Four-corner code type terahertz absorber and design method thereof

Abstract

The invention discloses a four-corner code type terahertz absorber and a design method thereof, and relates to the technical field of microwave absorbers, and the absorber comprises a metal resonance layer, an intermediate dielectric layer and a metal reflection layer which are sequentially arranged from top to bottom; the metal resonance layer comprises a plurality of metal microstructures which are periodically arranged, each metal microstructure comprises four corner brace type metal patches, and right angles of the four corner brace type metal patches are aligned with the central position and are distributed in a central symmetry manner; the width and the length of each corner-code-type metal patch and the distance between two adjacent corner-code-type metal patches are obtained through multi-target particle swarm optimization optimization design, rapid design of the four-corner-code-type terahertz absorber can be achieved, simultaneous optimization design of two or more performance indexes is achieved, and the design efficiency and the absorption performance are improved.

Description

Technical Field

[0001] The present application relates to the technical field of microwave absorbers, and in particular to a four-corner code type terahertz absorber and a design method thereof. Background Art

[0002] Terahertz (THz) waves are electromagnetic waves between microwaves and far infrared rays. With the rapid development of photonics and nanotechnology in recent years, combined with the many advantages of THz waves, the research on THz science has been applied to information communication, national security, quality management of food and agricultural products, and ultra-high-speed computing. THz radiation has the dual characteristics of microwaves and light waves. THz can penetrate most dielectric materials and will not cause damage to the object being measured due to its low photon energy, making THz widely used in many fields such as cancer and tumor treatment, non-destructive testing, and medical imaging.

[0003] The development and utilization of terahertz waves are inseparable from terahertz functional devices. Terahertz absorbers are a basic functional device for terahertz applications and can be widely used in micro-bolometers, detectors, spectrum imaging, stealth and other aspects.

[0004] The physical modeling and simulation solution process of the terahertz absorber microstructure is time-consuming and usually requires repeated modification of parameters and continuous iterative calculations to obtain better property indicators. The development cycle is long, which is not conducive to the practical application of terahertz metamaterial sensors. Summary of the invention

[0005] The purpose of this application is to provide a four-corner code type terahertz absorber and a design method thereof, which can realize the rapid design of the four-corner code type terahertz absorber, achieve simultaneous optimization design of two or more performance indicators, and improve design efficiency and absorption performance.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a four-corner code type terahertz absorber, comprising: a metal resonance layer, an intermediate dielectric layer and a metal reflection layer arranged in sequence from top to bottom;

[0008] The metal resonance layer includes a plurality of periodically arranged metal microstructures, which are used to resonate with the incident terahertz wave and refract the incident terahertz wave to the intermediate dielectric layer;

[0009] The intermediate dielectric layer is used to receive the terahertz wave refracted by the metal resonance layer and propagate it to the metal reflection layer; or to receive the terahertz wave reflected back by the metal reflection layer and propagate it to the metal resonance layer;

[0010] The metal reflective layer is used to reflect the terahertz wave propagating in the intermediate dielectric layer to the intermediate dielectric layer;

[0011] The metal microstructure includes four corner code type metal patches, which are right-angled to the center position and are centrally symmetrically distributed; the width and length of each corner code type metal patch and the distance between two adjacent corner code type metal patches are obtained by multi-objective particle swarm algorithm optimization design.

[0012] Optionally, the corner code type metal patch includes a transverse metal patch and a longitudinal metal patch, and one end of the transverse metal patch and one end of the longitudinal metal patch are vertically connected.

[0013] Optionally, the material used for the metal resonance layer is copper, the thickness of which is 0.2 μm, and the arrangement period of the metal microstructure is 200 μm.

[0014] Optionally, the intermediate dielectric layer is a square structure, and the material used for the intermediate dielectric layer is polytetrafluoroethylene with a thickness of 15 μm.

[0015] Optionally, the metal reflective layer is a square structure, the material used for the metal reflective layer is copper, and the thickness is 0.2 μm.

[0016] In a second aspect, the present application provides a method for designing a four-corner code type terahertz absorber, comprising:

[0017] Determining structural parameters of the metal microstructure, including the width and length of the corner code type metal patch and the distance between two adjacent corner code type metal patches;

[0018] Determining an optimization target of the absorber, wherein the optimization target is the absorptivity and quality factor of the absorber;

[0019] Taking maximization of the optimization target as the goal, a multi-objective particle swarm algorithm is used to solve the optimal structural parameters of the metal microstructure.

[0020] Optionally, the method of maximizing the optimization target and using a multi-objective particle swarm algorithm to solve the optimal structural parameters of the metal microstructure specifically includes:

[0021] The structural parameters of the metal microstructure are used as position vector elements of particles in the multi-objective particle swarm algorithm, the particles in the population are initialized, and the initial value of the iteration number t is set to 1;

[0022] Calculate the absorption rate and quality factor corresponding to each particle in the population of the tth iteration;

[0023] Add the undominated particles in the population of the t-th iteration to the optimal solution set; the condition of being undominated is that one of the absorption rate and quality factor of the particle is always better than the absorption rate and quality factor of the other particles; the optimal solution set includes the undominated particles in the population of the t-th iteration and before the t-th iteration;

[0024] Determine whether the iteration stop condition is met and obtain the determination result;

[0025] If the judgment result is yes, determining the structural parameters represented by the position vector of the particle with the best absorptivity and quality factor in the optimal solution set as the optimal structural parameters of the metal microstructure;

[0026] If the judgment result is no, then select a particle as the optimal particle from the optimal solution set corresponding to the population of the t-th iteration, update the position vector element of each particle in the population, set t=t+1, and return to the step of "calculating the absorption rate and quality factor corresponding to each particle in the population of the t-th iteration".

[0027] Optionally, the selecting a particle from the optimal solution set corresponding to the population at the t-th iteration as the optimal particle specifically includes:

[0028] Determine the maximum value of the absorption rate, the minimum value of the absorption rate, the maximum value of the quality factor and the minimum value of the quality factor in the optimal solution set corresponding to the population of the t-th iteration respectively, establish a coordinate system with the absorption rate as the vertical axis and the quality factor as the horizontal axis, and divide the grid boundary according to the maximum and minimum values ​​of the absorption rate, and the maximum and minimum values ​​of the quality factor; wherein, the horizontal coordinate of the lower left corner point of the grid boundary is the minimum value of the quality factor, and the vertical coordinate of the lower left corner point is the minimum value of the absorption rate; the horizontal coordinate of the upper right corner point of the grid boundary is the maximum value of the quality factor, and the vertical coordinate of the upper right corner point is the maximum value of the absorption rate; the rectangular area formed by connecting the two diagonal points of the lower left corner point and the upper right corner point is used as the divided grid boundary;

[0029] The grid boundary is uniformly divided into small grids by an adaptive grid method, and the number of particles in each small grid is calculated as the crowding density;

[0030] The probability of each small grid being selected is calculated according to the crowding density, and the optimal particle is selected by the roulette algorithm according to the probability, wherein the probability calculation formula is:

[0031]

[0032] Among them, PR j represents the probability of the jth grid being selected, Numj represents the number of particles in the jth grid with particles; J represents the total number of grids with particles; beta represents the optimal selection pressure.

[0033] Optionally, the iteration stop condition includes two conditions;

[0034] There is at least one particle in the optimal solution set corresponding to the population of the tth iteration, whose quality factor is greater than the preset quality factor and whose absorption rate is greater than the preset absorption rate;

[0035] Or the number of iterations t of the population reaches the preset total number of iterations.

[0036] Optionally, the position update formula of the position vector element of each particle in the update population is:

[0037]

[0038]

[0039] in, represents the velocity vector of the i-th particle in the population at the t+1th iteration, ω represents the inertia weight, represents the velocity vector of the i-th particle in the population of the t-th iteration, c1 and c2 represent the first acceleration factor and the second acceleration factor respectively; r1 and r2 represent the first random number and the second random number in the range of (0, 1) respectively, p id represents the individual optimal position vector of the i-th particle, g d represents the global optimal position vector of the population, represents the position vector of the i-th particle in the population at the t-th iteration, Represents the position vector of the i-th particle in the population at the t+1th iteration.

[0040] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0041] The present application provides a four-corner code type terahertz absorber and its design method, equipment and medium. By accurately setting the period of the metal microstructure and carefully optimizing the optimized parameters such as the width, length and distance between two adjacent corner code type metal patches, the present application solves the problem of blind and inefficient structural parameter selection in the design of traditional terahertz absorbers, and realizes efficient capture and absorption of terahertz waves. The present application clearly takes the absorptivity and quality factor of the absorber as the optimization target, and uses a multi-objective particle swarm algorithm to perform iterative optimization based on the randomly generated initial population (each population position corresponds to a set of optimized parameter solutions), which solves the limitation of single-target optimization in traditional design methods, and ensures high quality factors while improving the absorptivity, thereby realizing the rapid design of metamaterial absorbers.

[0042] In summary, through the above steps, the present application successfully solves the performance bottleneck existing in the design of terahertz absorbers, achieves the perfect absorption target with an absorption rate close to 100%, and significantly improves the quality factor of the absorber. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0044] Figure 1 A top view of a four-corner terahertz absorber provided in one embodiment of the present application;

[0045] Figure 2 A schematic structural diagram of a four-corner terahertz absorber provided in one embodiment of the present application;

[0046] Figure 3 A schematic flow chart of a design method of a four-corner terahertz absorber provided in one embodiment of the present application;

[0047] Figure 4 A schematic flow chart of a design method for determining a terahertz absorber using a multi-objective particle swarm algorithm provided in an embodiment of the present application;

[0048] Figure 5 The adaptive grid flow chart provided in one embodiment of the present application is as follows: Figure 5 (a) is a schematic diagram of the grid boundary. Figure 5 (b) is a schematic diagram of grid boundary expansion. Figure 5 (c) is a schematic diagram of small grid division. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] In order to make the above-mentioned objects, 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.

[0051] Embodiment 1

[0052] like Figure 1 and Figure 2As shown, an embodiment of the present application provides a four-corner code type terahertz absorber, including a metal resonance layer, an intermediate dielectric layer and a metal reflection layer arranged in sequence from top to bottom.

[0053] The metal resonance layer includes a plurality of periodically arranged metal microstructures for resonating with the incident terahertz wave and refracting the incident terahertz wave to the intermediate dielectric layer, thereby reducing reflection of the electromagnetic wave on the interface and allowing more electromagnetic waves to enter the absorber.

[0054] The intermediate dielectric layer is used to receive the terahertz wave refracted by the metal resonance layer and propagate it to the metal reflection layer; or to receive the terahertz wave reflected back by the metal reflection layer and propagate it to the metal resonance layer.

[0055] The metal reflective layer is used to reflect the terahertz wave propagating in the intermediate dielectric layer to the intermediate dielectric layer, and prevent the incident terahertz wave from continuing to propagate forward, so that the terahertz wave is not transmitted out.

[0056] The metal microstructure includes four corner code metal patches, which are L-shaped; the right angles of the four L-shaped metal patches are aligned with the center position and are centrally symmetrically distributed; the width and length of each L-shaped metal patch and the distance between two adjacent L-shaped metal patches are obtained by multi-objective particle swarm algorithm optimization design.

[0057] As an optional implementation, the L-shaped metal patch includes a transverse metal patch and a longitudinal metal patch, and one end of the transverse metal patch and one end of the longitudinal metal patch are vertically connected.

[0058] As an optional implementation, the thickness of the metal resonance layer and the metal reflection layer are both 0.2 μm, the thickness of the intermediate dielectric layer is 15 μm, and the period of the metal microstructure is 200 μm.

[0059] As an optional implementation manner, the metal reflective layer and the intermediate dielectric layer are both square structures.

[0060] As an optional implementation, the metal resonance layer and the metal reflection layer are made of copper, and the intermediate dielectric layer is made of PTFE polytetrafluoroethylene. 7 S / m, pure copper patch with a thickness of 0.2μm; the electromagnetic loss is mainly caused by the dielectric loss of the filling material in the intermediate dielectric layer, and the dielectric constant ε r =2.1, PTFE polytetrafluoroethylene with a thickness of 15μm.

[0061] Embodiment 2

[0062] The present application embodiment provides a design method for the four-corner code type terahertz absorber described in the first embodiment, such as Figure 3 As shown, including:

[0063] Step 101, determining the structural parameters of the metal microstructure, including the width and length of the corner code type metal patch and the distance between two adjacent corner code type metal patches.

[0064] Step 102, determining the optimization target of the absorber, wherein the optimization target is the absorption rate and quality factor of the absorber.

[0065] Step 103 , taking maximization of the optimization target as the goal, a multi-objective particle swarm algorithm is used to solve the optimal structural parameters of the metal microstructure.

[0066] In the embodiment of the present application, the number of population particles is 10, the population position range has different ranges according to the optimized parameters, the update speed is 1 / 10 of the range of each parameter, and the initial population is randomly generated.

[0067] In the embodiment of the present application, the position information of the randomly generated initial population corresponds to the three geometric structure parameters to be optimized, including the width d and length l of the metal patch of the L-shaped patch and the distance g between adjacent L-shaped patches. The operating frequency band is 1-2.5THz, and the range of each design parameter is shown in Table 1.

[0068] Table 1 Variation range of various design parameters

[0069]

[0070] In the embodiments of the present application, Figure 4 As shown, step 103 specifically includes:

[0071] (1) Set the initial parameters of the algorithm; for example: the learning factor is 1.8, the initial inertia weight is 0.9, the ending inertia weight is 0.4, and the number of iterations is 10.

[0072] (2) Iterate according to the particle swarm algorithm. If the current iteration number is less than the total iteration number, the position information of the particles in the updated initial population is substituted into the finite-difference time-domain (FDTD) method for calculation to obtain the spectral information corresponding to each particle.

[0073] (3) Calculate the dominance relationship between the spectral information corresponding to each particle and the current optimal solution set to obtain a new optimal solution set. If a solution y is not worse than another solution z in all objectives and is better than y in at least one objective, then y is said to dominate z:

[0074]

[0075] f a Represents the a-th objective function.

[0076] (4) Calculating the particle crowding density by adaptive meshing, randomly deleting particles with large crowding density, and limiting the number of particles in the optimal solution set to a set number. Specifically, it includes: uniformly dividing small grids within the grid boundary by an adaptive meshing method, and calculating the number of particles in each small grid as the crowding density;

[0077] The probability of each small grid being selected is calculated according to the crowding density, and the optimal particle is selected by the roulette algorithm according to the probability, wherein the probability calculation formula is:

[0078]

[0079] Among them, PR j represents the probability of the jth grid being selected, Numj represents the number of particles in the jth grid with particles; J represents the total number of grids with particles; beta represents the optimal selection pressure. When the beta value is large, e (-beta*Numj) It is highly sensitive to Numj. The increase in the number of particles in the grid will significantly reduce the probability of the grid being selected, which helps the algorithm select particles in a relatively sparse grid, thereby increasing the diversity of solutions.

[0080] (5) If there is a solution in the current non-dominated solution set whose absorptivity and quality factor are both greater than the set threshold (for example, the absorptivity A is greater than 0.99 and the quality factor Q is greater than 150) or the current number of iterations reaches the total number of iterations (for example, 10 times), the optimal solution set corresponding to the current number of iterations is the final optimal solution set. Otherwise, the particle crowding density is calculated in the non-dominated solution set by adaptive mesh division, and the particles with small crowding density are randomly selected as the optimal particles. The randomly generated initial population is updated, and the spectral information corresponding to the updated particles is calculated using FDTD.

[0081] (6) The process of adaptive grid is to find the minimum and maximum values ​​of all individuals in each objective function as shown in the following figure. Figure 5 (a), where A max is the maximum value of the absorption rate, A min is the minimum value of the absorption rate, Q max is the maximum value of the quality factor, Q min is the minimum value of the quality factor, and x1-x6 represents the particle. Figure 5(b) is used as the grid boundary to ensure that the edge solution is within the grid, where LA is the minimum value of the absorption rate after expansion, UA is the maximum value of the absorption rate after expansion, LQ is the minimum value of the quality factor after expansion, and UQ is the maximum value of the absorption rate after expansion. Then, the grid is divided into adjacent Figure 5 (c) shows a small grid. One of the small grids will be randomly selected, and the number of particles in the small grid represents the probability that the solution in the grid will be selected or deleted.

[0082] (7) The iterative update phase mainly targets the particle speed and position. It is performed according to the defined formula using the initial parameters, global optimal values, and individual optimal values. After the particle position is updated, the FDTD calculation method is used to calculate the spectrum, and this process is repeated.

[0083] The update formula for updating the position vector element of each particle in the population is:

[0084]

[0085]

[0086] in, represents the velocity vector of the i-th particle in the population at the t+1th iteration, ω represents the inertia weight, represents the velocity vector of the i-th particle in the population of the t-th iteration, c1 and c2 represent the first acceleration factor and the second acceleration factor respectively; r1 and r2 represent the first random number and the second random number in the range of (0, 1) respectively, p id represents the individual optimal position vector of the i-th particle, g d represents the global optimal position vector of the population, represents the position vector of the i-th particle in the population at the t-th iteration, It represents the position vector of the ith particle in the population at the t+1th iteration. The simulation results show that the absorber output by this method can reach more than 98%, the quality factor Q can reach 979, and the design time is shortened by 3 / 4.

[0087] The embodiment of the present application can realize the rapid design of the terahertz absorber by a multi-objective particle swarm optimization algorithm, thereby improving the design efficiency and the performance of the terahertz absorber. When the terahertz absorber is subjected to vertical electromagnetic wave incident from the top layer to the bottom layer, the incident electromagnetic wave is completely consumed inside the absorber through the special metal microstructure of the top layer.

[0088] The present application also provides an application scenario, which applies the above-mentioned four-corner code type terahertz absorber design method. Specifically: The terahertz absorber design method provided in this embodiment can be applied in the radar stealth technology scenario. In this scenario, the radar stealth technology scenario includes three key links: target identification, electromagnetic wave emission, and electromagnetic wave reception and analysis. The electromagnetic wave is emitted from the radar system, reflected by the target area, and then captured and analyzed by the receiving system to achieve target identification and tracking. The terahertz absorber design method provided in this embodiment is a key technology in the electromagnetic wave reception and analysis link. Specifically, in the electromagnetic wave reception process, the designed terahertz absorber uses the special metal microstructure of the top layer to efficiently absorb and dissipate the vertically incident electromagnetic waves, so that the electromagnetic wave signal reflected back to the radar is greatly weakened, thereby achieving the stealth effect of the target. In addition, this embodiment can be quickly designed by a multi-objective particle swarm optimization algorithm, which not only improves the design efficiency, but also significantly improves the comprehensive performance of the absorber, ensuring its stability and reliability in complex electromagnetic environments.

[0089] The technical features of the above embodiments may 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.

[0090] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will 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 four-corner terahertz absorber, characterized in that: The four-corner code type terahertz absorber comprises: a metal resonance layer, an intermediate dielectric layer and a metal reflection layer arranged in sequence from top to bottom; The metal resonance layer includes a plurality of periodically arranged metal microstructures, which are used to resonate with the incident terahertz wave and refract the incident terahertz wave to the intermediate dielectric layer; The intermediate dielectric layer is used to receive the terahertz wave refracted by the metal resonance layer and propagate it to the metal reflection layer; or to receive the terahertz wave reflected back by the metal reflection layer and propagate it to the metal resonance layer; The metal reflective layer is used to reflect the terahertz wave propagating in the intermediate dielectric layer to the intermediate dielectric layer; The metal microstructure includes four corner code type metal patches, which are right-angled to the center position and are centrally symmetrically distributed; the width and length of each corner code type metal patch and the distance between two adjacent corner code type metal patches are obtained by multi-objective particle swarm algorithm optimization design.

2. The four-corner terahertz absorber according to claim 1, characterized in that: The corner code type metal patch includes a transverse metal patch and a longitudinal metal patch, and one end of the transverse metal patch and one end of the longitudinal metal patch are vertically connected.

3. The four-corner terahertz absorber according to claim 1, characterized in that: The material used for the metal resonance layer is copper, the thickness of which is 0.2 μm, and the arrangement period of the metal microstructure is 200 μm.

4. The four-corner terahertz absorber according to claim 1, characterized in that: The intermediate dielectric layer is a square structure, and the material used for the intermediate dielectric layer is polytetrafluoroethylene with a thickness of 15 μm.

5. The four-corner terahertz absorber according to claim 1, characterized in that: The metal reflective layer is a square structure, the material used for the metal reflective layer is copper, and the thickness is 0.2 μm.

6. A method for designing a four-corner terahertz absorber, characterized in that: The four-corner code type terahertz absorber design method comprises: Determining structural parameters of the metal microstructure, including the width and length of the corner code type metal patch and the distance between two adjacent corner code type metal patches; Determining an optimization target of the absorber, wherein the optimization target is the absorptivity and quality factor of the absorber; Taking maximization of the optimization target as the goal, a multi-objective particle swarm algorithm is used to solve the optimal structural parameters of the metal microstructure.

7. The method for designing a four-corner terahertz absorber according to claim 6, characterized in that: The method of maximizing the optimization target and using a multi-objective particle swarm algorithm to solve the optimal structural parameters of the metal microstructure specifically includes: The structural parameters of the metal microstructure are used as position vector elements of particles in the multi-objective particle swarm algorithm, the particles in the population are initialized, and the initial value of the iteration number t is set to 1; Calculate the absorption rate and quality factor corresponding to each particle in the population of the tth iteration; Add the undominated particles in the population of the t-th iteration to the optimal solution set; the condition of being undominated is that one of the absorption rate and quality factor of the particle is always better than the absorption rate and quality factor of the other particles; the optimal solution set includes the undominated particles in the population of the t-th iteration and before the t-th iteration; Determine whether the iteration stop condition is met and obtain the determination result; If the judgment result is yes, determining the structural parameters represented by the position vector of the particle with the best absorptivity and quality factor in the optimal solution set as the optimal structural parameters of the metal microstructure; If the judgment result is no, then a particle is selected as the optimal particle from the optimal solution set corresponding to the population of the t-th iteration, the position vector element of each particle in the population is updated, and t=t+1 is set, and the step of "calculating the absorption rate and quality factor corresponding to each particle in the population of the t-th iteration" is returned.

8. The method for designing a four-corner terahertz absorber according to claim 7, characterized in that: The step of selecting a particle as the optimal particle from the optimal solution set corresponding to the population at the t-th iteration specifically includes: Determine the maximum value of the absorption rate, the minimum value of the absorption rate, the maximum value of the quality factor and the minimum value of the quality factor in the optimal solution set corresponding to the population of the t-th iteration respectively, establish a coordinate system with the absorption rate as the vertical axis and the quality factor as the horizontal axis, and divide the grid boundary according to the maximum and minimum values ​​of the absorption rate, and the maximum and minimum values ​​of the quality factor; wherein, the horizontal coordinate of the lower left corner point of the grid boundary is the minimum value of the quality factor, and the vertical coordinate of the lower left corner point is the minimum value of the absorption rate; the horizontal coordinate of the upper right corner point of the grid boundary is the maximum value of the quality factor, and the vertical coordinate of the upper right corner point is the maximum value of the absorption rate; the rectangular area formed by connecting the two diagonal points of the lower left corner point and the upper right corner point is used as the divided grid boundary; The grid boundary is uniformly divided into small grids by an adaptive grid method, and the number of particles in each small grid is calculated as the crowding density; The probability of each small grid being selected is calculated according to the crowding density, and the optimal particle is selected by the roulette algorithm according to the probability, wherein the probability calculation formula is: Among them, PR j represents the probability of the jth grid being selected, Numj represents the number of particles in the jth grid with particles; J represents the total number of grids with particles; beta represents the optimal selection pressure.

9. The method for designing a four-corner terahertz absorber according to claim 7, characterized in that: The iteration stop condition includes two conditions: There is at least one particle in the optimal solution set corresponding to the population of the tth iteration, whose quality factor is greater than the preset quality factor and whose absorption rate is greater than the preset absorption rate; Or the number of iterations t of the population reaches the preset total number of iterations.

10. The method for designing a four-corner terahertz absorber according to claim 7, characterized in that: The position update formula of the position vector element of each particle in the update population is: in, represents the velocity vector of the i-th particle in the population at the t+1th iteration, ω represents the inertia weight, represents the velocity vector of the i-th particle in the population of the t-th iteration, c1 and c2 represent the first acceleration factor and the second acceleration factor respectively; r1 and r2 represent the first random number and the second random number in the range of (0, 1) respectively, p id represents the individual optimal position vector of the i-th particle, g d represents the global optimal position vector of the population, represents the position vector of the i-th particle in the population at the t-th iteration, Represents the position vector of the i-th particle in the population at the t+1th iteration.

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