Infrared broadband metasurface absorber and intelligent design method and device thereof

Abstract

The application discloses an infrared wide-band super surface absorber and an intelligent design method and device thereof, and is based on an intelligent design method of a particle swarm optimization algorithm, and converts a design process of an absorption rate performance parameter target to be reached by a super surface to be designed into a process of optimizing a target extreme value of an optimized function. By using an optimization strategy and adjusting a plurality of variables in the optimized function, adjustment of structure parameters of the super surface is realized, so that an optimal value of the optimized function, i.e. maximization of the absorption rate of the super surface, is realized. By using the method, in the face of a complex electromagnetic environment and a complex optical antenna design scene, the dependence of a structure designer on electromagnetic theory knowledge and experience can be reduced, and the design efficiency of a complex super surface can be greatly improved and the design time can be reduced.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic metamaterials design technology, and in particular to an infrared broadband metasurface absorber and its intelligent design method and device. Background Technology

[0002] Metamaterials are three-dimensional composite materials composed of subwavelength unit structures distributed according to artificial structures in a periodic or quasi-periodic manner. They can possess certain properties that are not found in natural materials, such as negative permittivity and negative permeability.

[0003] The design process of metamaterials is extremely complex, requiring significant time and computational resources. Therefore, researchers have conducted extensive research in the field, exploring algorithms and tools for metamaterial design. Currently, there are two main approaches: one employs theoretical calculations based on physics, utilizing existing theories such as Mie theory or equivalent medium models like the Lewin and GEM models. This method requires theoretical knowledge of electromagnetic fields and equivalent models and can only design metamaterials with simple structures. The other approach, and currently the most commonly used, is a semi-automatic method, such as the finite element method, finite difference method, finite integral method, or method of moments. This method relies on iterative full-wave numerical simulation tools like COMSOL and CST for case-by-case analysis and trial-and-error. Solving the forward problem using this method begins with certain initial and boundary conditions, solving spatially and temporally discrete Maxwell's equations, setting sufficient meshes and iteration steps. Under the premise that these conditions or parameters are correctly set, the program can accurately calculate the electromagnetic response of a given structure. If the desired response is obtained, then the structure is the desired one; otherwise, fine-tuning the geometry and repeatedly running simulations are required to gradually approach the target response. Furthermore, due to limitations in designer capabilities and computational resources, only a limited number of design parameters are adjusted when searching for the optimal structure, and the resulting outcome may not be optimal. Current numerical simulation tools can only perform calculations case-by-case based on the set structural parameters of the metamaterial; they cannot perform reverse design, i.e., designing metamaterial structures with on-demand electromagnetic responses. Because of the complex and non-intuitive relationship between the two, as metamaterial structures become increasingly complex, even the most mature numerical simulation tools struggle to solve this problem.

[0004] Traditional metasurface intelligent design is often achieved through analyzing physical models and establishing numerical simulation methods that correlate the internal unit structure with electromagnetic wave response. This requires designers to have a lot of electromagnetic theory knowledge and rich design experience, resulting in a high design threshold. Furthermore, when facing engineering applications, it is likely to involve optimization design on the order of millions. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide an infrared broadband metasurface absorber and its intelligent design method and device, so as to solve the limitations of the existing technology, such as the need for continuous trial and error, case-by-case calculation and analysis in the design process of traditional metamaterial absorbers, and the need for designers to have previous simulation experience and professional knowledge related to metamaterials.

[0006] Therefore, the technical solution of the present invention is:

[0007] According to a first aspect of the present invention, an infrared broadband metasurface absorber is provided, wherein the metasurface absorber utilizes a Schottky junction at a silicon-based metal-silicon interface to achieve efficient interlayer electron transition excitation, thereby completing an efficient photoelectric conversion process.

[0008] Furthermore, the metasurface absorber includes a silicon substrate, an optical antenna, and silicon dioxide. The optical antenna is embedded in the silicon dioxide and surrounded by via aluminum. The via aluminum is in contact with cobalt silicide, and the cobalt silicide is fabricated on the silicon substrate.

[0009] According to a second aspect of the present invention, a smart design method for an infrared broadband metasurface absorber as described above is provided, the method comprising:

[0010] Set the operating parameters of the supersurface absorber;

[0011] Generating simulation calculation model: Based on the preset working parameters, generate a simulation model of the metasurface absorber except for the optical antenna; encode one periodic unit of the optical antenna as a binary grid to represent it, using a 0-1 matrix description, where "1" represents cobalt silicide material and "0" represents free space, randomly generate n initial matrices, that is, generate n initial particles, corresponding to n random geometric structures of the metasurface, and generate the initial optical antenna structure based on the generated matrices;

[0012] Initialize the particle swarm parameters, which include particle dimension, learning factors c1 and c2, weight factor, population size, iteration step size range, and maximum number of iterations.

[0013] Randomly initialize the position and velocity of each particle;

[0014] Obtain the fitness values ​​of all particles: Construct an electromagnetic simulation model, perform finite-difference time-domain electromagnetic simulation to obtain the absorption spectrum of the target frequency band, obtain the absorptivity of the target frequency band for n random geometric structures, and calculate the fitness value of each particle;

[0015] Predict the individual best value for each particle: For any particle, compare its current fitness with the previous individual best value. If the current fitness is better than the previous individual best value, then the individual best value is updated to the current fitness value; otherwise, the individual best value is the previous individual best value.

[0016] Determine whether the optimal value of an individual particle meets a set threshold. If the set threshold is not met, update the position and velocity of each particle, and reacquire the fitness values ​​of all particles and predict the optimal value of each individual particle until the optimal value of an individual particle meets the set threshold. If the set threshold is met, generate an optical antenna structure based on the determined fitness values ​​of the particles.

[0017] Furthermore, the following method is used to determine whether the optimal value of an individual particle meets the set threshold:

[0018] The maximum absorptivity of the target frequency band is used as the fitness function and a threshold is set, defined as Fmax=∑A(λi)2, to evaluate the absorption spectrum characteristics of each pixelated structure, where A is the absorptivity of the metasurface and λi is the wavelength of the incident light wave, with the wavelength range set to 1μm to 2μm.

[0019] Furthermore, the setting of the operating parameters of the metasurface absorber specifically includes:

[0020] The operating wavelength range of the metasurface absorber was set to 1-2 μm. When X-polarized light was incident on the surface of the metasurface structure at incident angles of 0° and 90°, multiple scanning points were set within the wavelength range, and the absorption spectrum composed of the absorptivity at different frequency points was obtained by calculation.

[0021] Furthermore, the random initialization of the position and velocity of each particle specifically includes:

[0022] Under certain constraints, the initial velocity and orientation of particles are arbitrarily set, and the initial orientation of all particles is set to the individual global optimal value Pi, where the optimal value in Pi is the global optimal value Pg.

[0023] According to a third aspect of the present invention, a smart design apparatus for an infrared broadband metasurface absorber as described above is provided, the apparatus comprising:

[0024] The parameter setting module is configured to set the operating parameters of the supersurface absorber;

[0025] The model generation module is configured to generate a simulation model of the metasurface absorber, excluding the optical antenna, based on preset operating parameters. The periodic unit of the optical antenna is encoded as a binary mesh and described using a 0-1 matrix, where "1" represents cobalt silicide material and "0" represents free space. n initial matrices are randomly generated, which are n initial particles, corresponding to n random geometric structures of the metasurface. Based on the generated matrices, the initial optical antenna structure is generated.

[0026] The parameter initialization module is configured to initialize particle swarm parameters, which include particle dimension, learning factors c1 and c2, weight factor, population size, iteration step size range, and maximum number of iterations.

[0027] The random initialization module is configured to randomly initialize the position and velocity of each particle.

[0028] The fitness value acquisition module is configured to acquire the fitness values ​​of all particles: construct an electromagnetic simulation model, perform a finite-difference time-domain electromagnetic simulation to obtain the absorption spectrum of the target frequency band, obtain the absorptivity of the target frequency band of n random geometric structures, and calculate the fitness value of each particle.

[0029] The prediction module is configured to predict the individual best value for each particle: for any particle, its current fitness is compared with the previous individual best value. If the current fitness is better than the previous individual best value, the individual best value is updated to the current fitness value; otherwise, the individual best value is the previous individual best value.

[0030] The decision processing module is configured to determine whether the optimal value of an individual particle meets a set threshold. If the set threshold is not met, the position and velocity of each particle are updated, and the fitness values ​​of all particles are reacquired and the optimal value of each individual particle is predicted until the optimal value of an individual particle meets the set threshold. If the set threshold is met, an optical antenna structure is generated based on the determined fitness values ​​of the particles.

[0031] Furthermore, the decision processing module is further configured to determine whether the optimal value of an individual particle meets a set threshold using the following method:

[0032] The maximum absorptivity of the target frequency band is used as the fitness function and a threshold is set, defined as Fmax=∑A(λi)2, to evaluate the absorption spectrum characteristics of each pixelated structure, where A is the absorptivity of the metasurface and λi is the wavelength of the incident light wave, with the wavelength range set to 1μm to 2μm.

[0033] Furthermore, the parameter setting module is further configured as follows:

[0034] The operating wavelength range of the metasurface absorber was set to 1-2 μm. When X-polarized light was incident on the surface of the metasurface structure at incident angles of 0° and 90°, multiple scanning points were set within the wavelength range, and the absorption spectrum composed of the absorptivity at different frequency points was obtained by calculation.

[0035] Furthermore, the random initialization module is further configured as follows:

[0036] Under certain constraints, the initial velocity and orientation of particles are arbitrarily set, and the initial orientation of all particles is set to the individual global optimal value Pi, where the optimal value in Pi is the global optimal value Pg.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] This invention uses an intelligent optimization method for the intelligent design of encoded metasurfaces, which can reduce the computational scale and accelerate the reverse design process. Relying on the fitting ability of the particle swarm optimization algorithm, the corresponding metasurface structure can be directly obtained through the absorption rate response curve. This effectively solves the limitations of existing technologies, such as the need for continuous trial and error and case-by-case calculation and analysis in the design process of traditional metamaterial absorbers, and the need for designers to have previous simulation experience and professional knowledge related to metamaterials. Attached Figure Description

[0039] In drawings that are not necessarily drawn to scale, the same reference numerals may describe similar parts in different views. The same reference numerals with or without letter suffixes may indicate different instances of similar parts. The drawings generally illustrate various embodiments by way of example rather than limitation and, together with the description and claims, serve to explain embodiments of the invention. Where appropriate, the same reference numerals are used in all drawings to refer to the same or similar parts. Such embodiments are illustrative and not intended to be exhaustive or exclusive embodiments of the apparatus or method.

[0040] Figure 1 This is a schematic diagram of a short-wave infrared broadband metasurface absorber structure according to an embodiment of the present invention.

[0041] Figure 2a This is a schematic diagram of a metasurface structure according to an embodiment of the present invention.

[0042] Figure 2b This is a schematic diagram of the digital encoding of a metasurface structure according to an embodiment of the present invention.

[0043] Figure 3 This is a flowchart illustrating an intelligent design method for an infrared broadband metasurface absorber according to an embodiment of the present invention.

[0044] Figure 4This is a structural diagram of an intelligent design device for an infrared broadband metasurface absorber according to an embodiment of the present invention. Detailed Implementation

[0045] The following examples are merely illustrative of the invention, and the scope of the invention is not limited to the embodiments described. Therefore, any non-essential modifications and adjustments made by those skilled in the art based on the above description to other embodiments are still within the scope of protection of this invention.

[0046] The invention will now be further described with reference to the accompanying drawings.

[0047] This invention provides an infrared broadband metasurface absorber that utilizes a Schottky junction at a silicon-based metal-silicon interface to achieve efficient interlayer electron transition excitation, thereby completing an efficient photoelectric conversion process.

[0048] In some embodiments, such as Figure 1 As shown, the metasurface absorber includes a silicon substrate 1, an optical antenna 2, and silicon dioxide 3. The optical antenna 1 is embedded in the silicon dioxide 3. The optical antenna 2 is surrounded by via aluminum 4. The via aluminum 4 is in contact with cobalt silicide 5. The cobalt silicide 5 is fabricated on the silicon substrate 1.

[0049] Based on the metasurface absorber described above, this invention also provides a smart design method for a short-wave infrared broadband metasurface absorber, which is implemented according to the following steps:

[0050] Step 1: Set the operating parameter range of the supersurface absorber

[0051] The operating wavelength range is 1-2 μm. X-polarized light is incident on the surface of the metasurface structure at incident angles of 0° and 90°. 200 scanning points are set within the wavelength range, and the absorption spectrum composed of the absorptivity at different frequency points is obtained by calculation.

[0052] Step 2: Generate simulation calculation model

[0053] Based on the preset operating parameters, a simulation model of the metasurface absorber, excluding the optical antenna, is generated in the FDTD Solution Lumerical electromagnetic simulation software.

[0054] One periodic unit of the optical antenna is encoded as a binary grid, described by a 0-1 matrix, where "1" represents cobalt silicide material and "0" represents free space (where cobalt silicide material is absent). n initial matrices are randomly generated, corresponding to n initial particles and their random geometric structures on the metasurface in FDTD. Based on the generated matrices, the initial optical antenna structure is generated in the FDTD Solution Lumerical electromagnetic simulation software (e.g., ...). Figure 2a and Figure 2b (As shown).

[0055] Step 3: Initialize particle swarm parameters

[0056] This mainly includes particle dimension, learning factors c1 and c2, weight factor ω, population size, iteration step size range, and maximum number of iterations tmax.

[0057] Step 4: Randomly initialize the position and velocity of each particle.

[0058] Under certain constraints, the initial velocity and orientation of particles are arbitrarily set, and the initial orientation of all particles is set to the individual global optimal value Pi, where the optimal value in Pi is the global optimal value Pg.

[0059] Step 5: Obtain the fitness values ​​of all particles;

[0060] An electromagnetic simulation model was constructed using FDTD Solution Lumerical electromagnetic simulation software, and the absorption spectrum of the target frequency band was obtained by performing finite-difference time-domain electromagnetic simulation. The absorbance of n randomly geometrically structured target frequencies was acquired. The fitness value of each particle was calculated.

[0061] Step 6: Predict the individual optimal value for each particle

[0062] For any particle, its current fitness is compared with the previous individual's optimal value Pi. If the current fitness is better than Pi, then Pi is the current fitness value; otherwise, it remains the original Pi. If the fitness value meets the requirements, proceed to step 8; otherwise, proceed to step 7.

[0063] In some embodiments, the maximum absorptivity of the target frequency band is used as the fitness function and a threshold is set. Defined as Fmax = ∑A(λi)², it is used to evaluate the absorption spectral characteristics of each pixelated structure, where A is the absorptivity of the metasurface, and λi is the wavelength of the incident light wave, with the wavelength λ ranging from 1 μm to 2 μm.

[0064] Step 7: Using the fitness values, update the position and velocity of each particle, generate the next batch of optical antenna structures, and return to step 5.

[0065] Step 8: Generate the optical antenna structure in FDTD using the optimal value determined in Step 5. This is the optimal solution of the structure optimization model, and the optimization is complete.

[0066] The design method provided by this invention saves a lot of human and computer resources and can effectively avoid the local optima problem that exists in the human design process.

[0067] This invention also provides an intelligent design device for short-wave infrared broadband metasurface absorbers, such as... Figure 4 As shown, the device 400 includes:

[0068] The parameter setting module 401 is configured to set the operating parameters of the supersurface absorber.

[0069] The model generation module 402 is configured to generate a simulation model of the metasurface absorber, excluding the optical antenna, based on preset operating parameters. The optical antenna is represented by a binary grid encoded as a periodic unit, described by a 0-1 matrix, where "1" represents cobalt silicide material and "0" represents free space. n initial matrices are randomly generated, which are n initial particles, corresponding to n random geometric structures of the metasurface. Based on the generated matrices, the initial optical antenna structure is generated.

[0070] The parameter initialization module 403 is configured to initialize particle swarm parameters, which include particle dimension, learning factors c1 and c2, weight factor, population size, iteration step size range, and maximum number of iterations.

[0071] The random initialization module 404 is configured to randomly initialize the position and velocity of each particle.

[0072] The fitness value acquisition module 405 is configured to acquire the fitness values ​​of all particles by: constructing an electromagnetic simulation model, performing a finite-difference time-domain electromagnetic simulation to obtain the absorption spectrum of the target frequency band, acquiring the absorptivity of the target frequency band of n random geometric structures, and calculating the fitness value of each particle.

[0073] The prediction module 406 is configured to predict the individual best value for each particle: for any particle, its current fitness is compared with the previous individual best value. If the current fitness is better than the previous individual best value, the individual best value is updated to the current fitness value; otherwise, the individual best value is the previous individual best value.

[0074] The decision processing module 407 is configured to determine whether the optimal value of an individual particle meets a set threshold. If the set threshold is not met, the position and velocity of each particle are updated, and the fitness values ​​of all particles are reacquired and the optimal value of each individual particle is predicted until the optimal value of an individual particle meets the set threshold. If the set threshold is met, an optical antenna structure is generated based on the determined fitness values ​​of the particles.

[0075] In some embodiments, the decision processing module is further configured to determine whether the optimal value of an individual particle satisfies a set threshold by means of the following method:

[0076] The maximum absorptivity of the target frequency band is used as the fitness function and a threshold is set, defined as Fmax=∑A(λi)2, to evaluate the absorption spectrum characteristics of each pixelated structure, where A is the absorptivity of the metasurface and λi is the wavelength of the incident light wave, with the wavelength range set to 1μm to 2μm.

[0077] In some embodiments, the parameter setting module is further configured to:

[0078] The operating wavelength range of the metasurface absorber was set to 1-2 μm. When X-polarized light was incident on the surface of the metasurface structure at incident angles of 0° and 90°, multiple scanning points were set within the wavelength range, and the absorption spectrum composed of the absorptivity at different frequency points was obtained by calculation.

[0079] In some embodiments, the random initialization module is further configured to:

[0080] Under certain constraints, the initial velocity and orientation of particles are arbitrarily set, and the initial orientation of all particles is set to the individual global optimal value Pi, where the optimal value in Pi is the global optimal value Pg.

[0081] It should be noted that the short-wave infrared broadband metasurface absorber intelligent design device provided in this embodiment belongs to the same technical concept as the prior intelligent design method, and can achieve the same beneficial effect, which will not be elaborated here.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A smart design method for an infrared broadband metasurface absorber, wherein the infrared broadband metasurface absorber comprises a silicon substrate, an optical antenna, and silicon dioxide; the optical antenna is embedded in the silicon dioxide and surrounded by via aluminum; the via aluminum is in contact with cobalt silicide, and the cobalt silicide is fabricated on the silicon substrate; efficient interlayer electron transition excitation is achieved by utilizing the Schottky junction at the cobalt silicide-silicon interface on silicon. The operating wavelength is in the infrared band of 1μm to 2μm, characterized in that, The method includes: Set the operating parameters of the supersurface absorber; Generating simulation calculation models: Based on the preset working parameters, a simulation model of the metasurface absorber, excluding the optical antenna, is generated; one periodic unit of the optical antenna is encoded as a binary grid and described using a 0-1 matrix, where "1" represents cobalt silicide material and "0" represents free space. n initial matrices are randomly generated, that is, n initial particles are generated, corresponding to n random geometric structures of the metasurface. Based on the generated matrices, the initial optical antenna structure is generated. Initialize the particle swarm parameters, which include particle dimension, learning factors c1 and c2, weight factor, population size, iteration step size range, and maximum number of iterations. Randomly initialize the position and velocity of each particle; Obtain the fitness values ​​of all particles: Construct an electromagnetic simulation model, perform finite-difference time-domain electromagnetic simulation to obtain the absorption spectrum of the target frequency band, obtain the absorptivity of the target frequency band for n random geometric structures, and calculate the fitness value of each particle; Predict the individual best value for each particle: For any particle, compare its current fitness with the previous individual best value. If the current fitness is better than the previous individual best value, then the individual best value is updated to the current fitness value; otherwise, the individual best value is the previous individual best value. Determine whether the optimal value of an individual particle meets a set threshold. If the set threshold is not met, update the position and velocity of each particle, and reacquire the fitness values ​​of all particles and predict the optimal value of each individual particle until the optimal value of an individual particle meets the set threshold. If the set threshold is met, generate an optical antenna structure based on the determined fitness values ​​of the particles.

2. The method as described in claim 1, characterized in that, The following method is used to determine whether the optimal value of an individual particle meets a set threshold: The maximum absorptivity of the target frequency band is used as the fitness function and a threshold is set, defined as 𝐹max=∑A(𝜆i)2, to evaluate the absorption spectrum characteristics of each pixelated structure, where A is the absorptivity of the metasurface and 𝜆i is the wavelength of the incident light wave, with the wavelength range set to 1 μm to 2 μm.

3. The method as described in claim 1, characterized in that, The setting of the operating parameters of the metasurface absorber specifically includes: The operating wavelength range of the metasurface absorber was set to 1-2 μm. When X-polarized light was incident on the surface of the metasurface structure at incident angles of 0° and 90°, multiple scanning points were set within the wavelength range, and the absorption spectrum composed of the absorptivity at different frequency points was obtained by calculation.

4. The method as described in claim 1, characterized in that, The random initialization of the position and velocity of each particle specifically includes: Under certain constraints, the initial velocity and orientation of particles are arbitrarily set, and the initial orientation of all particles is set to the individual global optimal value Pi, where the optimal value in Pi is the global optimal value Pg.

5. An intelligent design device for an infrared broadband metasurface absorber, wherein the infrared broadband metasurface absorber includes a silicon substrate, an optical antenna, and silicon dioxide; the optical antenna is embedded in the silicon dioxide and surrounded by via aluminum; the via aluminum is in contact with cobalt silicide, which is fabricated on the silicon substrate; efficient interlayer electron transition excitation is achieved by utilizing the Schottky junction at the cobalt silicide-silicon interface on silicon. The operating wavelength is in the infrared band of 1μm to 2μm, characterized in that, The device includes: The parameter setting module is configured to set the operating parameters of the supersurface absorber; The model generation module is configured to generate a simulation model of the metasurface absorber, excluding the optical antenna, based on preset working parameters. The periodic unit of the optical antenna is encoded as a binary mesh and described using a 0-1 matrix, where "1" represents cobalt silicide material and "0" represents free space. n initial matrices are randomly generated, which means n initial particles are generated, corresponding to n random geometric structures of the metasurface. Based on the generated matrices, the initial optical antenna structure is generated. The parameter initialization module is configured to initialize particle swarm parameters, which include particle dimension, learning factors c1 and c2, weight factor, population size, iteration step size range, and maximum number of iterations. The random initialization module is configured to randomly initialize the position and velocity of each particle. The fitness value acquisition module is configured to acquire the fitness values ​​of all particles: construct an electromagnetic simulation model, perform a finite-difference time-domain electromagnetic simulation to obtain the absorption spectrum of the target frequency band, obtain the absorptivity of the target frequency band of n random geometric structures, and calculate the fitness value of each particle. The prediction module is configured to predict the individual best value for each particle: for any particle, its current fitness is compared with the previous individual best value. If the current fitness is better than the previous individual best value, the individual best value is updated to the current fitness value; otherwise, the individual best value is the previous individual best value. The decision processing module is configured to determine whether the optimal value of an individual particle meets a set threshold. If the set threshold is not met, the position and velocity of each particle are updated, and the fitness values ​​of all particles are reacquired and the optimal value of each individual particle is predicted until the optimal value of an individual particle meets the set threshold. If the set threshold is met, an optical antenna structure is generated based on the determined fitness values ​​of the particles.

6. The apparatus as claimed in claim 5, characterized in that, The decision processing module is further configured to determine whether the optimal value of an individual particle meets a set threshold using the following method: The maximum absorptivity of the target frequency band is used as the fitness function and a threshold is set, defined as 𝐹max=∑A(𝜆i)2, to evaluate the absorption spectrum characteristics of each pixelated structure, where A is the absorptivity of the metasurface and 𝜆i is the wavelength of the incident light wave, with the wavelength range set to 1 μm to 2 μm.

7. The apparatus as claimed in claim 5, characterized in that, The parameter setting module is further configured as follows: The operating wavelength range of the metasurface absorber was set to 1-2 μm. When X-polarized light was incident on the surface of the metasurface structure at incident angles of 0° and 90°, multiple scanning points were set within the wavelength range, and the absorption spectrum composed of the absorptivity at different frequency points was obtained by calculation.

8. The apparatus as claimed in claim 5, characterized in that, The random initialization module is further configured as follows: Under certain constraints, the initial velocity and orientation of particles are arbitrarily set, and the initial orientation of all particles is set to the individual global optimal value Pi, where the optimal value in Pi is the global optimal value Pg.

Images