A multi-dimensional imaging method for defect state distribution in perovskite photovoltaic devices

Abstract

The present invention discloses a multi-dimensional imaging method for the defect state distribution of a perovskite photovoltaic device, comprising the following steps: focusing a laser spot on a sample, scanning and detecting, and fitting the photocurrent; thermal admittance spectroscopy and driver-level capacitance analysis, providing DC and AC outputs through an electrical impedance analyzer, measuring the corresponding capacitance of the device, and obtaining the device's defect state density. The present invention integrates the defect characterization techniques of thermal admittance spectroscopy, driver-level capacitance analysis, and scanning photocurrent detection by fitting the carrier migration distance and defect state density values, establishing energy space simulation and spatial three-dimensional simulation of the defect distribution in the device, thereby understanding the precise energy and spatial characteristics of the defect distribution; based on the three-dimensional spatial defect distribution, combined with the defect distribution in the energy space, the microstructure and function of the perovskite in the device are revealed, providing strong technical support for the design of more targeted defect passivation strategies, and promoting the efficiency of perovskite photovoltaic devices to further approach the theoretical limit.

Description

Technical Field

[0001] The present invention relates to the technical field of imaging defect state distribution of perovskite photovoltaic devices, and in particular to a multi-dimensional imaging method for defect state distribution of perovskite photovoltaic devices. Background Art

[0002] Perovskites are cutting-edge materials with broad potential applications, exhibiting diverse crystal structures and excellent optoelectronic and magnetoelectric properties. Due to their unique advantages, such as high carrier mobility, long carrier diffusion lengths, and solution-based preparation, perovskites have found widespread application in sensors, biomedical applications, light-emitting diodes, and solar cells. To promote the commercialization of perovskite solar cells, a thorough analysis of the microstructure and properties of perovskite films and devices is crucial for understanding and exploring the underlying mechanisms.

[0003] Until now, defects remain the core problem that prevents perovskite photovoltaic devices from approaching their theoretical efficiency limits, and are also a hot topic of research for researchers. There are many different methods for detecting defect state information, and the characterization areas involve both energy and space. Thermal admittance spectroscopy is a mature technology that can be used to explore energy-related defect state distribution information, while driver-level capacitance analysis can reveal the distribution of trap states in the vertical direction of the device space. If you want to achieve the cross-integration of defect state characterization technologies and draw a panoramic view of the distribution of defects related to energy or space, you need to put the characterization information on the same level, that is, the same observation object and characterization dimension. There are many different types of defect state characterization on the surface of thin films. However, currently, analyzing the defect-related information of perovskite photovoltaic devices requires preparing multiple sets of thin film samples or devices for multiple characterization tests in order to comprehensively infer the quality of the final photovoltaic device. The characterization of defect state distribution in the horizontal direction of the device surface is still underdeveloped and urgently needs to be developed to help draw and analyze a three-dimensional panoramic view of defect states in the spatial dimension. To analyze defects horizontally, synchronous feedback of electrical signals and imaging is required. Scanning photocurrent detection technology can detect and image the photocurrent information in device samples, providing rich semiconductor information, including internal electric fields, charge transport, and recombination dynamics. This provides the prerequisite for mapping the distribution of defect states in photovoltaic devices in three-dimensional space and energy space, which is conducive to obtaining comprehensive and in-depth information related to device performance. However, researchers have not yet achieved imaging of this distribution.

[0004] The defect characterization technologies that have been developed so far are mainly focused on single-dimensional information presentation, including scanning photocurrent detection technology (J. Chen, GP Zhu, X. Li, YHL Lou, C. Dong, KL Wang, S. Yuan, CH Chen, YR Shi, T. Wang, Small 2022, 18, 2201930), driver-level capacitance analysis technology (Z. Ni, C. Bao, Y. Liu, Q. Jiang, W.-Q. Wu, S. Chen, X. Dai, B. Chen, B. Hartweg, Z. Yu, Science 2020, 367, 1352-1358) and thermal admittance spectroscopy technology, which respectively present information related to defect states and horizontal drift distance, defect states and detection depth, and defect states and defect energy range. At present, no researchers have achieved an in-depth analysis of various characterization technologies to obtain the common points of these characterization technologies, thereby realizing the cross-integration of various characterization technologies and mapping the distribution of defect states in perovskite photovoltaic devices in multiple dimensions. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a multi-dimensional imaging method for the defect state distribution of perovskite photovoltaic devices, so as to solve the problem that the existing defect characterization technology can only present single-dimensional information, the defect-related information obtained is more one-sided, and the mechanism of device performance is not thoroughly presented.

[0006] In order to solve the above technical problems, the present invention provides a multi-dimensional imaging method for defect state distribution of perovskite photovoltaic devices, comprising the following steps:

[0007] S1. Scanning photocurrent measurement: The laser spot is focused on the sample, and the photocurrent is scanned, detected, and fitted. The fitting model is an exponential decay model, and the formula is:

[0008]

[0009] Where y is the photocurrent obtained by fitting, A1 is the maximum photocurrent value measured by the device, x is the distance between the laser spot and the electrode, and L is the photogenerated carrier diffusion length obtained by fitting;

[0010] S2. Thermal admittance spectroscopy measurement and driver-level capacitance analysis measurement: The electrical impedance analyzer provides DC and AC outputs to measure the corresponding capacitance of the device;

[0011] When measuring thermal admittance spectroscopy, the distribution formula of defect state density is:

[0012]

[0013] Among them, N T (Ew) is the defect state density corresponding to a fixed energy depth, Vbi is the built-in potential of the device, W is the depletion width, which is obtained from CV measurements with Mott-Schottky analysis, and k, q, T, w, and C are the Boltzmann constant, elementary charge, temperature, angular frequency, and specific capacitance, respectively;

[0014] The formula for the partition energy is:

[0015]

[0016] Where Ew is the boundary energy, w0 is the angular frequency of trying to escape, w0=2πv0T 2 , ν0 is the temperature-independent escape attempt frequency, obtained from the temperature-dependent capacitance frequency measurement;

[0017] When analyzing and measuring the driver-level capacitance, the carrier density is calculated as follows:

[0018]

[0019] Where N is the carrier density, ε = 6.5 is the dielectric constant of perovskite, q is the basic charge, and A = 0.16 cm 2 is the active area of ​​the photovoltaic device. The derivation of the carrier density including deep trap states is based on the nonlinear relationship between the change in charge (δQ) and the perturbation AC bias (δV): δQ / δV = C0 + C1δV + C2(δV)2 + ..., while DLCP (Driver Level Capacitance Analysis) uses variable δV to measure the junction capacitance and uses the information embedded in the higher-order terms to obtain the capacitance contribution from the trap states, and the first-order derivative C0 and the second-order derivative C1 are obtained by differentiation;

[0020] The total carrier density minus the free carrier density gives the defect state density N at low AC frequencies. T ;

[0021] S3. Cross-combining the photocurrent measured by scanning photocurrent with the defect state density measured by thermal admittance spectroscopy and driver-level capacitance analysis to obtain a three-dimensional image of the defect state distribution of the device.

[0022] Based on thermal admittance spectroscopy, driver-level current analysis, and scanning photocurrent detection technology, the present invention combines optical characterization technology to collect and compare signals from various characterization technologies on perovskite photovoltaic devices with different structures, clarifying the significance and commonalities of the signals; by cross-integrating scanning photocurrent detection with thermal admittance spectroscopy and driver-level current analysis, the distribution of defect states in the complete perovskite photovoltaic device in three-dimensional space and energy space is obtained. Through two integrated images, the internal microstructure and characteristics of the device are further analyzed in depth, revealing the microscopic information of the device in depth.

[0023] Furthermore, in the three-dimensional image of defect state distribution, the X-axis is the photocurrent value obtained by scanning photocurrent detection, the Y-axis is the defect state density obtained by thermal admittance spectroscopy or driver level capacitance analysis, and the Z-axis is the detection range.

[0024] Furthermore, the scanning photocurrent measurement is specifically as follows: the laser beam emitted by the laser is focused on the sample through the objective lens, the electrically controlled XY scanning mirror drives the sample to move so that the focused beam scans the sample surface, realizing spatial mapping of the photocurrent distribution, and the sample photocurrent is de-noised and amplified through a spatial filter to convert the photocurrent into a voltage signal.

[0025] Furthermore, in S1, the bias voltage is set to 0V.

[0026] Furthermore, the laser is a continuous wave laser, which provides a continuous and stable light beam for scanning photocurrent measurement.

[0027] Furthermore, the objective lens focuses the laser beam into a diffraction-limited light spot to ensure high spatial resolution during the measurement process, and the objective lens is a high numerical aperture objective lens of a microscope.

[0028] Furthermore, the scanning range of the laser beam on the sample is 1600×500μm 2 , step size ≥ 2μm.

[0029] Furthermore, when measuring the thermal admittance spectrum, the DC bias is from 0V to the open circuit voltage of the photovoltaic device, and the AC frequency scanning range is 1kHz-10MHz.

[0030] Furthermore, during the thermal admittance spectrum measurement, the DC bias is fixed at 0V and the AC bias amplitude is 20mV.

[0031] Furthermore, when the driver-level capacitance is analyzed and measured, the total carrier density tends to saturate as the AC frequency further increases. The free carrier density N0 is obtained by measuring the carrier density at a high AC frequency, and the total carrier density is obtained by measuring the carrier density at a low AC frequency. The defect state density N0 at a low AC frequency is obtained by subtracting the free carrier density N0 from the total carrier density. T .

[0032] Furthermore, the variable δV is 20-300 mV.

[0033] Furthermore, in S3, the detection range lateral migration distance and vertical detection depth of the scanning photocurrent measurement, thermal admittance spectroscopy measurement and driver-level capacitance analysis measurement are uniformly processed.

[0034] Furthermore, in S3, when the photocurrent measured by the scanning photocurrent is cross-combined with the defect state density measured by the driving-level capacitance analysis, a three-dimensional distribution image of the device defect space is obtained. The longitudinal defect distribution of the device is reflected at the edge of the three-dimensional distribution image, and the transverse defect distribution is hidden inside the three-dimensional distribution image.

[0035] Furthermore, in S3, when the photocurrent measured by the scanning photocurrent is cross-combined with the defect state density measured by the thermal admittance spectrum, a three-dimensional distribution image of the device defect energy space is obtained. The defect distribution in the device energy space is reflected on the surface of the three-dimensional distribution image, and the lateral defect distribution is hidden inside the three-dimensional distribution image.

[0036] Beneficial effects of the present invention:

[0037] The present invention integrates defect characterization technologies such as thermal admittance spectroscopy, driver-level capacitance analysis, and scanning photocurrent detection technology by fitting the carrier migration distance and defect state density values, and establishes energy-space simulation and spatial three-dimensional simulation of the defect distribution in the device, thereby understanding the precise energy and spatial characteristics of the defect distribution.

[0038] Based on the obtained three-dimensional spatial defect distribution of photovoltaic devices and combined with the defect distribution in energy space, the present invention reveals the microstructure and function of perovskite in the device, providing strong technical support for designing more targeted defect passivation strategies and promoting the efficiency of perovskite photovoltaic devices to further approach the theoretical limit. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a schematic diagram of a scanning photocurrent detection device of the present invention;

[0040] Figure 2 Schematic diagram of the distance between the electrode and the laser point in the scanning photocurrent detection of the present invention;

[0041] Figure 3 Schematic diagram of the thermal admittance spectroscopy and driver-level capacitance analysis and testing device of the present invention;

[0042] Figure 4 In the figure, a, b, and c are respectively the signal curves obtained by the driver level capacitance analysis, scanning photocurrent measurement, and thermal admittance spectroscopy measurement in Example 1, and d and e are respectively schematic diagrams of the defect state distribution in three-dimensional space and energy space;

[0043] Figure 5 In the figure, a is a three-dimensional distribution image of device defects drawn by fusion of driver-level capacitance analysis and scanning photocurrent detection signals in Example 1, and b is a three-dimensional distribution image of device defect energy space drawn by fusion of thermal admittance spectrum and scanning photocurrent detection signals in Example 1;

[0044] Figure 6 is the three-dimensional image of defect distribution in Example 2;

[0045] Figure 7 is a three-dimensional image of defect distribution in Example 3;

[0046] Figure 8 is a three-dimensional image of defect distribution in Example 4;

[0047] Explanation of the numbers in the figure: 1. Laser, 2. Objective lens, 3. Electric-controlled XY scanning mirror, 4. Spatial filter, 5. Photovoltaic device. DETAILED DESCRIPTION

[0048] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0049] Example 1

[0050] This embodiment relates to a multi-dimensional imaging method for defect state distribution of a perovskite photovoltaic device, comprising the following steps:

[0051] S1. Build a device for scanning photocurrent detection technology, including a laser 1, an objective lens 2, an electrically controlled XY scanning mirror 3, and a spatial filter 4, such as Figure 1 As shown, the laser 1 is a continuous wave laser that provides a continuous and stable light beam for scanning photocurrent measurement. The light is focused into a diffraction-limited spot on the perovskite photovoltaic device 5 through the high numerical aperture objective lens 2 of the microscope, ensuring high spatial resolution during the measurement process. The electrically controlled XY scanning mirror 3 drives the perovskite photovoltaic device to move so that the focused light beam can scan the sample surface to achieve spatial mapping of the photocurrent distribution. The spatial filter helps to shape the light beam and filter out any unwanted spatial patterns.

[0052] use Figure 1 The device completed the photocurrent measurement. For the formamidinium lead iodide perovskite, the optimal excitation laser wavelength was confirmed to be 633nm and the power was 2mW. When the bias was 0V, the sample's photocurrent was removed from the noise and amplified by a spatial filter, and the photocurrent was converted into a voltage signal. During the photocurrent mapping process, the laser spot was fixed and the sample-level scanning range was 1600×500μm. 2 , the minimum step size is 2μm. The model of the fitting result is an exponential decay model, and the formula is Where y is the photocurrent obtained by fitting, A1 is the maximum photocurrent value measured by the device, and x is the distance between the laser spot (laser point) and the electrode (see Figure 2 ), L is the diffusion length of photogenerated carriers obtained by fitting.

[0053] S2. Build the equipment used for thermal admittance spectroscopy and driver-level capacitance analysis and testing, such as Figure 3,The electrical impedance analyzer provides DC and AC outputs and measures the corresponding ,capacitance of the device, and the capacitance-frequency measurement is performed on the ,probe station to obtain the demarcation energy.

[0054] Thermal admittance spectroscopy: For capacitance-voltage measurements, the DC bias ranges from 0 V to the open-circuit voltage of the photovoltaic device. During the measurement, the DC bias is fixed at 0 V, the AC bias amplitude is 20 mV, and the AC frequency sweep range is 1 k-10 MHz.

[0055] The distribution formula of defect state density is:

[0056]

[0057] Among them, N T (Ew) is the defect state density corresponding to a fixed energy depth, V bi is the built-in potential of the device, W is the depletion width, which is obtained from CV measurements with Mott-Schottky analysis, and k, q, T, w, and C are the Boltzmann constant, elementary charge, temperature, angular frequency, and specific capacitance, respectively;

[0058] The formula for the partition energy is:

[0059]

[0060] Where Ew is the boundary energy, w0 is the angular frequency of trying to escape, w0=2πv0T 2 , ν0 is the temperature-independent escape attempt frequency, obtained from the temperature-dependent capacitance frequency measurement;

[0061] When analyzing and measuring the driver-level capacitance, the carrier density is calculated as follows:

[0062]

[0063] Where N is the carrier density, ε = 6.5 is the dielectric constant of perovskite, q is the basic charge, and A = 0.16 cm 2 is the active area of ​​the photovoltaic device. The derivation of the carrier density including deep trap states is based on the nonlinear relationship between the change in charge (δQ) and the perturbation AC bias (δV): δQ / δV = C0 + C1δV + C2(δV)2 + ..., while DLCP (Driver Level Capacitance Analysis) uses variable δV to measure the junction capacitance and uses the information embedded in the higher-order terms to obtain the capacitance contribution from the trap states, and the first-order derivative C0 and the second-order derivative C1 are obtained by differentiation;

[0064] The total carrier density minus the free carrier density gives the defect state density N at low AC frequencies. T , and the contour distance from the junction barrier is represented by εA / C0.

[0065] In S1 and S2, the signal curves obtained by driver level capacitance analysis, scanning photocurrent measurement, and thermal admittance spectrum measurement are as follows: Figure 4 As shown in a, b, and c, the schematic diagram of defect state distribution in three-dimensional space and energy space is as follows Figure 4 As shown in d and e, single-dimensional information is presented.

[0066] S3. Combine the longitudinal direction of space with the transverse direction of space to obtain a three-dimensional distribution diagram of the device defect state in space, realize the visualization of the defect distribution, and cross-combine the results of the driver level capacitance analysis and the scanning photocurrent test to obtain the three-dimensional spatial distribution of the defect state in the photovoltaic device, such as Figure 5 Figure a shows the normalized vertical detection depth and lateral migration distance, more clearly illustrating defect distribution trends. The photogenerated carrier diffusion length, obtained by fitting test data using the physical formula listed for scanning photocurrent detection, reflects the defects distributed on the device surface but does not directly extract specific defect density values. Therefore, this 3D image provides more detailed information on defect state distribution trends in three-dimensional space, visualizing and reflecting the full spatial distribution of defect states within the device. The device's longitudinal defect distribution is reflected at the edges of the 3D image, while the lateral defect distribution is hidden within the image.

[0067] By combining the spatial longitudinal direction with energy, a three-dimensional distribution diagram of the device defect state in the energy space is obtained to realize the visualization of the defect distribution. By cross-combining the results of thermal admittance spectrum and scanning photocurrent test, the energy space distribution of the defect state in the photovoltaic device is obtained, such as Figure 5 b. The scanning photocurrent test is further integrated with the thermal admittance spectroscopy data to simulate energy correlation and device surface defect distribution. The detection range is also normalized. The distribution trend in energy space is reflected on the surface of the contour map, while the lateral distribution is hidden within the 3D image.

[0068] Example 2

[0069] Butylamine iodide molecules were used to optimize the perovskite surface for post-treatment to achieve surface passivation. Thermal admittance spectroscopy, drive-level capacitance analysis, and scanning photocurrent detection tests and data analysis were performed on the reference device (device before optimization) and the optimized device. The data were integrated into a three-dimensional distribution map of the device defect state in space and a three-dimensional distribution map in energy space. Figure 6 .

[0070] Based on spatial three-dimensional image analysis, the photocurrent value and trend obtained by scanning photocurrent detection determine the x-axis position and range of the image, and the defect state density and trend obtained by driver-level capacitance analysis determine the y-axis position and range of the image, with the detection range serving as the z-axis. The higher the y-axis value, the higher the maximum photocurrent obtained by the device, that is, the higher the corresponding initial film quality, while the lower the x-axis value, the lower the extreme defect state density. The defect state data distribution obtained by driver-level capacitance analysis and the photocurrent distribution trend obtained by scanning photocurrent detection determine the surface area of ​​the image. The smaller the area, the more concentrated the defect state distribution and the more uniform the device performance. After treating the upper interface of the device, it can be found that the defect state density of the device has decreased in both the vertical and lateral directions, indicating that the device quality has been comprehensively improved. However, the surface area did not show a significant difference, which means that the surface treatment failed to significantly optimize the defect state distribution of the device, that is, the optimization was not thorough.

[0071] Regarding the defect distribution in energy space, similar to the 3D spatial graph, the defect state density and trend obtained from the thermal admittance spectroscopy test determine the y-axis position and range of the graph. This confirms that the surface treatment can produce excellent defect passivation within a certain range. However, within the deep energy level range, there are certain areas where the optimization effect is not achieved, which is one of the reasons for the small area difference in the 3D spatial graph. The discrete test curves show an all-round improvement before and after device optimization, and the 3D graph further reveals the shortcomings of the optimization effect.

[0072] Example 3

[0073] Benzenesulfonic acid molecules were used to pretreat the lower interface of the perovskite to achieve lower interface passivation, which showed similar results to Example 2, such as Figure 7 As shown, the optimized device has achieved an all-around improvement in quality in three-dimensional space, while the surface area shows no significant difference. Bottom-interface pretreatment also fails to significantly optimize the overall defect distribution of the device. Observing the three-dimensional defect distribution diagram in energy space reveals a significant improvement in defect optimization for deep energy levels, while slightly less so for shallow energy levels. However, compared to surface treatment, bottom-interface pretreatment can alter the surface area of ​​the image. Because the perovskite film is grown and prepared on this basis, it can have a profound impact on the overall film.

[0074] Example 4

[0075] The sulfaguanidine molecules were introduced into the perovskite precursor solution for defect passivation, which showed completely different effects and images from the interface treatment. Figure 8As shown, in three-dimensional space, it is clearly shown that not only are the defect states of the optimized device fully passivated, but also because the defect passivation starts from the inside of the film, the surface area of ​​the image is significantly reduced, proving the thoroughness and effectiveness of the molecular passivation optimization. In the energy space, it can be found that the defect states are well suppressed and optimized in the entire energy range, which proves that the passivation molecules not only have a good interaction with the perovskite, but also achieve a full range of passivation effects.

[0076] In summary, based on the three-dimensional spatial defect distribution of the photovoltaic device of the present invention, combined with the defect distribution in the energy space, the microstructure and function of the perovskite film in the device are revealed, providing strong technical support for the design of more targeted defect passivation strategies, and further promoting the efficiency of perovskite photovoltaic devices to approach the theoretical limit.

[0077] The above embodiments are merely preferred embodiments for the purpose of fully illustrating the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are within the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims.

Claims

1. A multi-dimensional imaging method for defect state distribution of perovskite photovoltaic devices, characterized in that: The steps include: S1. Scanning photocurrent measurement: The laser spot is focused on the sample, and the photocurrent is scanned, detected, and fitted. The fitting model is an exponential decay model, and the formula is: ； in, y is the photocurrent obtained by fitting, A 1 The maximum photocurrent value measured for the device is x is the distance between the laser spot and the electrode, L is the diffusion length of photogenerated carriers obtained by fitting; S2. Thermal admittance spectroscopy measurement and driver-level capacitance analysis measurement: The electrical impedance analyzer provides DC and AC outputs to measure the corresponding capacitance of the device; When measuring thermal admittance spectroscopy, the distribution formula of defect state density is: ； in, N T ( Ew ) is the defect state density corresponding to a fixed energy depth, V bi is the built-in potential of the device, W is the depletion width, as determined by Mott-Schottky analysis CV The measurement shows that k 、 q 、 T 、 w and C are the Boltzmann constant, elementary charge, temperature, angular frequency, and specific capacitance, respectively; The boundary energy formula is: ; in, Ew For the boundary energy, w 0 is trying to escape the corner frequency, w 0 = , ν 0 is the temperature-independent escape attempt frequency, derived from the temperature-dependent capacitance-frequency measurement; When analyzing and measuring the driver-level capacitance, the carrier density is calculated as follows: ； in, N is the carrier density, ε =6.5 is the dielectric constant of perovskite, q is the elementary charge, A =0.16cm 2 The junction capacitance is measured using a variable perturbation AC bias for the active area of ​​the photovoltaic device, and the capacitance contribution from the trap states is obtained using the information embedded in the higher-order terms. The first-order derivative is obtained by differentiation. C 0 and the second-order derivative C 1 ; The defect state density at low AC frequencies is obtained by subtracting the free carrier density from the total carrier density. N T ; Among them, the free carrier density is obtained by measuring the carrier density at high AC frequency, and the total carrier density is obtained by measuring the carrier density at low AC frequency; S3. Cross-combine the photocurrent measured by scanning photocurrent with the defect state density measured by thermal admittance spectroscopy and driver-level capacitance analysis to obtain a three-dimensional image of the defect state distribution of the device, wherein the X-axis in the three-dimensional image of the defect state distribution is the photocurrent value obtained by scanning photocurrent detection, the Y-axis is the defect state density obtained by thermal admittance spectroscopy or driver-level capacitance analysis, and the Z-axis is the detection range.

2. The multi-dimensional imaging method for defect state distribution of a perovskite photovoltaic device according to claim 1, characterized in that: The scanning photocurrent measurement is specifically as follows: the laser beam emitted by the laser is focused on the sample through the objective lens, and the electrically controlled XY scanning mirror drives the sample to move so that the focused beam scans the sample surface to achieve spatial mapping of the photocurrent distribution. The sample photocurrent is de-noised and amplified by a spatial filter, and the photocurrent is converted into a voltage signal.

3. The multi-dimensional imaging method for defect state distribution of a perovskite photovoltaic device according to claim 2, characterized in that: The objective lens focuses the laser beam into a diffraction-limited light spot.

4. The multi-dimensional imaging method for defect state distribution of a perovskite photovoltaic device according to claim 2, characterized in that: The scanning range of the laser beam on the sample is 1600×500 μm 2 , step size ≥ 2μm.

5. The multi-dimensional imaging method for defect state distribution of a perovskite photovoltaic device according to claim 1, characterized in that: When measuring the thermal admittance spectrum, the DC bias is from 0 V to the open circuit voltage of the photovoltaic device, and the AC frequency sweep range is 1 kHz-10 MHz.

6. The multi-dimensional imaging method for defect state distribution of a perovskite photovoltaic device according to claim 1, characterized in that: The variable perturbation AC bias voltage is 20-300 mV.

7. The multi-dimensional imaging method for defect state distribution of a perovskite photovoltaic device according to claim 1, characterized in that: In S3, the detection range, lateral migration distance and vertical detection depth of the scanning photocurrent measurement, thermal admittance spectroscopy measurement and driver-level capacitance analysis measurement are homogenized.

8. The multi-dimensional imaging method for defect state distribution of a perovskite photovoltaic device according to claim 1, characterized in that: In S3, when the photocurrent measured by scanning photocurrent is cross-combined with the defect state density measured by driving-level capacitance analysis, a three-dimensional distribution image of the device defect space is obtained. The longitudinal defect distribution of the device is reflected at the edge of the three-dimensional distribution image, and the transverse defect distribution is hidden inside the three-dimensional distribution image.

9. The multi-dimensional imaging method for defect state distribution of a perovskite photovoltaic device according to claim 1, characterized in that: In S3, when the photocurrent measured by scanning photocurrent is cross-combined with the defect state density measured by thermal admittance spectroscopy, a three-dimensional distribution image of the device defect energy space is obtained. The defect distribution in the device energy space is reflected on the surface of the three-dimensional distribution image, and the lateral defect distribution is hidden inside the three-dimensional distribution image.

Images