A method and system for predicting the evolution of the dielectric properties of metasurface materials in a space radiation environment

Abstract

The present application relates to the technical field of super surface material performance prediction, aiming at solving the problems of narrow applicability, low precision and lack of dynamic feedback of existing prediction methods, and proposes a super surface material dielectric property evolution prediction method and system under space radiation environment. The method is: constructing a dynamically updated physical prior knowledge base; constructing a multi-field coupling calculation module, based on the knowledge base, carrying out radiation transport field, defect-carrier evolution field and dielectric field calculation, and at least establishing a dynamic feedback mechanism of defect-carrier evolution field and dielectric field; the output results of the module are visualized; based on experimental data, the multi-field coupling calculation results are verified and optimized, the parameters of the calculation module are calibrated, and the verified parameters and experimental data are stored in the physical prior knowledge base, realizing the dynamic updating of the physical prior knowledge base and the iterative optimization of the calculation module. The present application is suitable for the anti-radiation design and performance life evaluation of super surface devices in space environment such as aerospace and satellite.

Description

Technical Field

[0001] This invention relates to the field of metasurface material performance prediction technology, specifically to a method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment based on physical priors and multi-field coupling. Background Technology

[0002] Metasurface materials, as artificial electromagnetic materials composed of periodically arranged subwavelength units, have broad application prospects in fields such as space communication and remote sensing due to their ability to precisely control the phase, amplitude, and polarization of electromagnetic waves. However, the space environment contains complex radiation fields (including ionizing radiation such as protons, heavy ions, and electrons, as well as non-ionizing radiation such as terahertz and infrared radiation). The radiation energy interacts with metasurface materials (such as silicon-based nanopillar-silica substrate metasurfaces and metal-dielectric composite metasurfaces), triggering the following microscopic physical changes: Defect generation: Energy deposition from ionizing radiation causes lattice atoms in metasurface functional layers (such as silicon) to shift, generating point defects such as vacancies and interstitial atoms, or defect aggregation to form composite defects (such as double vacancies and vacancy-doped atom complexes). Carrier evolution: Radiative ionization excites electron-hole pairs, and defects act as carrier traps to capture free carriers, leading to dynamic changes in carrier concentration and mobility; Dielectric property degradation: The aforementioned microscopic changes alter the polarization response of metasurface materials (such as electronic polarization and lattice polarization), thereby leading to a decrease in the real part of the dielectric constant. Energy storage characteristics) offset, imaginary part ( Increased loss characteristics ultimately lead to a decrease in the electromagnetic control performance of metasurfaces (such as beam pointing accuracy and reflection efficiency).

[0003] Therefore, accurately predicting the evolution of dielectric properties of metasurface materials under space radiation environments is a core prerequisite for ensuring the rational design and accurate assessment of the service life of metasurface devices, and is therefore extremely necessary. This is because once metasurface devices are put into use in space scenarios, on-site maintenance and replacement are difficult. If the evolution trend of dielectric properties with radiation dose and service time cannot be predicted, it will lead to excessive redundancy in device design (resulting in cost waste) or insufficient design (causing premature failure). At the same time, it will fail to provide a reliable basis for life assessment, thereby affecting the operational stability of the entire space system.

[0004] Existing methods for predicting the dielectric properties of metasurfaces have the following key shortcomings: 1. Lack of prior physical support: Existing models mostly rely on empirical formulas (such as linear fitting of the relationship between radiation dose and dielectric constant), without incorporating prior physical information such as material intrinsic radiation response parameters (such as defect formation energy and carrier trapping cross section) and metasurface structure parameters (such as unit cell period and functional layer thickness), resulting in narrow applicability of the models (only suitable for specific materials / radiation types) and poor extrapolation ability. 2. Insufficient multi-field coupling: The effect of space radiation on metasurfaces is a multi-physics coupling process of "radiative transport field - defect - carrier evolution field - electromagnetic dielectric field". Existing methods mostly use single-physics field simulation (such as simulating radiative transport only through Monte Carlo (MC) or calculating defect energy levels only through density functional theory (DFT)). They have not established a dynamic feedback mechanism between fields and cannot accurately capture the dynamic evolution of dielectric properties with radiation dose. 3. Poor adaptability across the full dose range: low dose ( Under these conditions, the change in dielectric properties is linearly related to the dose, and at high doses ( Due to defect saturation and carrier recombination equilibrium, the dielectric properties tend to stabilize. Existing models do not have differentiated calculation modules for different dose ranges, resulting in prediction errors exceeding 20% ​​in the high-dose region. 4. Lack of verification mechanism: The existing model does not have a closed-loop verification module of "computation-experiment", and cannot correct model parameters (such as defect generation efficiency and carrier relaxation time) in real time through experimental data, which leads to a gradual decrease in prediction accuracy over time.

[0005] Therefore, the core shortcomings of existing metasurface dielectric property prediction methods can be summarized into three categories: narrow applicability, low accuracy, and lack of dynamic feedback.

[0006] Therefore, this invention integrates physical priors to achieve a metasurface dielectric property evolution prediction model that is deeply coupled across multiple fields and covers the entire dose range, in order to meet the design and lifetime assessment requirements of metasurface devices in space environments. Summary of the Invention The purpose of this invention is to provide a method for predicting the evolution of dielectric properties of metasurface materials under space radiation conditions. This method achieves the prediction of the metasurface dielectric constant by constructing a physical prior knowledge base and a multi-field coupled calculation module. , With accurate prediction of radiation dose, it solves the problems of narrow applicability, low accuracy and lack of dynamic feedback of existing methods.

[0007] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for predicting the evolution of dielectric properties of metasurface materials under space radiation conditions, the method comprising the following steps: Step 1: Construct a dynamically updatable physical prior knowledge base; Step 2: Construct a multi-field coupling calculation module, and perform radiation transport field calculation, defect-carrier evolution field calculation, and dielectric field calculation based on the physical prior knowledge base, and establish at least a dynamic feedback mechanism between the defect-carrier evolution field and the dielectric field. Step 3: Process and visualize the results output by the multi-field coupling calculation module; Step 4: Verify and optimize the multi-field coupling calculation results based on experimental data. By calibrating the calculation module parameters, the verified parameters and experimental data are stored in the physical prior knowledge base, thereby realizing the dynamic updating of the physical prior knowledge base and the iterative optimization of the calculation module.

[0008] Furthermore, the aforementioned physical prior knowledge base includes the following sub-bases: A sub-library of intrinsic material parameters is used to store the radiation response parameters of metasurface functional layers and substrates; A sub-library of space radiation parameters is used to store typical space radiation environment parameters, including radiation type and particle energy. The metasurface structure parameter sub-library is used to store the geometric parameters of metasurface units, including unit period, functional layer thickness, and unit fill rate. The experimental calibration sub-library stores experimental measurement data of metasurface dielectric properties under different radiation conditions, which is used for model parameter correction.

[0009] Furthermore, the aforementioned radiation types include protons, heavy ions, and electrons.

[0010] Furthermore, the aforementioned multi-field coupling calculation module includes a radiation transport field calculation submodule, a defect-carrier evolution field calculation submodule, and an electromagnetic dielectric field calculation submodule; The radiation transport field calculation submodule is used to simulate the transport process of space radiation particles in the metasurface and calculate the energy deposition distribution, initial defect concentration and initial carrier concentration. The defect-carrier evolution field calculation submodule is used to simulate radiation-induced defect dynamic evolution and carrier evolution based on the initial defect concentration and the initial carrier concentration, output the final defect concentration and carrier concentration, and is also used to receive dielectric property data fed back by the electromagnetic dielectric field calculation submodule to correct the carrier trapping cross section. The electromagnetic dielectric field calculation submodule is used to calculate the equivalent dielectric constant of the metasurface based on the final defect concentration and carrier concentration distribution. It is also used to feed back the dielectric property data to the defect-carrier evolution field to optimize the defect mobility parameters.

[0011] Furthermore, the aforementioned radiation transport field calculation submodule calculates the three-dimensional energy deposition distribution, initial defect concentration, and initial carrier concentration by inputting radiation parameters and metasurface structure parameters from the physical prior knowledge base; The defect-carrier evolution field calculation submodule includes defect evolution and carrier evolution; The defect evolution is as follows: by combining the defect migration energy barrier in the physical prior knowledge base, the defect aggregation process is simulated, and the initial defect concentration is corrected to the final defect concentration. The carrier evolution is as follows: the defect energy level is calculated using the first-principles method, and the carrier concentration is calculated in combination with the rate equation; The electromagnetic dielectric field calculation submodule includes the real part of the dielectric constant. Calculation and imaginary part of dielectric constant calculate: Real part of dielectric constant The calculation is as follows: using the Maxwell-Garnett effective dielectric theory, combined with the final defect concentration to correct the polarizability, the real part of the dielectric constant is calculated. ; Imaginary part of dielectric constant The calculation is as follows: using the Drude model, the conductivity is calculated by combining carrier concentration and carrier mobility, and then the imaginary part of the dielectric constant is obtained. .

[0012] Furthermore, during the aforementioned carrier evolution, a carrier lifetime correction is introduced to adjust the steady-state carrier concentration.

[0013] Furthermore, the output in step 3 above includes: Quantitative data: different radiation doses D The real part of the dielectric constant below Imaginary part of dielectric constant ; Evolution curve: - D curve, - D curve.

[0014] Furthermore, step 4 above specifically includes: Measurement of the metasurface after radiation using an ellipsometry , Alternatively, the dielectric spectrum can be measured using terahertz time-domain spectroscopy; Calculate the relative error between experimental values ​​and model predictions. d ; like d >5%, correct the parameters in the physical prior knowledge base, and restart the multi-field coupling calculation; The validated parameters and experimental data are stored in a physical prior knowledge base to achieve dynamic optimization of the model.

[0015] Secondly, the method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment described in this invention can be entirely implemented using computer software. Therefore, correspondingly, this invention also provides a system for predicting the evolution of dielectric properties of metasurface materials under space radiation environment.

[0016] Thirdly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment as described in any one of the above claims.

[0017] Fourthly, the present invention also provides a computer device, the device including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment as described in any one of the above claims.

[0018] The beneficial effects of this invention are as follows: (1) Physical priors and deep coupling of multiple fields: This invention constructs a dynamic physical prior knowledge base, integrates material intrinsic parameters, radiation parameters and structural parameters, and establishes a multi-field feedback mechanism of "radiative transport-defect-carrier-electromagnetic dielectric" (such as dielectric loss feedback to correct carrier trapping cross section), breaks through the limitations of existing single physical field simulation, and improves the applicability and accuracy of the model; (2) "Computation-Experiment" Closed-Loop Verification: This invention introduces a verification and optimization module, which corrects physical prior parameters (such as defect generation efficiency and carrier mobility) in real time through experimental data and updates the knowledge base to achieve dynamic optimization of the model and ensure long-term prediction stability. (3) Cross-scale and cross-scene adaptation: The model supports different types of metasurfaces (silicon-based, metal-dielectric composite) and different space radiation environments (proton, heavy ion, electron). Scene switching can be achieved by calling the corresponding parameters in the physical prior knowledge base without reconstructing the model, and the adaptability is strong.

[0019] (4) This invention is applicable to the radiation resistance design and performance life assessment of metasurface devices (such as metasurface antennas and beamformers) in space environments such as aerospace and satellites. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a diagram illustrating the overall framework of a method for predicting the evolution of dielectric properties of metasurface materials under space radiation conditions, as proposed in this invention. Figure 2 This is a framework diagram of the multi-field coupling calculation proposed in this invention. Detailed Implementation The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

[0022] Example 1, Combination Figure 1 to Figure 2 This embodiment aims to provide a method for predicting the evolution of dielectric properties of metasurface materials under space radiation conditions. By constructing a physical prior knowledge base and a multi-field coupled calculation module, the method can be used to predict the dielectric constant of metasurface materials. , With accurate prediction of radiation dose, it solves the problems of narrow applicability, low accuracy and lack of dynamic feedback of existing methods.

[0023] The prediction method includes the following steps: Step 1: Construct a dynamically updatable physical prior knowledge base; Step 2: Construct a multi-field coupling calculation module, and perform radiation transport field calculation, defect-carrier evolution field calculation, and dielectric field calculation based on the physical prior knowledge base, and establish at least a dynamic feedback mechanism between the defect-carrier evolution field and the dielectric field. Step 3: Process and visualize the results output by the multi-field coupling calculation module; Step 4: Verify and optimize the multi-field coupling calculation results based on experimental data. By calibrating the calculation module parameters, the verified parameters and experimental data are stored in the physical prior knowledge base, thereby realizing the dynamic updating of the physical prior knowledge base and the iterative optimization of the calculation module.

[0024] The following is combined Figure 1 This embodiment describes a method for predicting the evolution of dielectric properties of metasurface materials under space radiation conditions. Figure 1 The above shows the overall architecture of the prediction method of the present invention, which includes four core parts: physical prior knowledge base, multi-field coupling calculation, dielectric property output and verification optimization. Each part can dynamically interact through data interface. Among them, the physical prior knowledge base stores the intrinsic parameters of metasurface materials, space radiation parameters, metasurface structure parameters and experimental calibration data, forming a dynamically updated knowledge base; Multi-field coupling calculation: includes a radiation transport field calculation submodule, a defect-carrier evolution field calculation submodule, and an electromagnetic dielectric field calculation submodule, which realizes the collaborative simulation of multiple physical processes through inter-field data coupling; Dielectric property output: Based on multi-field coupling calculation results, the real part, imaginary part, and evolution curve of the dielectric constant of the metasurface under different radiation doses are output. - D curve, D (Radiation dose); Validation and optimization: By comparing experimental measurement data (such as elliptic polarization spectrum and terahertz time-domain spectrum) with the calculation results, the model parameters are corrected to improve the prediction accuracy.

[0025] Addressing the three core deficiencies of existing technologies, this invention develops a "prediction model and method based on physical priors and multi-field coupling," which, starting from the underlying physical mechanisms, overcomes the shortcomings of existing methods. The technical means and core principles for overcoming these deficiencies constitute the core support for the inventiveness of this application: To address the limited applicability of existing technologies, this invention constructs a physical prior knowledge base module that integrates intrinsic material parameters, radiation parameters, and metasurface structure parameters, fundamentally overcoming the limitations of scenario and material compatibility. In practical applications, there is no need to reconstruct the model; simply calling the corresponding parameters according to the specific scenario enables universal prediction across materials, radiation types, and the entire dose range, overcoming the limitation of existing models being "dedicated to specific scenarios."

[0026] To address the low accuracy of existing technologies, this invention innovatively designs a multi-field coupling calculation module to achieve cascaded coupling and dynamic interaction of "radiative transport field → defect-carrier evolution field → electromagnetic dielectric field." Simultaneously, a dose-segmented correction model is introduced to accurately match the dielectric evolution patterns under different doses. In principle, the multi-field coupling module breaks the fragmented calculation mode of a single physical field, enabling bidirectional feedback of data between fields. For example, the energy deposition distribution output by the radiative transport field directly drives the generation, recombination, and migration processes of defects and carriers, while the polarization response calculated from the electromagnetic dielectric field inversely corrects the carrier trapping cross-section and defect diffusion coefficient, fully reconstructing the entire physical process of radiation damage. The dose-segmented correction model is designed to differentiate the damage characteristics across different dose ranges. In the low-dose range, a linear model is used to characterize the linear growth of defects and carriers, while in the high-dose range, a saturated model is used to fit the nonlinear effects of defect aggregation and carrier recombination. This effectively avoids the error problem of single fitting across the entire dose range in existing models, controlling the prediction error to within 5%.

[0027] To address the lack of dynamic feedback in existing technologies, this invention adds a verification and optimization module, constructing a closed-loop logic of "computation-experiment" to endow the model with self-optimization capabilities. Its core principle is to calculate the error threshold between the predicted results and experimental measurement data in real time. When the error exceeds a preset range, it automatically corrects key parameters in the physical prior knowledge base (such as defect generation efficiency and dielectric polarization relaxation time) and simultaneously updates the iteration coefficients of multi-field coupled calculations, forming a closed-loop cycle of "prediction-verification-correction-re-prediction." This design not only breaks the unidirectional calculation mode of existing models but also allows the model's accuracy to continuously improve with the accumulation of experimental data, ensuring the stability and accuracy of long-term predictions.

[0028] In summary, this invention solves the three core defects of existing technologies through an innovative architecture of "physical prior knowledge base + multi-field coupling calculation + verification and optimization closed loop". Its technical solution is not a simple superposition of existing technical means, but has significant creative and practical value, providing a brand-new technical path for radiation-resistant design and lifetime assessment of metasurface devices for space use.

[0029] The following is combined Figure 2 The above parts will be explained in detail; (1) Physics prior knowledge base This knowledge base is used to provide basic physical parameter support for multi-field coupling calculations and includes the following sub-libraries: Material intrinsic parameter sub-library: used to store radiation response parameters of metasurface functional layers and substrates; Space radiation parameter sub-library: used to store typical space radiation environment parameters, including radiation type (proton, heavy ion, electron) and particle energy; Metasurface structure parameter sub-library: used to store the geometric parameters of metasurface units, including unit period, functional layer thickness, and unit fill rate; Experimental calibration sub-library: Used to store experimental measurement data of metasurface dielectric properties under different radiation conditions, for model parameter correction.

[0030] (2) Multi-field coupling calculation module This module is the core of the prediction, achieving deep multi-field coupling through cascaded calculations and feedback corrections of "radiative transport → defect-carrier evolution → electromagnetic dielectric response," such as... Figure 2 As shown, it includes: A. Calculation of radiation transport field Its function is to simulate the transport process of space radiation particles in metasurfaces and calculate the energy deposition distribution and initial defect concentration. The specific implementation method is as follows: Using the Monte Carlo (MC) method (such as Geant4 software), the radiation parameters and metasurface structure parameters from the prior physical knowledge base are input, and the output is: Three-dimensional energy deposition distribution (Unit: eV / ) μm ³);

[0031] The initial defect concentration distribution is as follows: , or To generate efficiency for defects, This is the energy for defect formation.

[0032]

[0033] Wherein, the initial carrier concentration is ξ is the ionization efficiency. The ionization energy required to generate an electron-hole pair.

[0034] The coupling interface is: the initial defect concentration distribution and initial carrier concentration Output to the defect-carrier evolution field calculation.

[0035] B. Defect-Carrier Evolution Field Calculation Its function is to simulate the dynamic evolution of radiation-induced defects (generation, migration, recombination) and carrier evolution (excitation, capture, recombination), and output the final defect concentration. With carrier concentration ; The specific implementation method is as follows: Defect evolution: Molecular dynamics (MD) methods (such as LAMMPS software) are used, combined with defect migration barriers from a prior physical knowledge base, to simulate the defect aggregation process. ,( Correcting the initial defect concentration For the final defect concentration ; Carrier evolution: Defect energy levels are calculated using first-principles (DFT) methods (such as VASP software), combined with rate equations. , G Carrier generation rate, R Given the carrier recombination rate, calculate the carrier concentration. n ; In practical applications, charge carriers disappear through processes such as "defect trapping" and "electron-hole recombination," necessitating the introduction of charge carrier lifetime. t Corrected steady-state carrier concentration:

[0036] in,t Radiation time, carrier lifetime The carrier lifetime when there are no defects. k denoted as the defect scattering coefficient.

[0037] Coupling interface: The final defect concentration With carrier concentration The output is used for electromagnetic dielectric field calculation, and the dielectric property data fed back from the electromagnetic dielectric field is received to correct the carrier trapping cross section (for example, if the dielectric loss increases, it indicates that the carrier scattering is enhanced, and the trapping cross section needs to be increased).

[0038] C. Dielectric field calculation Its function is to calculate the equivalent dielectric constant of a metasurface based on defects and carrier distribution; The specific implementation method is as follows: Real part of dielectric constant ( Calculation: Using the Maxwell-Garnett effective medium theory, combined with defect concentration... N Corrected polarizability , Polarizability when there are no defects k Calculate the real part of the dielectric constant as the defect polarization correction factor. ; Imaginary part of dielectric constant ( Calculation: Using the Drude model, combined with carrier concentration n With carrier mobility m Calculate conductivity Therefore, we get:

[0039] in, oh The incident electromagnetic wave angular frequency, It is the vacuum permittivity; Coupled interface: , The dielectric property data is output to the dielectric property output and simultaneously fed back to the defect-carrier evolution field to optimize the defect mobility parameters.

[0040] (3) Dielectric property output Its function is to process and visualize the results of multi-field coupling calculations. The output should include at least: Quantitative data: different radiation doses ( D =0 , 10³ , 10 4 , 10 5 Gy) , Numerical value; Evolution curve: -D curve, - D Curve (including error range, based on uncertainty analysis of prior physical parameters); (4) Validation and optimization Its function is to calibrate model parameters using experimental data to improve prediction accuracy; The specific implementation process is as follows: Experimental measurements: The surface properties of the irradiated metasurface were measured using an ellipsometry. , Alternatively, the dielectric spectrum can be measured using terahertz time-domain spectroscopy; Error calculation: Calculate the relative error between the experimental value and the model prediction value. d ; Parameter correction: If d >5%, correct parameters in the physical prior knowledge base (such as adjusting defect generation efficiency). or (Carrier trapping cross section), restart multi-field coupling calculation; Model update: The validated parameters and experimental data are stored in the physical prior knowledge base to achieve dynamic model optimization.

[0041] Based on the above, this embodiment provides a specific operation of a method for predicting the evolution of metasurface dielectric properties under space radiation environment, including the following steps: Step 1: Parameter Input: Call the material parameters (such as the defect formation energy of silicon), space radiation parameters (such as the proton energy of 1 MeV), and structural parameters (such as the unit cell period of 300 nm) of the target metasurface from the physical prior knowledge base module, or manually input custom parameters; Step 2: Radiative Transport Simulation: Start the radiative transport field calculation and use Geant4 to simulate the transport of radiative particles in the metasurface to obtain the initial defect concentration distribution. ; Step 3: Defect-Carrier Evolution Simulation: [The text abruptly ends here, likely due to an incomplete N 0( x,y,z The input defect-carrier evolution field is calculated, and the defect aggregation is simulated using LAMMPS and the carrier concentration is calculated using VASP to obtain the final defect concentration. With carrier concentration ; Step 4: Electromagnetic Dielectric Calculation: [The following text appears to be a separate, unrelated section:] and Electromagnetic dielectric field calculations were performed using Maxwell-Garnett theory and the Drude model to calculate different doses. , And corrected using a dose segmentation model; Step 5: Output Results: Dielectric property output module output - D curve, - D curve; Step 6: Validation and Optimization: If the relative error between the experimental measured value and the predicted value... d >5%, improving the efficiency of correcting defects in the physical prior knowledge base. or Return to Step 2 and recalculate; if d If the value is ≤ 5%, output the final prediction result.

[0042] In summary, the method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment proposed in this invention has the following advantages: (1) Physical priors and deep coupling of multiple fields: Construct a dynamic physical prior knowledge base, integrate material intrinsic parameters, radiation parameters and structural parameters, and establish a multi-field feedback mechanism of "radiative transport-defect-carrier-electromagnetic dielectric" (such as dielectric loss feedback to correct carrier trapping cross section), break through the limitations of existing single physical field simulation, and improve the applicability and accuracy of the model; (2) "Computation-Experiment" Closed-Loop Verification: A verification and optimization module is introduced to correct physical prior parameters (such as defect generation efficiency and carrier mobility) in real time through experimental data and update the knowledge base to achieve dynamic optimization of the model and ensure long-term prediction stability. (3) Cross-scale and cross-scene adaptation: The model supports different types of metasurfaces (silicon-based, metal-dielectric composite) and different space radiation environments (proton, heavy ion, electron). Scene switching can be achieved by calling the corresponding parameters in the physical prior knowledge base without reconstructing the model, and the adaptability is strong.

[0043] Example 2: The method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment described in the above examples can be fully implemented using computer software. Therefore, this example provides a system for predicting the evolution of dielectric properties of metasurface materials under space radiation environment.

[0044] Example 3: This example provides a computer-readable storage medium storing a computer program. When the computer program is run by a processor, it executes the method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment described in the above example.

[0045] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0046] Example 4: This example provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment described in the above examples.

[0047] This embodiment provides a computer device. The hardware device in this part is a general model and is not shown in the figure. The system includes a processor and a memory. The processor and the memory can be connected by a bus or other means. The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, as well as corresponding program instructions / modules. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions and modules stored in the memory, so as to realize the method and steps for predicting the evolution of dielectric properties of metasurface materials under space radiation environment in the above method embodiment.

[0048] The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, mobile communication networks, and combinations thereof.

[0049] One or more modules are stored in the memory. When the processor executes, it performs the method steps in the embodiments. In this way, the invention objective can be achieved through the method, apparatus and process of the present invention. The specific details of the computer device described above can be understood by referring to the relevant descriptions and effects in the embodiments, and will not be repeated here.

[0050] The above description of the technical solution provided by the present invention through several specific embodiments is intended to highlight the advantages and benefits of the technical solution provided by the present invention. However, the above-described specific embodiments are not intended to limit the present invention. Any reasonable modifications and improvements to the present invention, reasonable combinations of implementation methods and equivalent substitutions based on the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment, characterized in that, The method is as follows: Step 1: Construct a dynamically updatable physical prior knowledge base; Step 2: Construct a multi-field coupling calculation module, and perform radiation transport field calculation, defect-carrier evolution field calculation, and dielectric field calculation based on the physical prior knowledge base, and establish at least a dynamic feedback mechanism between the defect-carrier evolution field and the dielectric field. Step 3: Process and visualize the results output by the multi-field coupling calculation module; Step 4: Verify and optimize the multi-field coupling calculation results based on experimental data. By calibrating the calculation module parameters, the verified parameters and experimental data are stored in the physical prior knowledge base, thereby realizing the dynamic updating of the physical prior knowledge base and the iterative optimization of the calculation module.

2. The method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment according to claim 1, characterized in that, The physical prior knowledge base includes the following sub-bases: A sub-library of intrinsic material parameters is used to store the radiation response parameters of metasurface functional layers and substrates; A sub-library of space radiation parameters is used to store typical space radiation environment parameters, including radiation type and particle energy. The metasurface structure parameter sub-library is used to store the geometric parameters of metasurface units, including unit period, functional layer thickness, and unit fill rate. The experimental calibration sub-library stores experimental measurement data of metasurface dielectric properties under different radiation conditions, which is used for model parameter correction.

3. The method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment according to claim 1 or 2, characterized in that, The multi-field coupling calculation module includes a radiation transport field calculation submodule, a defect-carrier evolution field calculation submodule, and an electromagnetic dielectric field calculation submodule. The radiation transport field calculation submodule is used to simulate the transport process of space radiation particles in the metasurface and calculate the energy deposition distribution, initial defect concentration and initial carrier concentration. The defect-carrier evolution field calculation submodule is used to simulate radiation-induced defect dynamic evolution and carrier evolution based on the initial defect concentration and the initial carrier concentration, output the final defect concentration and carrier concentration, and is also used to receive dielectric property data fed back by the electromagnetic dielectric field calculation submodule to correct the carrier trapping cross section. The electromagnetic dielectric field calculation submodule is used to calculate the equivalent dielectric constant of the metasurface based on the final defect concentration and carrier concentration distribution. It is also used to feed back the dielectric property data to the defect-carrier evolution field to optimize the defect mobility parameters.

4. The method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment according to claim 3, characterized in that, The radiation transport field calculation submodule calculates the three-dimensional energy deposition distribution, initial defect concentration, and initial carrier concentration by inputting radiation parameters and metasurface structure parameters from the physical prior knowledge base. The defect-carrier evolution field calculation submodule includes defect evolution and carrier evolution; The defect evolution is as follows: by combining the defect migration energy barrier in the physical prior knowledge base, the defect aggregation process is simulated, and the initial defect concentration is corrected to the final defect concentration. The carrier evolution is as follows: the defect energy level is calculated using the first-principles method, and the carrier concentration is calculated in combination with the rate equation; The electromagnetic dielectric field calculation submodule includes the real part of the dielectric constant. Calculation and imaginary part of dielectric constant calculate: Real part of dielectric constant The calculation is as follows: using the Maxwell-Garnett effective dielectric theory, combined with the final defect concentration to correct the polarizability, the real part of the dielectric constant is calculated. ; Imaginary part of dielectric constant The calculation is as follows: using the Drude model, the conductivity is calculated by combining carrier concentration and carrier mobility, and then the imaginary part of the dielectric constant is obtained. .

5. The method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment according to claim 4, characterized in that, During carrier evolution, a carrier lifetime correction is introduced to adjust the steady-state carrier concentration.

6. The method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment according to claim 1, characterized in that, The output in step 3 includes: Quantitative data: different radiation doses D The real part of the dielectric constant below Imaginary part of dielectric constant ; Evolution curve: - D curve, - D curve.

7. The method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment according to claim 1, characterized in that, Step 4 specifically involves: Measurement of the metasurface after radiation using an ellipsometry , Alternatively, the dielectric spectrum can be measured using terahertz time-domain spectroscopy; Calculate the relative error between experimental values ​​and model predictions. δ ; like δ >5%, correct the parameters in the physical prior knowledge base, and restart the multi-field coupling calculation; The validated parameters and experimental data are stored in a physical prior knowledge base to achieve dynamic optimization of the model.

8. A system for predicting the evolution of dielectric properties of metasurface materials under space radiation environment, characterized in that, The system is based on the method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment as described in any one of claims 1-7.

10. A computer device, characterized in that, The device includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the method for predicting the evolution of dielectric properties of metasurface materials under space radiation environment as described in any one of claims 1-7.

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