Method for combined characterization and optimization of ion migration and defect states of perovskite devices

Abstract

This invention discloses an optimization method for the joint characterization of ion migration and defect states in perovskite devices, comprising: acquiring the complex impedance value of the target device at each frequency point in a preset frequency range under each bias point in a preset bias sequence and calculating the actual capacitance value, generating an actual capacitance spectrum matrix including the correspondence between bias, frequency and actual capacitance value; extracting the frequency sequence and actual capacitance value corresponding to each bias point based on the actual capacitance spectrum matrix, and constructing an actual spectrum curve for each bias point based on the frequency logarithm and actual capacitance value of each frequency point, so as to synchronously acquire defect state or ion migration information, and uniformly analyze the coupling effect of ion migration and trapping in low-frequency capacitance, and realize the joint quantitative extraction of ion migration parameters and defect state parameters, so as to promote the development of joint characterization technology of ion migration and defect states in perovskite devices.

Description

Technical Field

[0001] This invention relates to the field of perovskite device internal information characterization technology, and more specifically, to an optimized method for joint characterization of ion migration and defect states in perovskite devices. Background Technology

[0002] Perovskite devices primarily refer to novel optoelectronic devices with organic-inorganic hybrid metal halide perovskite materials as the core light-absorbing layer or functional layer, such as perovskite solar cells. Perovskite devices possess excellent photoelectric properties and show great promise in photovoltaics, light emission, and detection. In practical applications of perovskite devices, defect states, acting as non-radiative recombination centers, directly reduce the open-circuit voltage and fill factor, while ion migration leads to current and voltage hysteresis, phase separation, and long-term performance degradation. Both are key factors determining device performance and stability. However, defect states and ion migration typically affect the device's capacitive response on similar timescales, and their coupling effect poses significant challenges for individual characterization and quantitative analysis.

[0003] Existing internal information characterization methods, such as deep-level transient spectroscopy and thermal admittance spectroscopy, only acquire single-dimensional information on defect states or ion migration to achieve characterization analysis. This makes it difficult to uniformly analyze the coupling effect of ion migration and trapping in low-frequency capacitors, and it is also impossible to simultaneously achieve joint quantitative extraction of ion migration parameters and trap state parameters. This seriously restricts the development of joint characterization technology for ion migration and defect states in perovskite devices.

[0004] Therefore, how to develop a method that can jointly characterize and analyze defect states and ion migration to optimize the performance and stability of perovskite devices has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention aims to overcome the problems of existing perovskite device internal information characterization methods, which only obtain single-dimensional information on ion migration or defect states for characterization analysis. These methods are difficult to uniformly analyze the coupling effect of ion migration and trapping in low-frequency capacitors, and cannot simultaneously achieve joint quantitative extraction of ion migration parameters and defect state parameters. This seriously restricts the development of joint characterization technology for ion migration and defect states of perovskite devices.

[0006] To achieve the above objectives, this invention provides a method for joint characterization and optimization of ion migration and defect states in perovskite devices, comprising: At each bias point in the preset bias sequence, the complex impedance value of the target device at each frequency point in the preset frequency range is collected and the actual capacitance value is calculated to generate an actual capacitance spectrum matrix that includes the correspondence between bias, frequency and actual capacitance value. Based on the actual capacitance spectrum matrix, the frequency sequence and actual capacitance value corresponding to each bias point are extracted, and the actual spectrum curve of each bias point is constructed according to the frequency logarithm of each frequency point and the actual capacitance value. Based on the actual spectrum curve of each bias point, the corresponding high-frequency capacitance value, low-frequency capacitance value, capacitor relaxation inflection frequency, and capacitor step amplitude are obtained under the preset frequency division rules. The intrinsic property parameters of the target device are pre-acquired and input into the simulation software to obtain the geometric parameters, initial ion migration parameters and initial trap state parameters of the target device. The parameters are constrained by high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection frequency and capacitance step amplitude. A physical field coupling simulation model is constructed and the simulated capacitance spectrum matrix is ​​output. The actual capacitance spectrum matrix and the simulated capacitance spectrum matrix are compared based on the parameter optimization algorithm. The difference between the comparison is input into the parameter optimization function for iterative calculation. When the parameter optimization function converges to a preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters and optimized defect state parameters are output.

[0007] Optionally, at each bias point in the preset bias sequence, the complex impedance value of the target device is collected at each frequency point in the preset frequency range, and the actual capacitance value is calculated to generate an actual capacitance spectrum matrix that includes the correspondence between bias, frequency, and actual capacitance value. Specifically, this includes: A preset bias sequence is set using an impedance analyzer, and at each bias point in the preset bias sequence, the complex impedance value of the target device at each frequency point from 1mHz to 5MHz is acquired based on logarithmic scanning. Based on the complex impedance value, the actual capacitance value of the target device at each frequency point is calculated using the complex impedance formula. It also generates an actual capacitance spectrum matrix that includes the correspondence between bias voltage, frequency, and actual capacitance value.

[0008] Optionally, in addition to setting the complex impedance value of the target device at each frequency point from 1 to 5 MHz using an impedance analyzer at each bias point in a preset bias sequence, the method also includes: The effective value of the AC disturbance voltage of the impedance analyzer is set by the control equipment; The current and voltage signal demodulators of the impedance analyzer are then turned on to perform synchronous phase-locked loop amplification and detection on the target device, and the filter order is set to be greater than the order threshold.

[0009] Optionally, the step of extracting the frequency sequence and actual capacitance value corresponding to each bias point based on the actual capacitance spectrum matrix, and constructing the actual spectrum curve of each bias point based on the frequency logarithm and actual capacitance value, specifically includes: Extract the frequency sequence and actual capacitance value corresponding to each bias point based on the actual capacitance spectrum matrix; A logarithmic coordinate system is constructed with the logarithm of the frequency points in the frequency sequence as the abscissa and the actual capacitance value as the ordinate. The actual spectrum curve of each bias point is constructed based on the logarithm of the frequency points and the corresponding actual capacitance value.

[0010] Optionally, the step of obtaining the corresponding high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude based on the actual spectrum curve of each bias point under a preset frequency division rule specifically includes: Based on the preset frequency division rules, identify the high-frequency plateau segment of the curve in the actual spectrum curve where the frequency is greater than the pre-acquired high-frequency threshold, so as to obtain the high-frequency capacitance plateau interval and select the high-frequency capacitance value within it. Identify the low-frequency plateau segment of the curve in the actual spectrum curve where all frequencies are lower than the pre-acquired low-frequency threshold, in order to obtain the low-frequency capacitance plateau interval and select the low-frequency capacitance value within it. Identify the inflection point frequency range where the curve transitions from a high-frequency plateau segment to a low-frequency plateau segment, and extract the frequency within the inflection point frequency range that is the same as the pre-acquired inflection point threshold, defining it as the capacitive relaxation inflection point frequency. The capacitance step amplitude is obtained based on the difference between the low-frequency capacitance value and the high-frequency capacitance value.

[0011] Optionally, the process of pre-acquiring intrinsic property parameters of the target device and inputting them into simulation software to obtain the geometric parameters, initial ion migration parameters, and initial trap state parameters of the target device, and constraining the parameters with high-frequency capacitance values, low-frequency capacitance values, capacitance relaxation inflection point frequencies, and capacitance step amplitudes, constructing a physical field coupling simulation model and outputting the simulated capacitance spectrum matrix, specifically includes: Input the intrinsic attribute parameters of the target device to be acquired into the simulation software; Geometric parameters of the target device are obtained based on the DDE equations in the simulation software. Initial ion migration parameters are obtained based on the NP equations in simulation software; Initial trap state parameters are obtained based on the SRH model in simulation software; The high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude are input into the simulation software for parameter constraints in order to construct a physical field coupling simulation model and output the simulated capacitance spectrum matrix.

[0012] Optionally, the step of inputting high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude into simulation software for parameter constraints to construct a physical field coupling simulation model and output a simulated capacitance spectrum matrix specifically includes: The high-frequency capacitance value, low-frequency capacitance value, capacitor relaxation inflection point frequency, and capacitor step amplitude are input into the simulation software for parameter constraints in order to construct a physical field coupling simulation model. Input the simulated bias voltage sequence, simulated frequency range, simulated values ​​of AC disturbance voltage, and simulated filter order into the physical field coupling simulation model to output the simulated capacitor spectrum matrix.

[0013] Optionally, the parameter optimization algorithm compares the actual capacitance spectrum matrix and the simulated capacitance spectrum matrix, inputs the difference into the parameter optimization function for iterative calculation, and outputs optimized ion migration parameters and optimized defect state parameters when the parameter optimization function converges to a preset threshold or reaches the maximum number of iterations. Specifically, this includes: The actual capacitor spectrum matrix and the simulated capacitor spectrum matrix are compared using the least squares method, and the comparison difference is obtained. The objective function is optimized by comparing the input parameters of the difference, so that when the objective function converges to a preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters and optimized defect state parameters are output.

[0014] Optionally, the step of optimizing the objective function by comparing the input parameters of the difference, so as to output optimized ion migration parameters and optimized defect state parameters when the objective function converges to a preset threshold or reaches the maximum number of iterations, specifically includes: By comparing the input parameters of the difference and optimizing the objective function, we obtain: In the formula: Represented as a simulated capacitor spectrum matrix; This is expressed as measurement uncertainty; When When convergence reaches a preset threshold or the maximum number of iterations is reached, the optimized ion migration parameters and optimized defect state parameters are output. Among them, optimizing ion migration parameters includes optimizing ion mobility, optimizing ion diffusion coefficient, optimizing the concentration of mobile ions, and optimizing ion activation energy; optimizing defect state parameters includes optimizing trap level depth, optimizing electron trapping cross section, optimizing hole trapping cross section, optimizing total trap state density, and optimizing energy distribution width.

[0015] Optionally, the step of optimizing the objective function by comparing the input parameters of the difference, so as to output the optimized ion migration parameters and optimized defect state parameters when the objective function converges to a preset threshold or reaches the maximum number of iterations, also includes: When the objective function for parameter optimization converges to a preset threshold or reaches the maximum number of iterations, the confidence intervals for the optimized ion migration parameters and the confidence intervals for the optimized defect state parameters are output.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The proposed method for joint characterization and optimization of ion migration and defect states in perovskite devices generates an actual capacitance spectrum matrix, enabling the complete acquisition of capacitance response information in preset frequency bands under different operating states of the target device, providing a comprehensive experimental data foundation for the analysis of coupling effects. Furthermore, by extracting feature parameters, it achieves a preliminary physical mechanism classification of high-frequency geometric capacitance, mid-frequency trap response, and low-frequency ion + deep trap response, providing precise experimental anchors for subsequent model constraints. Moreover, by introducing intrinsic property parameters of the target device and combining them with experimental feature parameters to construct a physical field coupling simulation model, it achieves digital reproduction of the coupling physical processes of trap trapping and ion migration. Finally, by using a parameter optimization algorithm to iteratively compare differences, it achieves simultaneous quantitative extraction and decoupling of ion migration parameters and defect state parameters, completely solving the problem that existing technologies cannot uniformly analyze coupling effects and can only obtain single-dimensional information. This provides a core basis with both physical reliability and quantitative accuracy for defect control and stability improvement of perovskite devices.

[0017] As can be seen from the above, the technical solution of the present invention can effectively solve the problem that existing perovskite device internal information characterization methods only achieve characterization analysis by obtaining single-dimensional information of ion migration or defect states, making it difficult to uniformly analyze the coupling effect of ion migration and trapping in low-frequency capacitors, and failing to simultaneously achieve joint quantitative extraction of ion migration parameters and defect state parameters, which seriously restricts the development of joint characterization technology for ion migration and defect states of perovskite devices.

[0018] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart illustrating the joint characterization and optimization method for ion migration and defect states of perovskite devices according to an embodiment of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to more fully understand the technical solutions of the present invention, exemplary embodiments of the present invention will be described more comprehensively and in detail below with reference to the accompanying drawings. Obviously, the one or more embodiments of the present invention described below are merely one or more specific ways to implement the technical solutions of the present invention, and are not exhaustive. It should be understood that other ways belonging to a general inventive concept can be used to implement the technical solutions of the present invention, and should not be limited to the embodiments described exemplary. Based on one or more embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] Reference Figure 1 The present invention provides a method for joint characterization and optimization of ion migration and defect states in perovskite devices, comprising: Step S11: At each bias point in the preset bias sequence, collect the complex impedance value of the target device at each frequency point in the preset frequency range. It also calculates the actual capacitance value C(f,V) and generates an actual capacitance spectrum matrix that includes the correspondence between bias voltage, frequency, and the actual capacitance value C(f,V). ; Step S12: Based on the actual capacitance spectrum matrix Extract the frequency sequence and actual capacitance value C(f,V) corresponding to each bias point, and construct the actual spectrum curve of each bias point based on the frequency logarithm of each frequency point and the actual capacitance value C(f,V). Step S13: Based on the actual spectrum curve of each bias point, obtain the corresponding high-frequency capacitance value, low-frequency capacitance value, capacitor relaxation inflection point frequency, and capacitor step amplitude under the preset frequency division rules. Step S14: Pre-acquire the intrinsic property parameters of the target device and input them into the simulation software to obtain the geometric parameters, initial ion migration parameters and initial trap state parameters of the target device. Then, use high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency and capacitance step amplitude to constrain the parameters, construct a physical field coupling simulation model and output the simulation capacitance spectrum matrix. Step S15: Optimize the actual capacitance spectrum matrix using a parameter optimization algorithm. The comparison is performed with the simulated capacitance spectrum matrix, and the difference is input into the parameter optimization function for iterative calculation. When the parameter optimization function converges to the preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters and optimized defect state parameters are output.

[0022] The proposed method for joint characterization and optimization of ion migration and defect states in perovskite devices generates an actual capacitance spectrum matrix. This research achieves complete acquisition of the capacitance response information of the target device in preset frequency bands under different operating states, providing a comprehensive experimental data foundation for the analysis of coupling effects. Furthermore, by extracting feature parameters, it achieves a preliminary physical mechanism classification of high-frequency geometric capacitance, mid-frequency trap response, and low-frequency ion + deep trap response, providing precise experimental anchors for subsequent model constraints. By introducing intrinsic property parameters of the target device and combining them with experimental feature parameters, a physical field coupling simulation model is constructed, enabling the digital reproduction of the coupling physical processes of trap trapping and ion migration. Additionally, by using parameter optimization algorithms to iteratively compare differences, it achieves simultaneous quantitative extraction and decoupling of ion migration parameters and defect state parameters, completely solving the problem that existing technologies cannot uniformly analyze coupling effects and can only obtain single-dimensional information. This provides a core basis with both physical reliability and quantitative accuracy for defect control and stability improvement of perovskite devices.

[0023] In one embodiment, in step S1, at each bias point in the preset bias sequence, the complex impedance value of the target device at each frequency point in the preset frequency range is collected. It also calculates the actual capacitance value C(f,V) and generates an actual capacitance spectrum matrix that includes the correspondence between bias voltage, frequency, and the actual capacitance value C(f,V). Previously, including: The target device is placed inside a shielded box, and the test terminals of the impedance analyzer are connected to the terminals of the target device, while the control terminals are connected to the control equipment.

[0024] Specifically, since the low-frequency capacitance response signal of perovskite devices is the key to distinguishing between trapping and ion migration coupling effects, the target device is placed in a shielded box to isolate it from external electromagnetic interference and prevent environmental clutter from covering or interfering with the weak low-frequency capacitance response signal of the target device, so as to ensure the signal-to-noise ratio and accuracy of the multi-bias full-frequency test data.

[0025] By connecting the test terminals of the impedance analyzer to the electrode terminals of the target device and the control terminals to a control device, the test parameters of the impedance analyzer can be precisely controlled and the test data of the target device can be automatically acquired through the data acquisition program within the control device.

[0026] The shielding box and control equipment can be, for example, an RF shielding box and a host computer, respectively. Both the shielding box and the control equipment are existing technologies, so their specific structures and working principles will not be described in detail here.

[0027] In one embodiment, the high current terminal (HCUR) and low current terminal (LCUR) of the impedance analyzer are connected to the positive and negative terminals of the target device under test, respectively.

[0028] Specifically, in the two-terminal method of this embodiment, the impedance of the target device... Drive voltage of impedance analyzer With measuring current Direct calculation yields: The two-terminal connection method is simple to operate, provides a stable connection, and is suitable for rapid measurement of high-impedance target devices.

[0029] In another embodiment, the high current terminal (HCUR) and low current terminal (LCUR) of the impedance analyzer are connected to the positive and negative terminals of the target device under test, respectively, and its high potential probe terminal (HPOT) and low potential probe terminal (LPOT) are led out to the positive and negative terminals of the target device, respectively.

[0030] Specifically, under the four-terminal method in this embodiment, the current driving circuit and the voltage measurement circuit can be separated, thereby effectively eliminating the influence of test cable resistance and contact resistance on the measurement results, improving the measurement accuracy under low impedance conditions, and making it suitable for rapid measurement of low impedance target devices.

[0031] In one embodiment, in step S11, at each bias point in the preset bias sequence, the complex impedance value of the target device at each frequency point in the preset frequency range is collected. It also calculates the actual capacitance value C(f,V) and generates an actual capacitance spectrum matrix that includes the correspondence between bias voltage, frequency, and the actual capacitance value C(f,V). Specifically, it includes: Step S111: Set a preset bias sequence using an impedance analyzer, and at each bias point in the preset bias sequence, collect the complex impedance value of the target device at each frequency point from 1mHz to 5MHz based on logarithmic scanning. ; Step S112: Based on complex impedance value The actual capacitance value C(f,V) of the target device at each frequency point is calculated using the complex impedance formula. It also generates a real capacitance spectrum matrix that includes the correspondence between bias voltage, frequency, and actual capacitance value C(f,V). .

[0032] Specifically, complex impedance value for: In the formula: Represented as frequency, This is represented as a bias voltage. It is expressed as a resistive component and defined as a complex impedance value. The real part, It is expressed as a reactance component and defined as a complex impedance value. The imaginary part.

[0033] Complex impedance value It can be directly obtained through the data acquisition program. It is worth noting that the impedance Z mentioned above represents the general physical concept of the ability of the target device to impede the flow of current in the measurement, and the complex impedance value Z(f,v) represents the specific complex quantitative form of this general physical concept at frequency f and bias voltage v.

[0034] Actual capacitance value If, then: In the formula: Expressed as angular frequency, This is expressed as taking the complex impedance value. The imaginary part is calculated.

[0035] Suppose that the preset bias sequence V has M bias points, then ,in, Let f be the m-th bias point; assuming there are N frequency points from 1 MHz to 5 MHz, then f = [ ,in, Let it be the nth frequency point; let Let C(f,V) be the actual capacitance value measured at bias point m and frequency point n. Then, the actual capacitance spectrum matrix is... : .

[0036] The actual capacitance value C(f,V) is calculated based on the complex impedance formula, and the actual capacitance spectrum matrix is ​​generated. , the complex impedance value Converting these values ​​into actual capacitance values ​​C(f,V) that can be directly used for feature extraction provides complete experimental data support for distinguishing high-frequency capacitance, mid-frequency trap capacitance, and low-frequency ion polarization capacitance. This is a prerequisite for decoupling and jointly quantitatively extracting defect states and ion migration signals.

[0037] Furthermore, the range of values ​​for M is 15 to 30, and the range of values ​​for N is 50 to 100.

[0038] It is worth noting that the preset bias sequence is input to the impedance analyzer via a control device. The impedance analyzer then precisely outputs the corresponding bias voltage to the target device, enabling gradient bias scanning based on the preset bias sequence. The preset bias sequence consists of a reverse bias range, a working bias range, and a forward bias range. The reverse bias range has a voltage range of -1 to 0V, with a voltage difference of 0.1V between adjacent bias points; the working bias range has a voltage range of 0V to open-circuit voltage, with a voltage difference of 0.05V between adjacent bias points; and the forward bias range has a voltage range of 0 to 1V, with a voltage difference of 0.1V between adjacent bias points.

[0039] The voltage difference between two adjacent bias points controls the precision of the bias scan, while a smaller step size is used in the working range to accurately capture the capacitance characteristics changes of the target device within its actual working range. Open-circuit voltage refers to the maximum potential difference across the target device when the external circuit is disconnected and no current flows out; it reflects the target device's potential voltage capability to convert light energy into electrical energy.

[0040] The forward bias range corresponds to the target device being in a carrier injection state. Under this state, the interface charge accumulation and ion migration effects of the target device are amplified, and the characteristic signals of interface state and ion migration can be obtained in a targeted manner, which is beneficial for obtaining information on the interface charge accumulation effect of the target device.

[0041] The operating bias range simulates the actual power generation state of the target device. By using smaller step sizes, it accurately captures subtle changes in capacitance, providing core data for analyzing the trapping and ion migration synergistic effects of the target device under real operation.

[0042] The reverse bias range corresponds to the target device being in a depletion state. At this time, the bulk deep level traps in the trap state will significantly affect the capacitor response. By using bias testing, the interference of interface carrier injection can be avoided, and the characteristic signals of deep level traps can be accurately obtained.

[0043] The data from the three intervals together cover the entire state of the target device from depletion to implantation, providing complete experimental data support for subsequent joint quantitative extraction of defect states and ion migration through simulation models.

[0044] Furthermore, within the preset frequency range of 1 MHz to 5 MHz, not only is a logarithmic scanning mode employed, but also, while satisfying N, 10 to 20 frequency points are selected within each frequency order of magnitude to ensure sufficient data points are collected in the range where capacitance changes rapidly, balancing testing efficiency with the accuracy of characteristic signal capture. This ensures coverage of the high-frequency region dominated by the geometric capacitance of the target device, the mid-frequency region of shallow trap response, and the low-frequency region of deep trap and ion migration coupling, while maintaining the sampling density of key characteristic points in each frequency band through logarithmic scanning, thus ensuring the complete capture of capacitance response characteristics of different physical mechanisms.

[0045] Each frequency order of magnitude is represented by a frequency value that is 10 times the adjacent frequency value, such as 1mHz~10mHz, 1Hz~10Hz, 1kHz~10kHz, and 100kHz~1MHz.

[0046] In one specific embodiment, in step S111, the complex impedance value of the target device is acquired at each frequency point from 1 to 5 MHz based on logarithmic scanning at each bias point in the preset bias sequence using an impedance analyzer. In addition, it also includes: The effective value of the AC disturbance voltage of the impedance analyzer is set by the control equipment; The current and voltage signal demodulators of the impedance analyzer are then turned on to perform synchronous phase-locked loop amplification and detection on the target device, and the filter order is set to be greater than the order threshold.

[0047] Specifically, the effective value of the AC disturbance voltage needs to be set to meet the AC amplitude under the small-signal linear response condition at room temperature. : < ≈26mV In the formula: Expressed as Boltzmann constant, Represented as absolute temperature, It is represented as elementary charge.

[0048] To avoid excessive disturbances that alter the carrier distribution and trapping state within the target device, and to ensure that the capacitor response reflects the inherent characteristics of the target device rather than nonlinear distortion, this provides a linearized data foundation for accurately distinguishing high-frequency geometric capacitance, mid-frequency trapping capacitance, and low-frequency ion-polarized capacitance. In this embodiment... =20mV.

[0049] By performing synchronous lock-in amplification and detection on the target device and setting the filter order >24dB / oct, the weak capacitance response signal that is in phase and frequency with AC disturbance is specifically amplified using the phase-sensitive detection principle of lock-in amplification. At the same time, the environmental electromagnetic noise and DC bias interference are suppressed by the filter, which solves the problem that the coupling signal of deep traps and ion migration in perovskite devices in the low frequency band is easily submerged by noise. This ensures that the key characteristic signals reflecting the coupling effect can be accurately captured, providing experimental data support with high signal-to-noise ratio for the construction of subsequent physical field coupling simulation models. This is a necessary prerequisite for realizing the joint quantitative characterization of defect states and ion migration.

[0050] In one embodiment, in step S12, based on the actual capacitance spectrum matrix Extract the frequency sequence and actual capacitance value C(f,V) corresponding to each bias point, and construct the actual spectrum curve of each bias point based on the frequency logarithm of each frequency point and the actual capacitance value C(f,V), specifically including: Step S121: Based on the actual capacitance spectrum matrix Extract the frequency sequence and actual capacitance value C(f,V) corresponding to each bias point; Step S122: Construct a logarithmic coordinate system with the frequency logarithm of the frequency points in the frequency sequence as the abscissa and the actual capacitance value C(f,V) as the ordinate, and construct the actual spectrum curve of each bias point based on the frequency logarithm of each frequency point and the corresponding actual capacitance value C(f,V).

[0051] Specifically, from the actual capacitance spectrum matrix The frequency point and actual capacitance value C(f,V) corresponding to each bias point are extracted from the middle part. Then, the actual frequency spectrum curve of each bias point is constructed with the logarithm of the frequency point as the x-axis and the actual capacitance value C(f,V) as the y-axis. This transforms the normalized matrix data into an intuitive and visual feature curve, providing an intuitive analytical basis for subsequent analysis of the frequency-varying characteristics of the target device and extraction of relevant target device parameters. This makes the differences in frequency capacitance characteristics of the target device under different biases clearly distinguishable.

[0052] In one embodiment, in step S13, based on the actual spectrum curve of each bias point, the corresponding high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude are obtained under a preset frequency division rule. Specifically, this includes: Step S131: Based on the preset frequency division rules, identify that all frequencies in the actual spectrum curve are greater than the pre-acquired high-frequency threshold. The high-frequency plateau segment of the curve is used to obtain the high-frequency capacitance plateau range and select the high-frequency capacitance value within it. Step S132: Identify the low-frequency plateau segment of the curve in the actual spectrum curve where all frequencies are lower than the pre-acquired low-frequency threshold, so as to obtain the low-frequency capacitance plateau interval and select the low-frequency capacitance value within it; Step S133: Identify the inflection point frequency range where the curve transitions from a high-frequency plateau segment to a low-frequency plateau segment, and extract and pre-acquire the inflection point threshold within the inflection point frequency range. The same frequency is defined as the capacitor relaxation inflection point frequency; Step S134: Obtain the capacitance step amplitude based on the difference between the low-frequency capacitance value and the high-frequency capacitance value.

[0053] Specifically, high-frequency capacitance values ​​correspond to the geometric capacitance of the target device, low-frequency capacitance values ​​reflect the maximum interfacial polarization capacitance caused by deep-level defect trapping and ion migration, the capacitance relaxation inflection point frequency corresponds to the trap state emissivity or ion migration response time of the target device, and the capacitance step amplitude reflects the contribution of trap states and ion migration to the total capacitance. A larger capacitance step amplitude indicates a higher defect state density or a higher concentration of migrateable ions. The higher the value, the more the intuitive curve characteristics of capacitance changing with frequency under different bias voltages in the actual spectrum curve can be transformed into quantifiable and analyzable characteristic parameters, providing an accurate and reliable quantitative basis for subsequent analysis of dielectric capacitance relaxation characteristics and characterization of the electrical performance of target devices.

[0054] It is worth noting that the high-frequency threshold is pre-acquired. It is determined by the relative permittivity of the target device. ,area and thickness The decision must meet the following conditions: In the formula: It is expressed as the vacuum permittivity.

[0055] The pre-acquisition low-frequency threshold is determined based on the frequency at which the capacitor can enter a stable low-frequency platform, based on the slow relaxation processes such as ion migration or trap state charging and discharging.

[0056] Pre-acquisition of inflection point threshold The acquisition of satisfaction: In the formula: It is expressed as the logarithm of the actual capacitance value C(f,V). It represents the logarithm of frequency.

[0057] In one embodiment, in step S14, intrinsic property parameters of the target device are pre-acquired and input into simulation software to obtain the target device's geometric parameters, initial ion migration parameters, and initial trap state parameters, and to obtain high-frequency capacitance values, low-frequency capacitance values, and capacitance relaxation inflection point frequencies. The parameters of the capacitance step amplitude are constrained to construct a physical field coupling simulation model and output the simulated capacitance spectrum matrix, specifically including: Step S141: Input the intrinsic attribute parameters of the target device to be acquired into the simulation software; Step S142: Obtain the geometric parameters of the target device based on the drift-diffusion equations (DDE) in the simulation software; Step S143: Obtain initial ion migration parameters based on the Nernst-Planck (NP) equation in the simulation software; Step S144: Obtain the initial trap state parameters based on the Shockley-Read-Hall (SRH) model in the simulation software; Step S145: Input the high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude into the simulation software for parameter constraints, so as to construct a physical field coupling simulation model and output the simulation capacitance spectrum matrix.

[0058] Specifically, this embodiment addresses the problem that existing perovskite device internal information characterization methods rely solely on single-dimensional information such as ion migration or defect states for characterization analysis, making it difficult to uniformly analyze the coupling effect of ion migration and trapping in low-frequency capacitors. Furthermore, by using characteristic parameters as constraints, it narrows the parameter optimization range, improving the efficiency and accuracy of joint quantitative extraction of ion migration and defect state parameters. This provides data support for subsequent comparison of actual and simulated data, and for decoupling the coupling effect of ion migration and trapping in the low-frequency band using a precise and unified physical field coupling simulation model. It serves as the core bridge for achieving joint characterization of ion migration and defect states.

[0059] In addition, the intrinsic property parameters of the target device are obtained in advance based on the target perovskite device to be characterized through previous material characterization, such as X-ray diffraction, ultraviolet-visible absorption spectroscopy, Hall effect testing, etc., or device structure measurement, such as thickness measurement by profilometer and effective area measurement by microscope.

[0060] The simulation software can be Sentaurus TCAD (Sentaurus Semiconductor Process Device Simulation Software), COMSOL Multiphysics (Commodo Physics Simulation Software), or numerical calculation programs independently developed using programming languages ​​such as MATLAB and Python to establish multiphysics coupling control equation models, etc. This invention is not limited to these; any simulation software that can achieve the same technical effect as this invention is within the protection scope of this invention.

[0061] In one specific embodiment, before obtaining the geometric parameters of the target device based on the DDE equations in the simulation software in step S142, the following steps are included: Based on the Arrhenius Relationship in simulation software and the capacitive relaxation inflection point frequency Calculate the depth of the trap level : In the formula: Represented as the escape frequency factor, It is expressed as an exponential function with the natural constant e as the base; Input the actual capacitance value C(f,V) into the simulation software to calculate the total density of trapped states. : In the formula: Represented as built-in potential, This represents the width of the depletion layer.

[0062] Specifically, the depth of the trap energy level and trap state density It can support the quantitative analysis of defect state parameters, providing a core physical basis for subsequent integration with ion migration parameters.

[0063] In one specific embodiment, in step S142, the geometric parameters of the target device are obtained based on the DDE equations in the simulation software, specifically including: Step S1421: Calculate the electron current density using the DDE equations in the simulation software. : In the formula: Expressed as electron carrier mobility; Expressed as electron concentration; Expressed as electric field strength; Expressed as the electron carrier diffusion coefficient; Represented as the spatial gradient of electron concentration n; Step S1422: Calculate the hole current density using the DDE equations in the simulation software. : In the formula: Represented as hole carrier mobility; This is expressed as hole concentration; It is expressed as the hole carrier diffusion coefficient; Represented as the spatial gradient of hole concentration p; Step S1423: Calculate electronic continuity using the DDE equations in the simulation software. : In the formula: Expressed as the photogenerated carrier generation rate; This is represented as the first composite rate; Step S1424: Calculate hole continuity using the DDE equations in the simulation software. : In the formula: This is represented as the second composite rate; Step S1425: Calculate the spatial potential distribution within the target device using the DDE equations in the simulation software. In the formula: Expressed as dielectric constant, Expressed as electrostatic potential, Expressed as ionized donor concentration, Expressed as ionization acceptor concentration, Represented as the concentration of the trap state, This is expressed as the concentration of electrons trapped in the trap. Among them, the geometric parameters of the target device include electron current density. Hole current density Electronic continuity Hole continuity And the spatial electric potential distribution.

[0064] Specifically, obtaining the geometric parameters of the target device can describe the transport, recombination, and space charge distribution of electrons and holes in the perovskite layer and transport layer of the target device. It is the basic input for constructing a physical field coupling simulation model, and provides a simulation benchmark that conforms to the real physical process of the target device for subsequent optimization of ion migration and trap state parameters, thereby ensuring the accuracy and reliability of the joint characterization results.

[0065] In addition, the first compound rate Second composite rate The same can be either the radiation recombination rate or the Auger recombination rate, etc. This invention is not limited to these. Any recombination rate that can be called in the simulation software and can achieve the same technical effect as this invention is within the protection scope of this invention.

[0066] In one specific embodiment, step S143 involves obtaining initial ion migration parameters based on the NP equation in the simulation software, specifically including: Step S1431: Calculate the ion diffusion coefficient using the NP equations in the simulation software. : In the formula: Expressed as ion mobility; Expressed as ionic charge number; Step S1432: Calculate the concentration of mobile ions using the NP equations in the simulation software. : In the formula: Represented as Faraday constant, Among them, the initial ion mobility parameters include ion mobility. ion diffusion coefficient Migratory ion concentration and ion activation energy .

[0067] Specifically, the initial ion migration parameters are the basic input for constructing the physical field coupling simulation model. They provide a reasonable starting range for subsequent parameter optimization and ensure that the physical field coupling simulation model can accurately describe the migration, accumulation and diffusion process of mobile ions under an external electric field. This provides core support for decoupling the coupling signals of ion migration and trapping in low-frequency capacitors and realizing joint quantitative characterization.

[0068] Furthermore, referencing publicly available experimental data and theoretical calculations in the field of perovskite devices, a reasonable estimation range is provided for the calculation of initial ion mobility parameters in the simulation software, thus providing an initial search range that conforms to physical laws through parameter optimization. Among these parameters is ion mobility. The estimated range is ; Concentration of mobile ions The estimated range is The estimated range for ion activation energy is 0.1–0.6 eV; ion diffusion coefficient The estimation range is limited by the Einstein Relation in the simulation software.

[0069] It is worth noting that, In one specific embodiment, step S144 involves obtaining initial trap state parameters based on the SRH model in the simulation software, specifically including: Step S1441: Calculate the net compound rate using the SRH model in the simulation software. : In the formula: Expressed as intrinsic carrier concentration; and All include minority lifespan. in, , In the formula: and These are represented as the effective density of states in the conduction band and the effective density of states in the valence band, respectively. and These are represented as the bottom energy level of the conduction band and the top energy level of the valence band, respectively. Step S1442: Calculate the electron capture cross section using the SRH model in the simulation software. : In the formula: Expressed as electron emissivity; Expressed as thermal velocity; and cavity capture section : In the formula: Represented as hole emission rate; Based on the Gaussian distribution model in the simulation model, the energy distribution width is calculated. : In the formula: This is expressed as the density of trapped states at energy E. The initial trap state parameters include the trap energy level depth. Electron capture section Hole trapping section Total density of trap states and the width of energy distribution .

[0070] Specifically, the initial trap state parameters accurately reproduce the trapping, emission, and recombination processes of charge carriers within the target device in the simulation software. This provides data support for decoupling the trap state response in the intermediate frequency capacitor signal and for constructing a physical field coupling simulation model. It is a key step in realizing the joint characterization of ion migration and defect states.

[0071] Furthermore, referencing publicly available experimental data and theoretical calculations in the field of perovskite devices, a reasonable estimation range is provided for the calculation of initial trap state parameters in the simulation software, thus providing an initial search range that conforms to physical laws through parameter optimization. Among these parameters is the trap energy level depth. The estimated range is 0.1~0.8 eV; electron capture cross-section and cavity capture section The estimation range is Total density of trap states The estimation range is Energy distribution width The estimated range is 0.05~0.3eV.

[0072] In one specific embodiment, in step S145, the high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude are input into the simulation software for parameter constraints to construct a physical field coupling simulation model and output the simulated capacitance spectrum matrix, specifically including: Step S1451: Input the high-frequency capacitance value, low-frequency capacitance value, capacitor relaxation inflection point frequency, and capacitor step amplitude into the simulation software for parameter constraints to construct a physical field coupling simulation model. Step S1452: Input the simulated bias voltage sequence, simulated frequency range, simulated values ​​of AC disturbance voltage, and simulated filter order into the physical field coupling simulation model to output the simulated capacitor spectrum matrix.

[0073] Specifically, the simulated bias sequence, simulated frequency range, and simulated filter order are the same as the preset bias sequence, preset frequency range, and actual filter order in the actual experiment, while the simulated value of the AC disturbance voltage is... satisfy: In the formula: Represented as the imaginary unit of the bias scan; This is represented as the scan time.

[0074] This embodiment is based on the principle of impedance Z described above, combined with the complex impedance value. Calculate the complex admittance by taking the reciprocal of the complex number. : Similarly to step S112 above, the simulation software uses complex admittance... Obtain the simulated capacitance value and output the simulated capacitance spectrum matrix.

[0075] Complex admittance With complex impedance value Essentially, they are complementary descriptions of the same circuit characteristics. The choice is made using complex impedance values. Calculating the reciprocal of complex admittance This is because the imaginary part of the complex admittance can directly correspond to the capacitance characteristics, thus allowing for a more intuitive extraction of the simulated capacitance value from the simulation model. : To simplify the process of matching simulation data with experimental data.

[0076] In one embodiment, in step S15, the actual capacitance spectrum matrix is ​​optimized based on a parameter optimization algorithm. The comparison is performed with the simulated capacitance spectrum matrix. The difference is then input into the parameter optimization function for iterative calculation. When the parameter optimization function converges to a preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters and optimized defect state parameters are output, specifically including: Step S151: Analyze the actual capacitance spectrum matrix using the least squares method. The difference between the simulated capacitor spectrum matrix and the actual capacitor spectrum matrix is ​​obtained. Step S152: Input the comparison difference into the parameter optimization objective function, and output the optimized ion migration parameters and optimized defect state parameters when the parameter optimization objective function converges to a preset threshold or reaches the maximum number of iterations.

[0077] Specifically, the least squares method and the parameter optimization objective function are both callable algorithms inherent in the simulation software, and the principles of these algorithms will not be elaborated further in this invention.

[0078] In one specific embodiment, in step S152, the objective function for optimizing the comparison difference input parameters is used to output optimized ion migration parameters and optimized defect state parameters when the parameter optimization objective function converges to a preset threshold or reaches the maximum number of iterations. Specifically, this includes: By comparing the input parameters of the difference and optimizing the objective function, we obtain: In the formula: Represented as a simulated capacitor spectrum matrix; This is expressed as measurement uncertainty; When When convergence reaches a preset threshold or the maximum number of iterations is reached, the optimized ion migration parameters and optimized defect state parameters are output. Among them, optimizing ion migration parameters includes optimizing ion mobility, optimizing ion diffusion coefficient, optimizing the concentration of mobile ions, and optimizing ion activation energy; optimizing defect state parameters includes optimizing trap level depth, optimizing electron trapping cross section, optimizing hole trapping cross section, optimizing total trap state density, and optimizing energy distribution width.

[0079] Specifically, the parameter optimization process in this embodiment breaks through the limitation of the ambiguity of the physical meaning of traditional equivalent circuit fitting, and realizes the simultaneous decoupling and extraction of multi-dimensional information on ion migration and defect states from a single test data. It completely solves the problem that existing technologies can only obtain single-dimensional information and cannot uniformly analyze coupling effects, providing accurate quantitative basis for material modification, interface optimization and stability improvement of perovskite devices.

[0080] It is worth noting that the relationship between optimized ion mobility and optimized ion migration activation energy can be expressed based on the Arrhenius equation as follows: In the formula: This is expressed as optimized ion mobility; This is represented as the optimized activation energy for ion migration.

[0081] In one specific embodiment, in step S152, the objective function for optimizing the comparison difference input parameters is optimized. When the objective function converges to a preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters and optimized defect state parameters are output. This also includes: When the objective function for parameter optimization converges to a preset threshold or reaches the maximum number of iterations, the confidence intervals for the optimized ion migration parameters and the confidence intervals for the optimized defect state parameters are output.

[0082] Specifically, optimizing the confidence intervals of ion migration parameters and defect state parameters provides a quantitative supplement to the reliability of the joint characterization results, offering a more rigorous basis for subsequent device optimization.

[0083] It is worth noting that the confidence intervals for optimizing ion mobility parameters include: the confidence intervals for optimizing ion mobility, optimizing ion diffusion coefficient, and optimizing migrateable ion concentration. And the confidence interval for optimizing ion activation energy is .

[0084] The confidence intervals for optimizing defect state parameters include: the confidence interval for optimizing trap level depth is... The confidence interval for optimizing the electron capture cross section is: The confidence interval for optimizing the hole trapping cross section is as follows: The confidence interval for optimizing the total density of trapped states is: And the confidence interval for optimizing the energy distribution width is .

[0085] It is worth noting that parameters not further described in the above embodiments of the present invention, such as elementary charge q and measurement uncertainty, are not included. These are all well-known parameters in the prior art or parameters that can be obtained inherently in simulation software. The specific acquisition principle of these parameters will not be elaborated further in this invention.

[0086] The embodiments of the present invention will now be described in conjunction with specific examples 1-7: Example 1 In Embodiment 1 of the present invention, the target device is a planar perovskite solar cell with an area of ​​0.1 cm². 2 It comprises, from bottom to top, a transparent conductive glass substrate, an electron transport layer, a perovskite light absorption layer, a hole transport layer, and a metal top electrode. The electron transport layer has a thickness of 30 nm, the perovskite light absorption layer has a thickness of 500 nm, the hole transport layer has a thickness of 200 nm, and the metal top electrode has a thickness of 80 nm.

[0087] Embodiment 1, implemented based on the above embodiments, further includes step S23: In one embodiment, in step S23, based on the actual spectrum curve of each bias point, the corresponding high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude are obtained under a preset frequency division rule. Specifically, this includes: Step S231: Based on the preset frequency division rules, identify the high-frequency plateau segments in the actual spectrum curve where all frequencies are greater than 100 kHz, to obtain the high-frequency capacitance plateau interval, and select a high-frequency capacitance value of 1.4 × 10⁻⁶ within it. -8 F / cm²; Step S232: Identify the low-frequency plateau segment of the curve in the actual spectrum curve where all frequencies are less than 0.1Hz, to obtain the low-frequency capacitance plateau interval, and select a low-frequency capacitance value of 1.2×10 within it. -7 F / cm²; Step S233: Identify the inflection point frequency range of the transition from the high-frequency plateau segment of the curve to the low-frequency plateau segment of the curve, and extract the frequency that is the same as 50Hz within the inflection point frequency range and define it as the capacitor relaxation inflection point frequency. Step S134: Based on the difference between the low-frequency capacitance value and the high-frequency capacitance value, obtain the capacitance step amplitude as 1.1 × 10⁻⁶. - 7 F / cm².

[0088] Embodiment 1, implemented based on the above embodiments, further includes step S25: In one embodiment, in step S25, the actual capacitance spectrum matrix is ​​optimized based on a parameter optimization algorithm. The comparison is performed with the simulated capacitance spectrum matrix. The difference is then input into the parameter optimization function for iterative calculation. When the parameter optimization function converges to a preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters and optimized defect state parameters are output, specifically including: Step S251: Analyze the actual capacitance spectrum matrix using the least squares method. The difference between the simulated capacitor spectrum matrix and the actual capacitor spectrum matrix is ​​obtained. Step S252: Input the comparison difference into the parameter optimization objective function, so that when the parameter optimization objective function converges to a preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters are output, including the optimized initial concentration of migrateable ions of 6.5 × 10⁻⁶. 16 cm -3 The optimized ion mobility is 3.8 × 10⁻⁶. -11 The optimized ion diffusion coefficient is 9.8 × 10 cm² / Vs. -13 cm² / s and the optimized ion migration activation energy is 0.32 eV. Optimizing defect state parameters includes optimizing the total trap density to 8.3 × 10⁻⁶. 15 cm -3 The optimized trap center energy level is 0.42 eV, the optimized energy distribution width is 0.18 eV, and the optimized electron capture cross section is 1.2 × 10⁻⁶. -15 cm² and an optimized hole trapping cross section of 3.5 × 10 cm². -16 cm².

[0089] As can be seen from Example 1, the present invention can achieve unified analysis of the coupling effect of ion migration and trapping based on the comparison of the actual capacitance spectrum matrix and the simulated capacitance spectrum matrix, combined with the parameter optimization objective function, and simultaneously extract the ion migration parameters and trap state parameters in a joint quantitative manner, which is conducive to promoting the development of joint characterization technology of ion migration and defect state of perovskite devices.

[0090] Example 2 The difference between Embodiment 2 of the present invention and Embodiment 1 above is that the target device is a ternary cation perovskite device.

[0091] Embodiment 2, implemented based on the above embodiments, further includes step S33: In one embodiment, in step S33, based on the actual spectrum curve of each bias point, the corresponding high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude are obtained under a preset frequency division rule. Specifically, this includes: Step S331: Based on the preset frequency division rules, identify the high-frequency plateau segment of the curve in the actual spectrum curve where the frequency is greater than 100 kHz, so as to obtain the high-frequency capacitance plateau interval and select a high-frequency capacitance value of 1.5E-8 F / cm² within it. Step S332: Identify the low-frequency plateau segment of the curve in the actual spectrum curve where all frequencies are less than 1Hz, to obtain the low-frequency capacitance plateau interval and select a low-frequency capacitance value of 5.5E-8 F / cm² within it; Step S333: Identify the inflection point frequency range where the curve transitions from the high-frequency plateau segment to the low-frequency plateau segment, and extract the frequency that is the same as 50Hz within the inflection point frequency range and define it as the capacitor relaxation inflection point frequency. Step S334: Based on the difference between the low-frequency capacitance value and the high-frequency capacitance value, the capacitance step amplitude is obtained as 4.0E-8 F / cm².

[0092] Embodiment 2, implemented based on the above embodiments, further includes step S35: In one embodiment, in step S35, the actual capacitance spectrum matrix is ​​optimized based on a parameter optimization algorithm. The comparison is performed with the simulated capacitance spectrum matrix. The difference is then input into the parameter optimization function for iterative calculation. When the parameter optimization function converges to a preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters and optimized defect state parameters are output, specifically including: Step S351: Analyze the actual capacitance spectrum matrix using the least squares method. The difference between the simulated capacitor spectrum matrix and the actual capacitor spectrum matrix is ​​obtained. Step S352: Input the comparison difference into the parameter optimization objective function, so that when the parameter optimization objective function converges to a preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters are output, including the optimized initial concentration of migrateable ions of 1.2 × 10⁻⁶. 16 cm -3 The optimized ion mobility is 3.8 × 10⁻⁶. -12 The optimized ion diffusion coefficient is 9.8 × 10 cm² / Vs. -13 cm² / s and the optimized ion migration activation energy is 0.48 eV. Optimizing defect state parameters includes optimizing the total trap density to 2.8 × 10¹ 5 cm -3 The trap center energy level was optimized to 0.30 eV, the energy distribution width was optimized to 0.10 eV, the electron trapping cross section was optimized to 1.2 × 10⁻¹⁵ cm², and the hole trapping cross section was optimized to 3.5 × 10⁻¹⁶ cm².

[0093] As can be seen from Example 2, compared with Example 1, the deep-level defects and interface polarization effects in Example 2 are weakened, thus demonstrating that the joint characterization and optimization method of ion migration and defect state of the present invention can quantitatively distinguish the differences in defect state and ion migration characteristics of perovskite systems with different components, and provide parameter guidance for multi-component optimization.

[0094] Example 3 The difference between Embodiment 3 of the present invention and Embodiment 1 above is that the thickness of the perovskite layer of the target device is 300 nm, and the optimized total trap density is 9.1 × 10¹. 5 cm -3 The optimized activation energy for ion migration is 0.26 eV.

[0095] As can be seen from Example 3, compared with Example 1, the ion migration and interface charge accumulation effects of the target device in Example 3 are more significant when the thickness is 300nm.

[0096] Example 4 The difference between Embodiment 4 of the present invention and Embodiment 1 above is that the thickness of the perovskite layer of the target device is 500 nm, and the optimized total trap density is 8.3 × 10¹. 5 cm -3 The optimized activation energy for ion migration is 0.32 eV.

[0097] As can be seen from Example 4, compared with Examples 1 and 3, the defect concentration and ion activation energy in Example 4 are consistent with those in Example 1, which verifies the reproducibility of the method and characterization results of the present invention. At the same time, compared with Example 3, it further confirms that the increase in perovskite layer thickness leads to an increase in ion migration activation energy and a decrease in the interface charge accumulation effect, providing a quantitative basis for the thickness optimization of perovskite devices.

[0098] Example 5 The difference between Embodiment 5 of the present invention and Embodiment 1 is that: Embodiment 1 involves a bias voltage test performed at room temperature, while in Embodiment 5, the absolute temperature T during the bias voltage test is 25°C. As shown in Example 5, compared to Example 1, the absolute temperature T in the bias test of Example 5 was precisely controlled to 25°C using a temperature control station. The remaining device structures, test parameters, and optimization procedures were exactly the same as in Example 1. Furthermore, the optimized parameters under precise temperature control of 25°C in Example 5 were consistent with the results under room temperature conditions in Example 1, verifying that the method of the present invention is not sensitive to small fluctuations in test temperature and has good experimental repeatability. Simultaneously, Example 5 provides accurate low-temperature reference data points for the subsequent variable-temperature series tests in Examples 6 and 7.

[0099] Example 6 The difference between Embodiment 6 of the present invention and Embodiment 1 above is that the absolute temperature T in the bias test is 45°C, the optimized ion mobility is increased to 2.2 times that of Embodiment 1, and the capacitance relaxation inflection point frequency is 110Hz. As can be seen from Example 6, compared with Example 1 and Example 5, after the temperature was increased to 45°C in Example 6, the optimized ion mobility increased by 2.2 times, and the capacitance relaxation inflection point frequency shifted from 50 Hz to about 110 Hz. This indicates that the ion migration process has significant Arrhenius thermal activation characteristics, and the increase in temperature accelerates the migration and interface accumulation rate of ions in the perovskite layer.

[0100] Example 7 The difference between Embodiment 7 of the present invention and Embodiment 1 above is that the absolute temperature T in the bias test is 65°C, and the optimized ion mobility is 2.1 × 10¹. 7 cm -3 .

[0101] As shown in Example 7, compared to Examples 1, 5, and 6, the optimized ion mobility obtained in Example 7 under the test condition of 65°C is further improved compared to the lower temperature test results of Examples 1, 5, and 6. Meanwhile, the optimized ion migration activation energy, as an intrinsic property of the material, is completely consistent with the test results of Example 1. Furthermore, by performing Arrhenius linear fitting on the optimized ion mobility measured under the three sets of varying temperature conditions in Examples 5, 6, and 7, the results show that the three sets of optimized ion mobilities fully conform to the Arrhenius-type intrinsic temperature dependence law of ion mobility. Moreover, the ion migration activation energy extracted by the fitting is in high agreement with the results obtained by the already recognized single-temperature joint inversion method, fully verifying the accuracy and reliability of extracting the ion migration activation energy using the method of the present invention.

[0102] While one or more embodiments of the present invention have been described above, those skilled in the art will recognize that the present invention can be implemented in any other form without departing from its spirit and scope. Therefore, the embodiments described above are illustrative and not restrictive, and many modifications and substitutions will be apparent to those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A method for joint characterization and optimization of ion migration and defect states in perovskite devices, characterized in that, include: At each bias point in the preset bias sequence, the complex impedance value of the target device at each frequency point in the preset frequency range is collected and the actual capacitance value is calculated to generate an actual capacitance spectrum matrix that includes the correspondence between bias, frequency and actual capacitance value. Based on the actual capacitance spectrum matrix, the frequency sequence and actual capacitance value corresponding to each bias point are extracted, and the actual spectrum curve of each bias point is constructed according to the frequency logarithm of each frequency point and the actual capacitance value. Based on the actual spectrum curve of each bias point, the corresponding high-frequency capacitance value, low-frequency capacitance value, capacitor relaxation inflection frequency, and capacitor step amplitude are obtained under the preset frequency division rules. The intrinsic property parameters of the target device are pre-acquired and input into the simulation software to obtain the geometric parameters, initial ion migration parameters and initial trap state parameters of the target device. The parameters are constrained by high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection frequency and capacitance step amplitude. A physical field coupling simulation model is constructed and the simulated capacitance spectrum matrix is ​​output. The actual capacitance spectrum matrix and the simulated capacitance spectrum matrix are compared based on the parameter optimization algorithm. The difference between the comparison is input into the parameter optimization function for iterative calculation. When the parameter optimization function converges to a preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters and optimized defect state parameters are output.

2. The method for joint characterization and optimization of ion migration and defect states of perovskite devices according to claim 1, characterized in that, The process involves acquiring the complex impedance value of the target device at each frequency point within a preset frequency range at each bias point in the preset bias sequence, calculating the actual capacitance value, and generating an actual capacitance spectrum matrix that includes the correspondence between bias, frequency, and actual capacitance value. Specifically, this includes: A preset bias sequence is set using an impedance analyzer, and at each bias point in the preset bias sequence, the complex impedance value of the target device at each frequency point from 1mHz to 5MHz is acquired based on logarithmic scanning. Based on the complex impedance value, the actual capacitance value of the target device at each frequency point is calculated using the complex impedance formula. It also generates an actual capacitance spectrum matrix that includes the correspondence between bias voltage, frequency, and actual capacitance value.

3. The method for joint characterization and optimization of ion migration and defect states of perovskite devices according to claim 2, characterized in that, The process of acquiring the complex impedance values ​​of the target device at each frequency point from 1 to 5 MHz using a logarithmic scan at each bias point in a preset bias sequence, while simultaneously setting the impedance using an impedance analyzer: The effective value of the AC disturbance voltage of the impedance analyzer is set by the control equipment; The current and voltage signal demodulators of the impedance analyzer are then turned on to perform synchronous phase-locked loop amplification and detection on the target device, and the filter order is set to be greater than the order threshold.

4. The method for joint characterization and optimization of ion migration and defect states of perovskite devices according to claim 3, characterized in that, The process of extracting the frequency sequence and actual capacitance value corresponding to each bias point based on the actual capacitance spectrum matrix, and constructing the actual spectrum curve of each bias point based on the frequency logarithm and actual capacitance value, specifically includes: Extract the frequency sequence and actual capacitance value corresponding to each bias point based on the actual capacitance spectrum matrix; A logarithmic coordinate system is constructed with the logarithm of the frequency points in the frequency sequence as the abscissa and the actual capacitance value as the ordinate. The actual spectrum curve of each bias point is constructed based on the logarithm of the frequency points and the corresponding actual capacitance value.

5. The method for joint characterization and optimization of ion migration and defect states of perovskite devices according to claim 4, characterized in that, The step of obtaining the corresponding high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude based on the actual spectrum curve of each bias point under a preset frequency division rule specifically includes: Based on the preset frequency division rules, identify the high-frequency plateau segment of the curve in the actual spectrum curve where the frequency is greater than the pre-acquired high-frequency threshold, so as to obtain the high-frequency capacitance plateau interval and select the high-frequency capacitance value within it. Identify the low-frequency plateau segment of the curve in the actual spectrum curve where all frequencies are lower than the pre-acquired low-frequency threshold, in order to obtain the low-frequency capacitance plateau interval and select the low-frequency capacitance value within it. Identify the inflection point frequency range where the curve transitions from a high-frequency plateau segment to a low-frequency plateau segment, and extract the frequency within the inflection point frequency range that is the same as the pre-acquired inflection point threshold, defining it as the capacitive relaxation inflection point frequency. The capacitance step amplitude is obtained based on the difference between the low-frequency capacitance value and the high-frequency capacitance value.

6. The method for joint characterization and optimization of ion migration and defect states of perovskite devices according to claim 5, characterized in that, The process involves pre-acquiring intrinsic attribute parameters of the target device and inputting them into simulation software to obtain the target device's geometric parameters, initial ion migration parameters, and initial trap state parameters. Parameter constraints are then applied using high-frequency capacitance values, low-frequency capacitance values, capacitance relaxation inflection point frequencies, and capacitance step amplitudes. A physical field coupling simulation model is constructed, and the simulated capacitance spectrum matrix is ​​output. Specifically, this includes: Input the intrinsic attribute parameters of the target device to be acquired into the simulation software; Geometric parameters of the target device are obtained based on the DDE equations in the simulation software. Initial ion migration parameters are obtained based on the NP equations in simulation software; Initial trap state parameters are obtained based on the SRH model in simulation software; The high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude are input into the simulation software for parameter constraints in order to construct a physical field coupling simulation model and output the simulated capacitance spectrum matrix.

7. The method for joint characterization and optimization of ion migration and defect states of perovskite devices according to claim 6, characterized in that, The process of inputting high-frequency capacitance values, low-frequency capacitance values, capacitance relaxation inflection point frequency, and capacitance step amplitude into simulation software for parameter constraints to construct a physical field coupling simulation model and output the simulated capacitance spectrum matrix specifically includes: The high-frequency capacitance value, low-frequency capacitance value, capacitance relaxation inflection point frequency, and capacitance step amplitude are input into the simulation software for parameter constraints in order to construct a physical field coupling simulation model. Input the simulated bias voltage sequence, simulated frequency range, simulated values ​​of AC disturbance voltage, and simulated filter order into the physical field coupling simulation model to output the simulated capacitor spectrum matrix.

8. The method for joint characterization and optimization of ion migration and defect states of perovskite devices according to claim 7, characterized in that, The parameter optimization algorithm compares the actual capacitance spectrum matrix and the simulated capacitance spectrum matrix, and inputs the difference into the parameter optimization function for iterative calculation. When the parameter optimization function converges to a preset threshold or reaches the maximum number of iterations, it outputs optimized ion migration parameters and optimized defect state parameters. Specifically, this includes: The actual capacitor spectrum matrix and the simulated capacitor spectrum matrix are compared and the comparison difference is obtained based on the least squares method. The objective function is optimized by comparing the input parameters of the difference, so that when the objective function converges to a preset threshold or reaches the maximum number of iterations, the optimized ion migration parameters and optimized defect state parameters are output.

9. The method for joint characterization and optimization of ion migration and defect states of perovskite devices according to claim 8, characterized in that, The step of comparing the input parameters to optimize the objective function, and outputting optimized ion migration parameters and optimized defect state parameters when the objective function converges to a preset threshold or reaches the maximum number of iterations, specifically includes: By comparing the input parameters of the difference and optimizing the objective function, we obtain: In the formula: Represented as a simulated capacitor spectrum matrix; This is expressed as measurement uncertainty; When When convergence reaches a preset threshold or the maximum number of iterations is reached, the optimized ion migration parameters and optimized defect state parameters are output. Among them, optimizing ion migration parameters includes optimizing ion mobility, optimizing ion diffusion coefficient, optimizing the concentration of mobile ions, and optimizing ion activation energy; optimizing defect state parameters includes optimizing trap level depth, optimizing electron trapping cross section, optimizing hole trapping cross section, optimizing total trap state density, and optimizing energy distribution width.

10. The method for joint characterization and optimization of ion migration and defect states of perovskite devices according to claim 9, characterized in that, The step of optimizing the objective function by comparing the input parameters of the difference, and outputting optimized ion migration parameters and optimized defect state parameters when the objective function converges to a preset threshold or reaches the maximum number of iterations, also includes: When the objective function for parameter optimization converges to a preset threshold or reaches the maximum number of iterations, the confidence intervals for the optimized ion migration parameters and the confidence intervals for the optimized defect state parameters are output.

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