A design method and related device for infrared polarization control metasurface

Abstract

The present application discloses a design method and related devices for an infrared polarization-controlled metasurface, which relate to the technical field of metasurface design. The method comprises obtaining a first actual phase distribution of the overall metasurface within the working band based on polarization characteristic data of an object and an environment at each detection angle within the working band; based on the first actual phase distribution, inverting the unknown parameters in the initial achromatic formula using an improved particle swarm optimization algorithm to obtain a final achromatic formula corresponding to each characteristic wavelength, and calculating the first actual phase value of each metasurface unit at each characteristic wavelength based on the final achromatic formula corresponding to each characteristic wavelength and the two-dimensional coordinate value of each metasurface unit; obtaining the size parameters of each metasurface unit based on the first actual phase value of each metasurface unit and a scanning parameter database, thereby constructing an overall metasurface with a stable wide-band infrared polarization stealth function.

Description

Technical Field

[0001] The present application relates to the technical field of metasurface design, and in particular to a design method and related device for an infrared polarization-controlled metasurface. Background Art

[0002] In the field of optical detection, the stealth performance of a target not only depends on traditional light intensity control, but is also closely related to the polarization characteristics of light. The polarization bidirectional reflectance distribution function (p-BRDF) model can relate the polarization characteristics of the target material to the polarization state of the incident light and the reflected light, thereby accurately describing the polarization response of the target material at different detection angles. This theory provides important theoretical support for the development of polarization stealth technology. At the same time, metasurfaces, as subwavelength-scale artificial structured materials, can flexibly control the polarization and phase characteristics of light through carefully designed subwavelength-scale structural units, thereby reducing the difference in polarization characteristics between the target material and the background environment, providing a new technical approach for the realization of polarization stealth technology.

[0003] Currently, infrared stealth technology based on metasurfaces primarily focuses on light intensity control, while neglecting the optimization of polarization properties. This results in the cloaking effect being easily lost when subjected to polarization detection methods. To address this issue, a metasurface design method is urgently needed to improve the performance of infrared polarization stealth. Summary of the Invention

[0004] The purpose of this application is to provide a design method and related devices for infrared polarization-controlled metasurfaces, which can enable the overall metasurface to effectively achieve stable infrared polarization stealth function within a wide spectrum.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a design method for an infrared polarization-controlled metasurface, comprising:

[0007] Based on the acquired object polarization characteristic data and environment polarization characteristic data at each detection angle within the working band, a first actual phase distribution of the overall metasurface within the working band is obtained; the working band includes multiple characteristic wavelengths; the overall metasurface includes multiple metasurface units; the first actual phase distribution is the initial phase distribution required for the overall metasurface to achieve infrared polarization stealth within the working band;

[0008] Based on the first actual phase distribution of the overall metasurface within the working band, an improved particle swarm optimization algorithm is used to invert the unknown parameters in the constructed initial achromatic formula to obtain a final achromatic formula corresponding to each characteristic wavelength, and based on the final achromatic formula corresponding to each characteristic wavelength and the obtained two-dimensional coordinate value of each metasurface unit, the first actual phase value of each metasurface unit at each characteristic wavelength is calculated; the improved particle swarm optimization algorithm is a particle swarm optimization algorithm combined with a Latin hypercube sampling algorithm;

[0009] Based on the first actual phase value of each metasurface unit at all characteristic wavelengths and the constructed scanning parameter database, the size parameters of each metasurface unit are obtained, and based on the size parameters of each metasurface unit, an overall metasurface with stable wide-band infrared polarization stealth function is constructed; the scanning parameter database is a database of the mapping relationship between the size parameters and phase values ​​of the metasurface units with stable infrared polarization stealth function at different characteristic wavelengths.

[0010] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned design method of the infrared polarization-controlled metasurface.

[0011] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned design method of the infrared polarization-controlled metasurface.

[0012] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the above-mentioned design method of the infrared polarization-controlled metasurface.

[0013] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0014] The present application provides a design method and related device for an infrared polarization-controlled metasurface. By applying the object polarization characteristic data and the calculated environmental polarization characteristic data to the design of the infrared polarization stealth metasurface in the infrared polarization stealth metasurface design part, that is, the measured polarization data is combined in the metasurface design, which is more in line with the actual situation than the metasurface design method of pure theoretical simulation. Moreover, by extracting the phase value obtained from the object polarization characteristic data and the environmental polarization characteristic data and using the improved particle swarm optimization algorithm to invert the additional phase value of the achromatic aberration formula at the characteristic wavelength, the chromatic aberration of the metasurface at the characteristic wavelength is eliminated, so that a more accurate actual phase value of each metasurface unit at the characteristic wavelength can be calculated, and the size parameters of each metasurface unit can be accurately adjusted based on these values, ensuring that the metasurface has stable wide-band infrared polarization stealth within the working band. This technical solution can effectively improve the stealth performance of the metasurface and has good practical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 This is a diagram of the application environment of a design method for an infrared polarization-controlled metasurface in Example 1 of the present application;

[0017] Figure 2 A schematic flow chart of a design method for an infrared polarization-controlled metasurface provided in Example 1 of the present application;

[0018] Figure 3 A schematic diagram of the structure of the experimental measurement device provided in Example 2 of the present application;

[0019] Figure 4 A schematic diagram of the process of constructing the final p-BRDF model provided in Example 2 of the present application;

[0020] Figure 5 A schematic diagram of the process of designing the infrared polarization stealth metasurface provided in Example 2 of the present application;

[0021] Figure 6 A schematic diagram of the process of parameter inversion of the particle swarm optimization algorithm provided in Example 2 of the present application;

[0022] Figure 7 A schematic diagram of the structure of a computer device provided in Example 3 of the present application.

[0023] Reference numerals:

[0024] 1: BRDF measurement device; 2: detector; 3: light source; 4: left rotary arm; 5: right rotary arm; 6: sample; 7: electric rotary stage; 8: controller; 9: computer. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] Currently, existing metasurface design methods often fail to consider the actual polarization characteristics of an object and fail to incorporate measured polarization data, resulting in deviations between the design results and actual requirements. Therefore, this application provides a metasurface design method that can incorporate measured polarization data to achieve high-precision phase control, effectively achieving infrared polarization stealth for the entire metasurface.

[0027] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0028] Example 1

[0029] The design method of infrared polarization control metasurface provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the acquired object and environment polarization characteristic data at each detection angle within the working band to the server 104. After receiving the data, the server 104 first obtains the first actual phase distribution of the overall metasurface within the working band based on the object and environment polarization characteristic data at each detection angle within the working band; then inverts the unknown parameters in the initial achromatic formula to obtain the final achromatic formula corresponding to each characteristic wavelength, and based on the final achromatic formula corresponding to each characteristic wavelength and the two-dimensional coordinate value of each metasurface unit, calculates the first actual phase value of each metasurface unit at each characteristic wavelength; finally, based on the first actual phase value of each metasurface unit at all characteristic wavelengths and the scan parameter database, obtains the size parameters of each metasurface unit, thereby constructing an overall metasurface with stable infrared polarization stealth function. The server 104 can feed back the obtained overall metasurface with stable infrared polarization stealth function to the terminal 102.

[0030] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, and IoT devices, and the server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0031] In an exemplary embodiment, Figure 2 As shown, a design method for infrared polarization control metasurface is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, and can also be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, including the following steps 201 to 203.

[0032] Step 201, based on the acquired object polarization characteristic data and environmental polarization characteristic data at each detection angle in the working band, obtain the first actual phase distribution of the overall metasurface in the working band; the working band includes multiple characteristic wavelengths; the overall metasurface includes multiple metasurface units; the first actual phase distribution is the initial phase distribution required for the overall metasurface to achieve infrared polarization stealth in the working band.

[0033] Step 202: Based on the first actual phase distribution of the entire metasurface within the working band, the unknown parameters in the constructed initial achromatic formula are inverted using the improved particle swarm optimization algorithm to obtain the final achromatic formula corresponding to each characteristic wavelength, and based on the final achromatic formula corresponding to each characteristic wavelength and the obtained two-dimensional coordinate value of each metasurface unit, the first actual phase value of each metasurface unit at each characteristic wavelength is calculated; the improved particle swarm optimization algorithm is a particle swarm optimization algorithm combined with the Latin hypercube sampling algorithm.

[0034] Step 203: Based on the first actual phase value of each metasurface unit at all characteristic wavelengths and the constructed scanning parameter database, the size parameters of each metasurface unit are obtained, and based on the size parameters of each metasurface unit, an overall metasurface with stable wide-band infrared polarization stealth function is constructed; the scanning parameter database is a database of the mapping relationship between the size parameters and phase values ​​of the metasurface units with stable infrared polarization stealth function at different characteristic wavelengths.

[0035] By implementing the above-mentioned steps 201 to 203, the object polarization characteristic data and the calculated environmental polarization characteristic data are applied to the design of the infrared polarization stealth metasurface in the infrared polarization stealth metasurface design part. That is, the measured polarization data is combined in the metasurface design, which is more in line with the actual situation than the metasurface design method of pure theoretical simulation. Moreover, by extracting the phase value obtained from the object polarization characteristic data and the environmental polarization characteristic data and the improved PSO algorithm to invert the additional phase value of the achromatic formula at the characteristic wavelength, the chromatic aberration of the metasurface at the characteristic wavelength is eliminated, so that a more accurate actual phase value of each metasurface unit at the characteristic wavelength can be calculated, and the size parameters of each metasurface unit are accurately adjusted based on these values, thereby ensuring that the metasurface has stable wide-band infrared polarization stealth within the working band.

[0036] Furthermore, in step 201, based on the acquired object polarization characteristic data and environment polarization characteristic data at each detection angle within the working band, a first actual phase distribution of the entire metasurface within the working band is obtained, specifically including:

[0037] (a1) Based on the constructed final polarization bidirectional reflectance distribution function model, the polarization characteristic data of the object at each detection angle within the working band are obtained.

[0038] (a2) Based on the acquired environmental radiation characteristic data and reflectivity data, calculate the environmental polarization characteristic data at each detection angle within the working band.

[0039] (a3) The object polarization characteristic data and the environment polarization characteristic data at each detection angle within the working band are converted to obtain the object Jones vector and the environment Jones vector at each detection angle within the working band, and based on the object Jones vector and the environment Jones vector at each detection angle within the working band, the Jones matrix corresponding to each detection angle within the working band of a single metasurface unit is obtained.

[0040] (a4) Based on the Jones matrix corresponding to each detection angle of the single metasurface unit within the working band, a second actual phase value corresponding to each detection angle of the single metasurface unit within the working band is obtained.

[0041] (a5) Based on the second actual phase value corresponding to each detection angle of the single metasurface unit within the working band, a first actual phase distribution of the entire metasurface within the working band is obtained.

[0042] Furthermore, in step (a1), based on the constructed final polarization bidirectional reflectance distribution function model, the polarization characteristic data of the object at each detection angle within the working band is obtained, specifically including:

[0043] (b1) Obtaining an image dataset of the sample material and processing the image dataset to obtain a measured polarization characteristic dataset; the image dataset is obtained by photographing the sample material at different incident angles and detection angles using a bidirectional reflectance distribution function measurement device.

[0044] (b2) Constructing an initial polarization bidirectional reflection distribution function model, and obtaining a polarization characteristic expression based on the initial polarization bidirectional reflection distribution function model.

[0045] (b3) Processing the polarization characteristic expression using Fourier series expansion to obtain an expanded polarization characteristic expression, and processing the expanded polarization characteristic expression using fast Fourier transform to obtain an initial value of the parameter to be inverted.

[0046] (b4) Based on the measured polarization characteristic data set and the initial value of the parameter to be inverted, the expanded polarization characteristic expression is parameter inverted using the quasi-Newton algorithm to obtain the final value of the parameter to be inverted.

[0047] (b5) Based on the final value of the parameter to be inverted and the expanded polarization characteristic expression, the final polarization bidirectional reflectance distribution function model is obtained, and based on the final polarization bidirectional reflectance distribution function model, the polarization characteristic data of the object at each detection angle within the working band are obtained.

[0048] Furthermore, in step (b4), based on the measured polarization characteristic data set and the initial value of the parameter to be inverted, the expanded polarization characteristic expression is subjected to parameter inversion using a quasi-Newton algorithm to obtain the final value of the parameter to be inverted, specifically including:

[0049] (c1) Based on the value of the parameter to be inverted at the current iteration number and the expanded polarization characteristic expression, the predicted value of the polarization characteristic at the current iteration number is calculated; the value of the parameter to be inverted at the current iteration number is the initial value of the parameter to be inverted at the first iteration.

[0050] (c2) constructing a first objective function based on the predicted value of the polarization characteristic at the current iteration number and the corresponding measured value of the polarization characteristic, and calculating the gradient value of the first objective function at the current iteration number.

[0051] (c3) Determine whether the gradient value of the first objective function at the current number of iterations meets the preset convergence condition.

[0052] If so, the value of the parameter to be inverted at the current number of iterations is output, and the value of the parameter to be inverted at the current number of iterations is used as the final value of the parameter to be inverted; if not, the search direction and search step at the current number of iterations are calculated based on the approximate Hessian matrix at the current number of iterations and the gradient value of the first objective function, and the value of the parameter to be inverted and the approximate Hessian matrix at the next number of iterations are calculated based on the search direction and search step at the current number of iterations, and the step is returned to the step "based on the value of the parameter to be inverted at the current number of iterations and the expanded polarization characteristic expression, the predicted value of the polarization characteristic at the current number of iterations is calculated".

[0053] Furthermore, in step (a4), based on the second actual phase value corresponding to each detection angle of each metasurface unit within the working band, a first actual phase distribution of the entire metasurface within the working band is obtained, which specifically includes:

[0054] (d1) Based on the obtained period of each metasurface unit and the radius and focus of the overall metasurface, the center coordinates of each metasurface unit are calculated.

[0055] (d2) Based on the center coordinates of each metasurface unit and the focus of the entire metasurface, the relative detection angle of each metasurface unit is calculated.

[0056] (d3) Based on the second actual phase value corresponding to each detection angle of a single metasurface unit within the working band and the relative detection angle of each metasurface unit, the second actual phase value of each metasurface unit within the working band is matched, and based on the second actual phase value of each metasurface unit within the working band, the first actual phase distribution of the overall metasurface within the working band is obtained.

[0057] Furthermore, in step 202, based on the first actual phase distribution of the entire metasurface within the working band, the unknown parameters in the constructed initial achromatic formula are inverted using an improved particle swarm optimization algorithm to obtain the final achromatic formula corresponding to each characteristic wavelength, specifically including:

[0058] (e1) Constructing a second objective function; the second objective function is determined based on the second actual phase value of each metasurface unit within the working band and the phase value calculated by the initial achromatic formula; the second actual phase value of each metasurface unit within the working band is determined by the first actual phase distribution of the entire metasurface within the working band.

[0059] (e2) Based on the second objective function and the current iteration position of each particle, the current iteration fitness value of each particle is calculated, and the current iteration position and current iteration fitness value of each particle are stored to obtain the individual position and individual fitness value, and the smallest fitness value among the individual fitness values ​​is selected as the optimal fitness value of the group, and the particle position corresponding to the optimal fitness value of the group is recorded as the optimal position of the group; the current iteration position of each particle is the additional phase value of the current iteration; the current iteration position and current iteration speed of each particle are calculated by using the Latin hypercube sampling algorithm in the first iteration.

[0060] (e3) Determine whether the current number of iterations has reached the maximum number of iterations.

[0061] If so, the optimal position of the group is output and used as the additional phase value; if not, the next iteration position and next iteration speed of each particle are updated, and the process returns to step "calculating the current iteration fitness value of each particle based on the second objective function and the current iteration position of each particle".

[0062] Furthermore, in step 203, based on the first actual phase value of each metasurface unit at each characteristic wavelength and the constructed scanning parameter database, the size parameters of each metasurface unit are obtained, specifically including:

[0063] (f1) Performing a difference sum operation on the first actual phase value of each metasurface unit at all characteristic wavelengths and each phase value in the constructed scanning parameter database to obtain the sum of the phase difference values ​​of each metasurface unit at all characteristic wavelengths.

[0064] (f2) Based on the sum of the phase difference values ​​of each metasurface unit at all characteristic wavelengths, the phase value corresponding to the minimum sum of the phase difference values ​​in the scanning parameter database is selected, and the unit structure parameter index of the phase value corresponding to the minimum sum of the phase difference values ​​in the scanning parameter database is assigned to the corresponding unit to obtain the size parameters of each metasurface unit.

[0065] Furthermore, the operating band is 8 to 11 μm; and the characteristic wavelength is a wavelength selected from the operating band at intervals of 0.5 μm.

[0066] Example 2

[0067] In order to make the technical solution of this embodiment clearer, the specific structure of the device of this embodiment and the implementation steps of the method are described in detail below in the form of examples.

[0068] This embodiment aims to provide a design method for infrared polarization-controlled metasurfaces based on polarization bidirectional reflectance distribution function (p-BRDF) theory, where infrared refers to the 8-11µm operating band. This method first establishes an initial p-BRDF model and obtains multi-angle polarization characteristic data of the sample material. This data is then combined with parameter inversion to obtain the final p-BRDF model. Finally, the design and simulation of an infrared polarization-stealth metasurface are carried out based on the p-BRDF model data and calculated ambient polarization characteristic data (in this embodiment, water polarization characteristic data is used as an example).

[0069] This method consists of two parts: p-BRDF model construction and infrared polarization stealth metasurface design. Figure 3 As shown; the flowchart of the final p-BRDF model construction part is as follows Figure 4 As shown; the flow chart of the infrared polarization stealth metasurface design is as follows Figure 5 As shown; the parameter inversion flow chart of the particle swarm optimization algorithm (PSO) is as follows Figure 6 shown.

[0070] In this embodiment, the p-BRDF model is constructed to obtain polarization characteristic data of an object at different detection angles, specifically including the following steps:

[0071] Step 1: Obtain the multi-angle polarization (DOP) characteristics of a planar sample material (i.e., measure the polarization characteristic dataset). The devices and components used include: a bidirectional reflectance distribution function (BRDF) measurement device 1, a light source 3 with a wavelength of 8-11µm, a detector 2 with a response band of 8-11µm (a split-focus plane polarization detector), a controller 8, and a computer 9. The material of the tested sample 6 is a stainless steel sheet. The specific test steps are as follows:

[0072] Step 1-1, turn on the light source and detector, the light source is 30 ° The incident zenith angle moves to 60 ° Zenith angle of incidence, in steps of 10 °, each time the light source steps forward, the electric rotating stage 7 of the BRDF measuring device 1 moves forward by 60 ° The detection azimuth angle is one rotation of the step length, and the light source 3 and the electric rotating stage 7 each step, the detector 2 from 0 ° The detection zenith angle moves to 90 ° Detect zenith angle with a step size of 2 ° , and take a photo of sample 6 at the same time.

[0073] Among them, the incident zenith angle and the detection zenith angle are ° Both are located on the top side of the arm, the electric rotary table 70 ° Directly facing the light source 3.

[0074] In step 1-2, a data set of images of the sample material is obtained, resulting in a total of 4*7*46=1288 images. The polarization characteristic data of each image is obtained after processing by computer 9. The data is then aggregated to obtain the polarization characteristic data of the sample material (i.e., the measured polarization characteristic data set).

[0075] Step 2: Build the initial p-BRDF model and then further obtain the DOP expression.

[0076] Among them, the initial p-BRDF model expression constructed is as follows:

[0077] ;

[0078] Where, is the angle between the normal of the object plane and the normal of the microfacet; Zenith angle of incidence; To detect the zenith angle; is the azimuth angle of the detector relative to the light source; is the Mueller matrix of the specular reflection component; is the Mueller matrix of the diffuse reflection component; is a constant; is the Mueller matrix of the volume scattering component; unknown parameters include roughness , specular reflection coefficient , diffuse reflectance and volume scattering coefficient .

[0079] The DOP expression is as follows:

[0080] ;

[0081] Where, 、 、 is the element in the Mueller matrix form expression of the p-BRDF model; is the radiation intensity of the light source; is the radiation intensity of the object surface; Represents the integrand Perform hemisphere integration; is the solid angle; is the radiation intensity of the object surface, and its expression is as follows:

[0082] ;

[0083] Where, is the blackbody radiation formula; is the surface area of ​​the object; 、 are the first radiation constant and the second radiation constant, respectively, , ; T is the temperature of the target object, are characteristic wavelengths, λ1 and λ2 are 8μm and 11μm respectively.

[0084] Step 3: Start parameter inversion. The parameter inversion algorithm is the Davidon-Fletcher-Powell algorithm (DFP) that combines the Discrete Fourier Series (DFS) and the Fast Fourier Transform (FFT).

[0085] Step 3-1: Use DFS and FFT to calculate the initial values ​​of the parameters to be inverted by the DFP algorithm.

[0086] Step 3-1-1, perform DFS (Fourier series expansion) on the DOP expression, the expression is as follows:

[0087] ;

[0088] Where, is a constant term, which represents the average value of the function in one period; It indicates the current order; N Indicates the maximum order, set to 3; and is the Fourier coefficient, is the parameter to be inverted, since N =3, so there are 6 inversion parameters in total; is the azimuth angle of the detector relative to the light source; is the fundamental angular frequency, .

[0089] Step 3-1-2, calculate by FFT (Fast Fourier Transform) The value of and The initial value of . and In the example, the number of iterations k is initially set to 0. and As the parameter to be inverted in the subsequent parameter inversion and The initial value of , the FFT calculation formula is as follows:

[0090] ;

[0091] Step 3-2: Use the DFP algorithm to perform parameter inversion.

[0092] Step 3-2-1, set the initial parameters of the DFP algorithm: σ, ε are constants, , ; k is the number of iterations, ; The objective function The approximate Hessian matrix (i.e., the approximation of the second-order derivative matrix), the primary matrix is a unit matrix with 6 orders; the parameter to be inverted is and , which are the Fourier coefficients of the DOP expansion in step 3-1-1, there are 6 in total. and The initial values ​​of and , remember the parameters .

[0093] Step 3-2-2, construct the objective function of the DFP algorithm. The first objective function is established based on the DOP measurement value and the DOP prediction value. , the first objective function The expression is as follows:

[0094] ;

[0095] Where, is the DOP prediction value of the model, is the DOP measurement value of the experiment.

[0096] Step 3-2-3, preset the convergence condition of the DFP algorithm: if the first objective function under the current number of iterations Gradient The module satisfies , then the convergence condition is reached, the inversion ends, and the corresponding parameter value is output As the optimal inversion value, it is also the final value of the parameter to be inverted; if If the conditions are not met, the next inversion is continued until the maximum number of iterations of the algorithm is reached, and finally the optimal inversion value is output.

[0097] in, Gradient The calculation formula is as follows:

[0098] ;

[0099] Step 3-2-4, calculate the search direction and search step size In order to update the parameters to be inverted:

[0100] in, The expression is as follows:

[0101] ;

[0102] Among them, the search step length , is the smallest non-negative integer that satisfies the following inequality :

[0103] .

[0104] Step 3-2-5, calculate the updated parameters to be inverted and the approximate Hessian matrix , and return to step 3-2-2 for the next iteration.

[0105] Among them, the new parameters to be inverted The expression is as follows:

[0106] ;

[0107] Among them, the new approximate Hessian matrix Calculated using the DFP correction formula, the correction formula expression is as follows:

[0108] ;

[0109] Where, , , and They are and The transpose of .

[0110] Step 3-3, the inversion results (Here is the final value of the parameter to be inverted) Substitute it into the DOP expression in step 3-1-1 to obtain the final p-BRDF model.

[0111] This application uses a quasi-Newton algorithm combined with Fourier series expansion in the p-BRDF model construction part. This inversion algorithm linearizes the DOP expression by expanding it into a Fourier series, avoiding the bottleneck problem of high-dimensional nonlinear optimization in traditional p-BRDF parameter fitting and improving computational efficiency.

[0112] In this embodiment, the infrared polarization stealth metasurface has an operating band range of 8 to 11 μm. Its function is to reduce the difference between the polarization characteristics of the object and the polarization characteristics of the environment, thereby achieving stealth. The phase control principle of the infrared polarization stealth metasurface combines the propagation phase and the geometric phase. The propagation phase value is mainly related to the size parameters and wavelength of the unit, and the geometric phase value is twice the rotation angle of the unit. Since the current metasurface achromatic design is still mainly based on discrete wavelengths, it is difficult to design the achromaticity of the metasurface under continuous wavelengths. Therefore, this embodiment adopts a discrete wavelength achromatic design. A wavelength is selected every 0.5 μm from the working band of 8 to 11 μm of the metasurface, for a total of 7 wavelengths (i.e., 8 μm, 8.5 μm, 9 μm, 9.5 μm, 10 μm, 10.5 μm, and 11 μm). These 7 wavelengths are used as the characteristic wavelengths of the metasurface achromaticity. The infrared polarization stealth metasurface design specifically includes the following steps:

[0113] Step 1: Obtain the object polarization characteristic data and the environment polarization characteristic data at each detection angle within the operating band. The detection angle here includes the detection zenith angle and the detection azimuth angle. The object polarization characteristic data is derived from the final p-BRDF model constructed in step 3-3 of the polarization characteristic model construction section and integrated into a data file. Assuming that the water surface in the environment is calm and the temperature is constant, the polarization characteristics are the same at each detection angle and wavelength. The polarization characteristic data is obtained using the following formula:

[0114] ;

[0115] Where R s is the reflectivity of the component parallel to the incident surface; R p is the reflectivity of the component at vertical incidence; I e is the thermal radiation of seawater; I r is the atmospheric sky diffuse radiation.

[0116] Step 2: Extract the object polarization characteristic data and water polarization characteristic data corresponding to each detection angle within the working band, convert them into Jones vector form, and then calculate the Jones matrix corresponding to each detection angle of a single metasurface unit within the working band according to the following formula:

[0117] ;

[0118] Where, is the Jones vector of the water body; is the Jones matrix of a single metasurface unit; is the Jones vector of the object.

[0119] The expression of the Jones matrix of a single metasurface unit is as follows:

[0120] ;

[0121] in, is the rotation angle of the metasurface unit relative to the x-axis; 、 are the amplitudes in the x-axis and y-axis directions respectively; is the phase delay in the x-polarization direction, is the phase delay in the y-polarization direction, and It can be obtained by the following expression:

[0122] ;

[0123] ;

[0124] Where, is the wavelength, is the equivalent refractive index of the metasurface unit, and The x and y directions respectively

[0125] Equivalent propagation length in the direction.

[0126] Step 3: According to the Jones matrix corresponding to each detection angle of the single metasurface unit in the working band obtained in step 2, the detection angle of the single metasurface unit in the working band is obtained. The corresponding second actual phase value is saved as a data file, where the second actual phase value is obtained by the following formula:

[0127] ;

[0128] Where, represents the geometric phase part, Represents the propagation phase part.

[0129] Step 4: Determine the period of each metasurface unit and the radius and focus of the entire metasurface, and then calculate the center coordinates of each metasurface unit. Taking the center coordinates of each metasurface unit as the origin, calculate the detection angle of the focus of the entire metasurface relative to each metasurface unit, and obtain the relative detection angle of each metasurface unit. Then, according to the second actual phase value obtained in step 3, and detection angle The corresponding relationship between the second actual phase value of each metasurface unit in the working band is obtained, and the first actual phase distribution required for the entire metasurface to achieve infrared polarization stealth in the working band is obtained. .

[0130] Step 5: Use the finite-difference time-domain (FDTD) simulation software to perform parameter scanning on the metasurface unit at the characteristic wavelength and build a parameter scanning database. : The mapping relationship between the unit size parameters and the phase value of the metasurface unit at different characteristic wavelengths and different detection angles can be obtained through parameter scanning. It represents the phase control range that can be achieved by the metasurface unit, where the size of the metasurface unit must be smaller than the period of the metasurface unit; at the same time, in order to ensure that the phase response of the metasurface unit meets the actual needs, that is, > When the unit material is the same, parameter scanning is performed on single metasurface units with different shapes to expand the phase control range of the metasurface unit.

[0131] Step 6: Since there is no first actual phase distribution required for the entire metasurface to achieve infrared polarization stealth in the working band Achromatic processing will make it difficult for light of different wavelengths to focus at the focal point of the entire metasurface, resulting in unstable polarization stealth effect. In order to enable the metasurface to maintain a stable infrared polarization stealth effect across the entire working band, it is necessary to construct an achromatic formula for the entire metasurface at the characteristic wavelength. The expression is as follows:

[0132] ;

[0133] Where, is the current characteristic wavelength of the entire metasurface; is the two-dimensional coordinate value of any hypersurface unit on the overall hypersurface; is the three-dimensional coordinate value of the focus of the overall hypersurface, which is located on a hemisphere with the center of the overall hypersurface as the origin and the focal length of the overall hypersurface as the radius; is the additional phase value corresponding to each characteristic wavelength, which is used to reduce the chromatic aberration of the metasurface. It is an unknown parameter, that is, a parameter that needs to be solved.

[0134] Step 7: The first actual phase distribution of the entire metasurface within the working band obtained in step 4 , invert the achromatic formula constructed in step 6 The corresponding additional phase value at the characteristic wavelength , where the inversion algorithm is the particle swarm optimization algorithm combined with the Latin hypercube sampling algorithm.

[0135] Step 7-1, set the initial parameters of the PSO algorithm. The particle dimension D represents the number of parameters to be inverted and is set to 7. The population size N1 is set to 30. is the current number of iterations, the maximum number of iterations Set to 200; Maximum inertia weight and minimum inertia weight 2 and 0.4 respectively; learning factors c3 and c4 are set to 1.59443; position maximum value X max and position minimum X min are 2π and 0 respectively; the maximum speed V max and the minimum speed V min They are 0.055 and -0.055 respectively.

[0136] In step 7-2, the position and velocity of the particles are initialized using the Latin Hypercube Sampling (LHS) algorithm.

[0137] Step 7-2-1, evenly divide into layers: Since there are 7 parameters to be inverted, the number of dimensions D=7, and evenly extract each dimension N =500 samples, then j Subintervals for: .

[0138] Step 7-2-2, random sampling: generate random offsets within the subinterval : ; then the normalized cumulative probability value for: .

[0139] Step 7-2-3, use the inverse cumulative distribution function of uniform distribution to generate the initial value of the particle, the inverse cumulative distribution function The expression is as follows:

[0140] ;

[0141] Where, a and b are the lower and upper limits of x respectively; for the initial position of the generated particle, a =X min , b =X max ; For the initial velocity of generated particles, a =V min , b =V max .

[0142] Step 7-3: construct the second objective function and use it to calculate the fitness values ​​of all initial particles in the current iteration, record the individual fitness value and individual position as well as the group optimal fitness value and group optimal position.

[0143] Among them, the fitness value is the value calculated by the second objective function; the individual fitness value and individual position G best Refers to the fitness value and position of each particle in the current iteration respectively; the optimal fitness value of the group is the minimum fitness value among all the particle fitness values ​​in the current iteration, and the corresponding particle position is recorded as the optimal position of the group Q best .

[0144] The second objective function expression is as follows:

[0145] ;

[0146] Where, is the phase value determined based on the achromatic formula in step 6, Get the phase value for step 4.

[0147] Step 7-4, preset algorithm termination condition: when the algorithm reaches the maximum number of iterations When , stop the loop and output the optimal position Q of the group best As the optimal inversion value, also as the final value of the parameter to be inverted, and this value (Q best That includes 7 additional phase values ​​C) Substitute into the achromatic formula in step 4 The actual phase distribution formula after eliminating chromatic aberration at the characteristic wavelength is obtained (i.e. the final achromatic formula); if the algorithm does not reach the maximum number of iterations , then continue to the next inversion until the maximum number of iterations of the algorithm is reached, and finally output the optimal inversion value.

[0148] Step 7-5, update the position and velocity of the particles in the PSO algorithm;

[0149] The speed update formula is as follows:

[0150] ;

[0151] ;

[0152] Where i is the particle number, i=1, 2,...N1; d is the particle dimension number, d=1, 2,...D; is the inertia weight; r 1 and r 2 is a random number in the interval [0, 1] to increase the randomness of the search; For the particle The velocity vector of dimension d in the iteration; is the number of particles (individuals) in the The optimal solution of the dth dimension in the iteration; is the historical optimal position of the swarm in the dth dimension at the kth iteration.

[0153] The position update formula is as follows:

[0154] ;

[0155] Where, For the The position vector of dimension d in the iteration is equal to The position vector of the dth dimension in the iteration With the The velocity vector of dimension d in the iteration sum.

[0156] Step 7-6, return to step 7-3 to calculate the fitness value of the updated particle and update the individual optimal position G best and the optimal position Q of the group best :Use the second objective function to calculate the fitness value of the updated particle, and update the individual optimal fitness value and individual optimal position G of each particle best , and update the group's optimal fitness value and group's optimal position Q at the same time best .

[0157] Step 8: Use the actual phase distribution formula after eliminating chromatic aberration at each characteristic wavelength Calculate the first actual phase value of each metasurface unit at each characteristic wavelength, and compare the obtained phase value with the scan parameter database to match.

[0158] The specific matching process is illustrated by taking a single metasurface unit as an example: each metasurface unit has a total of 7 discrete wavelengths, corresponding to 7 phase values. These 7 phase values ​​are subtracted from each phase value in the scanning parameter database and summed to obtain the total phase difference values; the index corresponding to the phase value with the smallest total phase difference value in the scanning parameter database is extracted, and then the corresponding size parameter of the phase value index in the scanning parameter database is assigned to this metasurface unit; the above steps are performed sequentially for each metasurface unit to obtain the size parameters of each metasurface unit.

[0159] Step 9: Generate the entire metasurface structure using the size parameters of each metasurface unit obtained in step 8. The overall metasurface is a metasurface with stable infrared polarization stealth function.

[0160] Step 10: Perform far-field simulation on the overall metasurface with stable infrared polarization stealth function generated in step 9 to obtain far-field simulation data.

[0161] Example 3

[0162] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a design method for an infrared polarization-controlled metasurface is implemented.

[0163] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0164] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0165] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0166] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0167] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0168] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0169] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0170] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0171] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A design method for an infrared polarization-controlled metasurface, characterized in that: The design method of the infrared polarization control metasurface includes: Based on the acquired object polarization characteristic data and environment polarization characteristic data at each detection angle within the working band, a first actual phase distribution of the overall metasurface within the working band is obtained; the working band includes multiple characteristic wavelengths; the overall metasurface includes multiple metasurface units; the first actual phase distribution is the initial phase distribution required for the overall metasurface to achieve infrared polarization stealth within the working band; Based on the first actual phase distribution of the overall metasurface within the working band, an improved particle swarm optimization algorithm is used to invert the unknown parameters in the constructed initial achromatic formula to obtain a final achromatic formula corresponding to each characteristic wavelength, and based on the final achromatic formula corresponding to each characteristic wavelength and the obtained two-dimensional coordinate value of each metasurface unit, the first actual phase value of each metasurface unit at each characteristic wavelength is calculated; the improved particle swarm optimization algorithm is a particle swarm optimization algorithm combined with a Latin hypercube sampling algorithm; Based on the first actual phase value of each metasurface unit at all characteristic wavelengths and the constructed scanning parameter database, the size parameters of each metasurface unit are obtained, and based on the size parameters of each metasurface unit, an overall metasurface with stable wide-band infrared polarization stealth function is constructed; the scanning parameter database is a database of the mapping relationship between the size parameters and phase values ​​of the metasurface units with stable infrared polarization stealth function at different characteristic wavelengths.

2. The design method of infrared polarization control metasurface according to claim 1, characterized in that: Based on the acquired object polarization characteristic data and the environment polarization characteristic data at each detection angle within the working band, a first actual phase distribution of the entire metasurface within the working band is obtained, specifically including: Based on the final polarization bidirectional reflectance distribution function model constructed, the polarization characteristic data of the object at each detection angle within the working band is obtained; Based on the acquired environmental radiation characteristic data and reflectivity data, the environmental polarization characteristic data at each detection angle within the working band is calculated; The object polarization characteristic data and the environment polarization characteristic data at each detection angle within the working band are converted to obtain the object Jones vector and the environment Jones vector at each detection angle within the working band, and based on the object Jones vector and the environment Jones vector at each detection angle within the working band, the Jones matrix corresponding to each detection angle within the working band of a single metasurface unit is obtained; Based on the Jones matrix corresponding to each detection angle of the single metasurface unit within the working band, a second actual phase value corresponding to each detection angle of the single metasurface unit within the working band is obtained; Based on the second actual phase value corresponding to each detection angle of a single metasurface unit within the working band, the first actual phase distribution of the entire metasurface within the working band is obtained.

3. The design method of infrared polarization control metasurface according to claim 2, characterized in that: Based on the final polarization bidirectional reflectance distribution function model constructed, the polarization characteristic data of the object at each detection angle within the working band is obtained, including: Acquiring an image dataset of the sample material and processing the image dataset to obtain a measured polarization characteristic dataset; the image dataset is obtained by photographing the sample material at different incident angles and detection angles using a bidirectional reflectance distribution function measurement device; Constructing an initial polarization bidirectional reflectance distribution function model, and obtaining a polarization characteristic expression based on the initial polarization bidirectional reflectance distribution function model; The polarization characteristic expression is processed by Fourier series expansion to obtain an expanded polarization characteristic expression, and the expanded polarization characteristic expression is processed by fast Fourier transform to obtain the initial value of the parameter to be inverted; Based on the measured polarization characteristic data set and the initial value of the parameter to be inverted, a quasi-Newton algorithm is used to perform parameter inversion on the expanded polarization characteristic expression to obtain a final value of the parameter to be inverted; Based on the final value of the parameter to be inverted and the expanded polarization characteristic expression, a final polarization bidirectional reflectance distribution function model is obtained, and based on the final polarization bidirectional reflectance distribution function model, the object polarization characteristic data at each detection angle in the working band is obtained.

4. The design method of the infrared polarization control metasurface according to claim 3, characterized in that: Based on the measured polarization characteristic data set and the initial value of the parameter to be inverted, a quasi-Newton algorithm is used to perform parameter inversion on the expanded polarization characteristic expression to obtain a final value of the parameter to be inverted, specifically including: Based on the value of the parameter to be inverted at the current iteration number and the expanded polarization characteristic expression, a predicted value of the polarization characteristic at the current iteration number is calculated; the value of the parameter to be inverted at the current iteration number is the initial value of the parameter to be inverted in the first iteration; Based on the polarization characteristic prediction value and the corresponding polarization characteristic measurement value at the current iteration number, a first objective function is constructed, and a gradient value of the first objective function at the current iteration number is calculated; Determine whether the gradient value of the first objective function under the current number of iterations meets the preset convergence condition; If so, the value of the parameter to be inverted at the current number of iterations is output, and the value of the parameter to be inverted at the current number of iterations is used as the final value of the parameter to be inverted; If not, based on the approximate Hessian matrix at the current number of iterations and the gradient value of the first objective function, the search direction and search step at the current number of iterations are calculated, and based on the search direction and search step at the current number of iterations, the parameter value to be inverted and the approximate Hessian matrix at the next number of iterations are calculated, and the step of "calculating the polarization characteristic prediction value at the current number of iterations based on the parameter value to be inverted at the current number of iterations and the expanded polarization characteristic expression" is returned to.

5. The design method of the infrared polarization control metasurface according to claim 2, characterized in that: Based on the second actual phase value corresponding to each detection angle of a single metasurface unit within the working band, a first actual phase distribution of the entire metasurface within the working band is obtained, specifically including: Based on the obtained period of each metasurface unit and the radius and focus of the overall metasurface, the center coordinates of each metasurface unit are calculated; Based on the center coordinates of each metasurface unit and the focus of the entire metasurface, the relative detection angle of each metasurface unit is calculated; Based on the second actual phase value corresponding to each detection angle of a single metasurface unit within the working band and the relative detection angle of each metasurface unit, the second actual phase value of each metasurface unit within the working band is matched, and based on the second actual phase value of each metasurface unit within the working band, the first actual phase distribution of the overall metasurface within the working band is obtained.

6. The design method of the infrared polarization control metasurface according to claim 1, characterized in that: Based on the first actual phase distribution of the overall metasurface within the working band, the unknown parameters in the constructed initial achromatic formula are inverted using the improved particle swarm optimization algorithm to obtain the final achromatic formula corresponding to each characteristic wavelength, specifically including: Constructing a second objective function; the second objective function is determined based on the second actual phase value of each metasurface unit within the working band and the phase value calculated by the initial achromatic formula; the second actual phase value of each metasurface unit within the working band is determined by the first actual phase distribution of the entire metasurface within the working band; Based on the second objective function and the current iteration position of each particle, the current iteration fitness value of each particle is calculated, and the current iteration position and the current iteration fitness value of each particle are stored to obtain the individual position and the individual fitness value, and the minimum fitness value among the individual fitness values ​​is selected as the optimal fitness value of the group, and the particle position corresponding to the optimal fitness value of the group is recorded as the optimal position of the group; the current iteration position of each particle is an additional phase value of the current iteration; the current iteration position and the current iteration speed of each particle are calculated by using the Latin hypercube sampling algorithm in the first iteration; Determine whether the current number of iterations has reached the maximum number of iterations; If so, the optimal position of the group is output and used as the additional phase value; If not, the next iteration position and the next iteration speed of each particle are updated, and the process returns to step "calculating the current iteration fitness value of each particle based on the second objective function and the current iteration position of each particle".

7. The design method of the infrared polarization control metasurface according to claim 1, characterized in that: Based on the first actual phase value of each metasurface unit at all characteristic wavelengths and the constructed scanning parameter database, the size parameters of each metasurface unit are obtained, including: Performing a difference sum operation on the first actual phase value of each metasurface unit at all characteristic wavelengths and each phase value in the constructed scanning parameter database to obtain the sum of the phase difference values ​​of each metasurface unit at all characteristic wavelengths; Based on the sum of the phase difference values ​​of each metasurface unit at all characteristic wavelengths, the phase value corresponding to the minimum sum of the phase difference values ​​in the scanning parameter database is selected, and the unit structure parameter index of the phase value corresponding to the minimum sum of the phase difference values ​​in the scanning parameter database is assigned to the corresponding unit to obtain the size parameters of each metasurface unit.

8. The design method of infrared polarization control metasurface according to claim 1, characterized in that: The operating band is 8~11μm; The characteristic wavelength is a wavelength selected from the working band at intervals of 0.5 μm.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and runnable on the processor, characterized in that the processor executes the computer program to implement the design method of the infrared polarization control metasurface according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for designing an infrared polarization-controlled metasurface according to any one of claims 1 to 8 is implemented.

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