact method according to claim 1, characterized in that: TRPL testing uses a laser power of 1 sun intensity, and the test time must be sufficient for the TRPL signal of the test sample to reach complete stability.
3. The method for predicting the performance of a perovskite solar device based on a non-contact method according to claim 1, characterized in that: The method is applied to a transparent electrode / / hole transport layer / perovskite stacked film structure, wherein the hole transport layer includes but is not limited to PEDOT:PSS, PTAA, 2PACz, MeO-2PACzACz, MeO-4PACz and Me-4PACz hole transport layer materials.
4. The method for predicting the performance of a perovskite solar device based on a non-contact method according to claim 1, characterized in that: This method is applied to a transparent electrode / hole transport layer / perovskite stacked film structure, where the perovskite includes but is not limited to CH3NH3PbI3 and Cs 0.05 FA 0.9 MA 0.05 PbI 2.85 Br 0.15 and other perovskite components.
5. The method for predicting the performance of a perovskite solar device based on a non-contact method according to claim 1, characterized in that: High-precision fitting of TRPL signals, fitting accuracy R 2 Greater than 0.95 and normalized.
6. The method for predicting the performance of a perovskite solar device based on a non-contact method according to claim 1, characterized in that: When establishing the differential lifetime-photoluminescence function relationship, the tail signal in the original TRPL data is discarded.
7. The method for predicting the performance of a perovskite solar device based on a non-contact method according to claim 1, characterized in that: If there is more than one slope mutation point in the differential lifetime-photoluminescence function relationship, the last slope mutation point where the slope curve changes from a variable slope curve to a platform region tending to a constant value is taken as the characteristic point.
8. The method for predicting the performance of a perovskite solar device based on a non-contact method according to claim 1, characterized in that: The total radiation recombination rate is defined as the reciprocal of the ordinate of the characteristic point, in units of (s -1 ).
9. The method for predicting the performance of a perovskite solar device based on a non-contact method according to claim 1, characterized in that: Interface defect concentration N trap Calculated by the following formula: Among them, v th is the carrier thermal motion velocity, take 10 7 cm / s, σ is the defect capture cross section, which is 10 -19 cm 2 .
10. The method for predicting the performance of a perovskite solar device based on a non-contact method according to claim 1, characterized in that: The voltage-current characteristic curve simulation platform adopts the open source simulation platform SIMsalabim developed by the University of Groningen. The voltage-current characteristic curve simulation also requires input of energy level and thickness parameters.