Intelligent Metasurface Topology Structure Design Method, Device, Equipment and Medium

Abstract

The intelligent metasurface topology design method, device, equipment and medium provided by the present invention include: establishing an initial model of an intelligent metasurface unit; adding a plurality of discrete patches on the metal patch layer to establish a reflection coefficient model of the initial model of the intelligent metasurface unit; starting from the maximum number of rows and columns, reducing the number of rows or columns in turn to construct different topologies; constructing an objective function based on the reflection coefficient and using an optimization algorithm to screen the topologies, and judging whether the optimized objective function of each topology meets the design requirements until the topology with the smallest design area is obtained; using the optimized objective function combined with the number of discrete patches in the topology to optimize the topology with the smallest design area; removing the unconnected discrete patches and adjusting the positional relationship between the patches to obtain the intelligent metasurface unit. The present invention can adaptively miniaturize the intelligent metasurface unit without affecting the working effect.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic metasurfaces, and particularly to a method, device, equipment and medium for designing the topology structure of intelligent metasurfaces. Background Art

[0002] Intelligent metasurface is an advanced artificial electromagnetic structure that can precisely manipulate the amplitude, phase, polarization and frequency of incident electromagnetic waves to achieve dynamic regulation of electromagnetic waves. The application fields of intelligent metasurfaces are extensive, including beam refraction, electromagnetic stealth, filtering, wireless communication and holographic imaging, etc. Miniaturization of intelligent metasurfaces is an important development direction in the current wireless communication field. The miniaturized intelligent metasurface can integrate multi-band reflectors on the same substrate. This integrated design not only saves valuable space resources, but also reduces the complexity and cost of the system. In addition, the miniaturized intelligent metasurface can improve the performance of wireless communication systems, especially in high-frequency communications. They solve the problem of signal blind area coverage by constructing virtual line-of-sight paths, provide blind area communication and sensing services, and at the same time enhance the signal by providing additional paths to expand the system coverage radius.

[0003] Currently, the methods for designing miniaturized intelligent metasurfaces mainly include two categories. The first category is to explore the application of equivalent circuit models in the design of intelligent metasurfaces. This method simplifies the design process by converting complex electromagnetic problems into circuit problems, but it is difficult to obtain an accurate physical model for complex intelligent metasurfaces. The second category is the intelligent metasurface design method based on deep learning. This method uses deep learning technology to establish the correspondence between the structural parameters of the metasurface and its electromagnetic response to achieve the rapid design of two common types of metasurfaces, namely continuous parameter structures and digital coding structures. However, since the deep learning method requires using electromagnetic simulation samples for modeling, it is difficult to fundamentally reduce the electromagnetic simulation cost used in the design of intelligent metasurfaces.

[0004] Therefore, there is an urgent need for a miniaturized intelligent metasurface design method to overcome the technical problems existing in the prior art. Summary of the Invention

[0005] Aiming at the defects existing in the prior art, the present invention provides a method, device, equipment and medium for designing the topology structure of intelligent metasurfaces.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] On the one hand, the present invention provides a method for designing the topology structure of intelligent metasurfaces, including the following steps:

[0008] S1. Establish an initial model of an intelligent metasurface unit, including a metal patch layer, a dielectric layer and a metal bottom plate grounding layer;

[0009] S2. Add multiple discrete patches on the metal patch layer and establish a reflection coefficient model for the initial model of the intelligent metasurface unit using a multi-port network:

[0010]

[0011] Among them, is the reflection coefficient function obtained using the multi-port network; and are the internal discrete port vector and the active device parameter vector respectively; is the multi-port network model; is the load impedance of the internal discrete port; is the impedance matrix of the multi-port; is the load impedance of the external electromagnetic wave port;

[0012] S3. Traverse the number of rows and columns of the discrete patches, starting from the maximum number of rows and columns, and successively reduce the number of rows or columns to construct different topological structures of the intelligent metasurface unit;

[0013] S4. Based on the reflection coefficient function, construct an objective function using the phase and amplitude of different working states, and use an optimization algorithm to screen the topological structure to minimize the optimization objective function. The objective function is:

[0014]

[0015] The optimization process is defined as:

[0016]

[0017] Among them, and are the internal discrete port vector and the active device parameter vector respectively; and are the optimal internal discrete port vector and the optimal active device parameter vector respectively; and are the weight coefficients of the phase constraint and the amplitude constraint in the objective function respectively; is the phase of the th working state; is the ideal phase of the th working state; is a comparison function used to find the minimum value between and ; is the amplitude of the th optimized working state; is the ideal amplitude of the th working state, takes the value of 3;

[0018] S5. Determine whether the optimization objective function of each topological structure meets the design requirements. If it meets, reduce the size of the design region of the topological structure; if it does not meet, increase the size of the design region of the topological structure. After correcting the size of the design region of the topological structure according to the determination, return to S4 until the topological structure with the smallest design region is obtained;

[0019] S6. Use the phases and amplitudes in different working states, as well as the number of discrete patches in the topological structure, to construct an optimization objective function that combines the number of discrete patches in the topological structure, and optimize the topological structure with the smallest design region. The optimization objective function that combines the number of discrete patches in the topological structure is:

[0020]

[0021] The optimization process is defined as:

[0022]

[0023] where, is the weight coefficient of the miniaturization function in the proportion of the objective function; is the sum of the elements with value "1" in

[0024] S7. Analyze the current distribution of the optimized topological structure with the smallest design region, remove the unconnected discrete patches, and adjust the positional relationship between the patches to obtain the intelligent metasurface unit.

[0025] Furthermore, in the initial model of the intelligent metasurface unit, the period length of the intelligent metasurface unit is calculated according to the following formula:

[0026]

[0027] where, is the speed of light in vacuum; is the maximum optimizable operating frequency.

[0028] Furthermore, the discrete patch is a square patch, and the length of the discrete patch is K / 20 to K / 30.

[0029] Furthermore, in S2, the steps of establishing the reflection coefficient model of the intelligent metasurface unit initial model by using the multi-port network include:

[0030] Determine the spacing between the patches according to the patch length and the period length;

[0031] An external electromagnetic wave port is added to the initial structure of the intelligent metasurface unit, and internal discrete ports are added between the discrete patches. The impedance parameters of the external electromagnetic wave port and the internal discrete ports are extracted using full-wave electromagnetic simulation to obtain a multi-port impedance matrix:

[0032]

[0033] Among them, is the self-impedance between the same internal discrete ports; , are the equivalent impedances between the internal discrete port and the external electromagnetic wave port ; among them, and represent directions; is the self-impedance between the same external electromagnetic wave ports;

[0034] A reflection coefficient model is constructed based on the multi-port impedance matrix.

[0035] Furthermore, the screening of the topological structure using the optimization algorithm includes:

[0036] Evaluating the electromagnetic performance of each topological structure according to the reflection coefficient, phase shift range, and loss value;

[0037] Selecting topological structures with a reflection coefficient less than -3 dB within the operating frequency range, a phase shift range of 0 to 315°, and a loss value of 0.002 to 0.0128.

[0038] Furthermore, the design requirements that the optimization objective function needs to meet are:

[0039] The sum of the differences between the optimized values and the ideal values of each parameter in the optimization objective function does not exceed 30.

[0040] Furthermore, the optimization objective of the optimization objective function combined with the number of discrete patches in the topological structure is:

[0041] The optimization objective function has a function value less than 30, or the number of iterations exceeds the set value.

[0042] On the other hand, the present invention provides an intelligent metasurface topological structure design device, including:

[0043] The first module is used to establish an initial model of the intelligent metasurface unit, including a metal patch layer, a dielectric layer, and a metal bottom plate grounding layer;

[0044] The second module is used to add a plurality of discrete patches on the metal patch layer and establish a reflection coefficient model of the initial model of the intelligent metasurface unit using a multi-port network:

[0045]

[0046] Among them, is the reflection coefficient function obtained by using a multi-port network; and are the internal discrete port vector and the active device parameter vector respectively; is the multi-port network model; is the load impedance of the internal discrete port; is the impedance matrix of the multi-port; is the load impedance of the external electromagnetic wave port;

[0047] The third module is used to traverse the number of rows and columns of the discrete patches, and start from the maximum number of rows and columns to sequentially reduce the number of rows or columns to construct the topological structures of different intelligent metasurface units;

[0048] The fourth module, based on the reflection coefficient function, is used to construct an objective function by using the phases and amplitudes of different working states, and use an optimization algorithm to screen the topological structures to minimize the optimization objective function, and the objective function is:

[0049]

[0050] The optimization process is defined as:

[0051]

[0052] Among them, and are the internal discrete port vector and the active device parameter vector respectively; and are the optimal internal discrete port vector and the optimal active device parameter vector respectively; and are the weight coefficients of the phase constraint and the amplitude constraint in the objective function respectively; is the phase of the th working state; is the ideal phase of the th working state; is a comparison function used to find the minimum value between and ; is the amplitude of the th optimized working state; is the ideal amplitude of the i th working state, takes the value of 3;

[0053] The fifth module is used to determine whether the optimization objective function of each topological structure meets the design requirements. If it meets the requirements, the size of the design area of the topological structure is reduced; if it does not meet the requirements, the size of the design area of the topological structure is enlarged. After correcting the size of the design area of the topological structure according to the judgment, it returns to the fourth module until the topological structure with the smallest design area is obtained;

[0054] The sixth module is used to construct an optimization objective function combined with the number of discrete patches in the topological structure by using the phases and amplitudes in different working states and the number of discrete patches in the topological structure, and optimize the topological structure with the smallest design area. The optimization objective function combined with the number of discrete patches in the topological structure is:

[0055]

[0056] The optimization process is defined as:

[0057]

[0058] Among them, is the weight coefficient of the miniaturization function in the proportion of the objective function; is the sum of the elements with "1" in;

[0059] The seventh module is used to analyze the current distribution of the optimized topological structure with the smallest design area, remove the unconnected discrete patches, and adjust the positional relationship between the patches to obtain the intelligent metasurface unit.

[0060] On the other hand, the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the intelligent metasurface topological structure design method are implemented.

[0061] On the other hand, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the intelligent metasurface topological structure design method are implemented.

[0062] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0063] The intelligent metasurface topology design method provided by the present invention uses a multi-port microwave network to miniaturize the intelligent metasurface unit, shortening the design cycle. Specifically, by adding external electromagnetic wave ports to the intelligent metasurface unit and adding internal discrete ports between discrete patches, a multi-port network model is constructed to obtain the multi-port impedance matrix of the intelligent metasurface unit. Based on the obtained impedance matrix, a reflection coefficient model of the initial model of the intelligent metasurface unit is constructed, and an objective function is constructed and optimized using an optimization algorithm to achieve a preliminary screening of the topology of the intelligent metasurface unit. Then, by optimizing the objective function, the electromagnetic response performance of the screened intelligent metasurface unit is verified, and the size of the design area of the topology is adaptively increased or decreased according to whether the electromagnetic response performance meets the design requirements. The electromagnetic response performance of the intelligent metasurface unit after adaptive modification is verified again until an intelligent metasurface with a topology structure having the smallest design area and meeting the design requirements is obtained. On the basis of the intelligent metasurface with the topology structure of the smallest design area, the number of discrete patches is optimized again, the unconnected discrete patches are removed, and the arrangement spacing of the remaining patches is adjusted to obtain the required intelligent metasurface.

[0064] The intelligent metasurface topology design method provided by the present invention can adaptively miniaturize the intelligent metasurface unit without affecting the working effect, reduce the number of surface patches of the intelligent metasurface unit, and reduce the spatial cost of the design. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on the structures shown in these drawings without creative efforts.

[0066] Figure 1 It is a flowchart of the intelligent metasurface topology design method provided for an embodiment;

[0067] Figure 2 It is an equivalent model diagram of a multi-port microwave network provided for an embodiment;

[0068] Figure 3 It is a schematic diagram of the initial model of the intelligent metasurface unit provided for an embodiment;

[0069] Figure 4 It is a structural diagram of the intelligent metasurface unit with the topology structure of the smallest design area provided for an embodiment;

[0070] Figure 5 It is a structural diagram of the miniaturized intelligent metasurface unit provided for an embodiment;

[0071] Figure 6 Electromagnetic response diagrams of 8 phase states obtained from electromagnetic simulation of an intelligent metasurface with unoptimized minimum design region topology provided for one embodiment;

[0072] Figure 7 Electromagnetic response diagrams of 8 phase states obtained from electromagnetic simulation of an intelligent metasurface with optimized minimum design region topology provided for one embodiment. Specific implementation manners

[0073] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0074] Refer to Figure 1 , and one embodiment provides a method for designing the topology of an intelligent metasurface, including the following steps:

[0075] S1. Establish an initial model of an intelligent metasurface unit, including a metal patch layer, a dielectric layer, and a metal bottom plate grounding layer;

[0076] S2. Add a plurality of discrete patches on the metal patch layer, and use a multi-port network to establish a reflection coefficient model of the initial model of the intelligent metasurface unit;

[0077] Reflection coefficient model of the initial model of the intelligent metasurface unit:

[0078]

[0079] Among them, is the reflection coefficient function obtained by using a multi-port network; and are the internal discrete port vector and the active device parameter vector respectively; is the multi-port network model; is the load impedance of the internal discrete port; is the impedance matrix of the multi-port; is the load impedance of the external electromagnetic wave port;

[0080] S3. Traverse the number of rows and columns of the discrete patches, and start from the maximum number of rows and columns and sequentially reduce the number of rows or columns to construct different topologies of the intelligent metasurface unit;

[0081] S4. Based on the reflection coefficient function, use the phase and amplitude of different working states to construct an objective function, and use an optimization algorithm to screen the topology to minimize the optimization objective function. The objective function is:

[0082]

[0083] The optimization process is defined as:

[0084]

[0085] where, and are the internal discrete port vector and the active device parameter vector, respectively; and are the optimal internal discrete port vector and the optimal active device parameter vector, respectively; and are the weight coefficients of the phase constraint and the amplitude constraint in the objective function, respectively; is the phase of the th operating state; is the ideal phase of the th operating state; is a comparison function used to find the minimum value between and ; is the amplitude of the th operating state after optimization; is the ideal amplitude of the th operating state, takes the value of 3;

[0086] S5. Judge whether the optimization objective function of each topological structure meets the design requirements. If it meets, reduce the size of the design area of the topological structure; if it does not meet, increase the size of the design area of the topological structure; after correcting the size of the design area of the topological structure according to the judgment, return to S4 until the topological structure with the minimum design area is obtained;

[0087] S6. Use the phases and amplitudes of different operating states, as well as the number of discrete patches in the topological structure, to construct an optimization objective function combined with the number of discrete patches in the topological structure, and optimize the topological structure with the minimum design area. The optimization objective function combined with the number of discrete patches in the topological structure is:

[0088]

[0089] The optimization process is defined as:

[0090]

[0091] where, is the weight coefficient of the miniaturization function in the objective function; is the sum of the elements with the value of "1" in;

[0092] S7. Analyze the current distribution of the topological structure of the optimized minimum design region, remove the unconnected discrete patches, and adjust the positional relationship between the patches to obtain the intelligent metasurface unit.

[0093] In the initial model of the intelligent metasurface unit, the period length of the intelligent metasurface unit is calculated according to the following formula:

[0094]

[0095] where is the speed of light in vacuum; is the maximum optimizable operating frequency.

[0096] In one embodiment, referring to Figure 3 , the period length of the initial model of the intelligent metasurface unit is 27.2 mm, the period width is 25.8 mm, the thickness of the dielectric layer is 3.3 mm, and the dielectric constant is 2.65; the operating frequency of this intelligent metasurface is 2.6 GHz; the metal patch layer is composed of 56 rectangular metal patches of the same size arranged at a certain interval and arranged symmetrically along the Y axis.

[0097] The discrete patch is a square patch, and the length of the discrete patch is K / 20 to K / 30. In this embodiment, the length of the discrete patch is 2 mm, and the initial patch interval is 0.3 mm.

[0098] Referring to Figure 2 , the method for establishing the reflection coefficient model of the intelligent metasurface unit using the multi-port network includes the following steps:

[0099] Determine the interval between the patches according to the patch length and the period length;

[0100] Add an external electromagnetic wave port to the initial structure of the intelligent metasurface unit, add internal discrete ports between the discrete patches, and use full-wave electromagnetic simulation to extract the impedance parameters of the external electromagnetic wave port and the internal discrete ports to obtain the multi-port impedance matrix:

[0101]

[0102] where is the self-impedance between the same internal discrete ports; , is the equivalent impedance between the internal discrete port and the external electromagnetic wave port ; where and represent directions; is the self-impedance between the same external electromagnetic wave ports;

[0103] Construct a reflection coefficient model based on the multi-port impedance matrix:

[0104]

[0105] where, is the reflection coefficient function obtained using the multi-port network; and are the internal discrete port vector and the active device parameter vector respectively; is the multi-port network model; is the load impedance of the internal discrete port; is the impedance matrix of the multi-port; is the load impedance of the external electromagnetic wave port, which is the characteristic impedance of the electromagnetic wave in vacuum.

[0106] According to the relationship between the period length of the intelligent metasurface unit and the size of the discrete patch, it is determined that in this intelligent metasurface unit, the number of rows of the discrete patch is 8 and the number of columns is 9.

[0107] Traverse the number of rows and columns of the discrete patches working from more to less, starting from the maximum number of rows and columns and decreasing the number of rows or columns in turn to construct the topological structures of different intelligent metasurface units. During the traversal process, based on the reflection coefficient function, use the phase and amplitude of different working states to construct an objective function, and use an optimization algorithm to screen the topological structures to minimize the optimization objective function. The objective function is:

[0108]

[0109] The optimization process is defined as:

[0110]

[0111] where, and are the internal discrete port vector and the active device parameter vector respectively; and are the optimal internal discrete port vector and the optimal active device parameter vector respectively; and are the weight coefficients of the phase constraint and the amplitude constraint in the objective function respectively; is the phase of the th working state; is the ideal phase of the th working state; is a comparison function used to find the minimum value between and ; is thei The amplitude of a working state; is the i ideal amplitude of the working state, and the value is 3; in this embodiment, 8 phase states of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315° are selected as the ideal phase states.

[0112] Evaluate the electromagnetic performance of each topological structure according to the reflection coefficient, phase shift range, and loss value;

[0113] Select a topological structure with a reflection coefficient less than -3 dB within the working frequency range, a phase shift range of 0 to 315°, and a loss value of 0.002 to 0.0128.

[0114] Judge whether the optimization objective function of each topological structure meets the design requirements. If it meets, reduce the size of the design area of the topological structure. Specifically, continue to reduce the number of rows and columns of the discrete patches in operation; if it does not meet, expand the size of the design area of the topological structure and increase the number of rows and columns of the discrete patches in operation; after correcting the size of the design area of the topological structure according to the judgment, return to calculate the optimization objective function of the topological structure, record the size of the design area of each topological structure that meets the design requirements, and select the topological structure with the smallest design area, referring to Figure 4 ;

[0115] Construct an optimization objective function combined with the number of discrete patches in the topological structure by using the phases, amplitudes of different working states, and the number of discrete patches in the topological structure, and optimize the topological structure with the smallest design area. The optimization objective function combined with the number of discrete patches in the topological structure is:

[0116]

[0117] The optimization process is defined as:

[0118]

[0119] Among them, is the weight coefficient of the miniaturization function in the proportion of the objective function; is the sum of the elements with "1" in

[0120] By adding the number of discrete patches to the performance optimization of the intelligent metasurface unit, calculate the number of open states in the internal discrete port vector and accumulate it. By accumulating the values of the port vector, the number of "1" in the vector increases, that is, the number of port states of "open" increases, so as to obtain more discrete patches that are not connected to the surrounding, delete them, and increase the degree of miniaturization.

[0121] The optimization objective of the optimization objective function for the number of discrete patches in the combined topological structure is as follows:

[0122] Optimization objective function The function value is less than 30, or the number of iterations exceeds the set value.

[0123] Refer to Figure 5 , analyze the current distribution of the topological structure of the minimum design area after optimization, remove the unconnected discrete patches, adjust the positional relationship between the patches, and obtain the intelligent metasurface unit.

[0124] Refer to Figure 6 , which is the electromagnetic response diagram of 8 phase states obtained from the electromagnetic simulation of the intelligent metasurface with the unoptimized topological structure of the minimum design area. Refer to Figure 7 , which is the electromagnetic response diagram of 8 phase states obtained from the electromagnetic simulation of the intelligent metasurface with the optimized topological structure of the minimum design area. It can be seen from Figure 6 that due to the coupling effect between the discrete patches, there is a frequency offset in the simulation results compared with the case where the discrete patches are not deleted. To reduce the influence of the frequency offset, adjust the horizontal and vertical distances between the discrete patches, reduce the electromagnetic coupling between the patches, and ensure the performance of the intelligent metasurface unit, as Figure 7 shown.

[0125] In one embodiment, an intelligent metasurface topological structure design device is provided, including:

[0126] The first module is used to establish an initial model of the intelligent metasurface unit, including a metal patch layer, a dielectric layer, and a metal bottom plate grounding layer;

[0127] The second module is used to add a plurality of discrete patches on the metal patch layer and establish a reflection coefficient model of the initial model of the intelligent metasurface unit by using a multi-port network:

[0128]

[0129] Among them, is the reflection coefficient function obtained by using the multi-port network; and are the internal discrete port vector and the active device parameter vector respectively; is the multi-port network model; is the load impedance of the internal discrete port; is the impedance matrix of the multi-port; is the load impedance of the external electromagnetic wave port;

[0130] The third module is used to traverse the number of rows and columns of the discrete patches, start from the maximum number of rows and columns and gradually reduce the number of rows or columns, and construct the topological structures of different intelligent metasurface units;

[0131] The fourth module, based on the reflection coefficient function, is used to construct an objective function by using the phases and amplitudes of different working states, and to screen the topological structures by using an optimization algorithm to minimize the optimized objective function. The objective function is:

[0132]

[0133] The optimization process is defined as:

[0134]

[0135] Wherein, and are the internal discrete port vector and the active device parameter vector respectively; and are the optimal internal discrete port vector and the optimal active device parameter vector respectively; and are the weight coefficients of the phase constraint and the amplitude constraint in the objective function respectively; is the phase of the th working state; is the ideal phase of the th working state; is a comparison function used to find the minimum value between and ; is the amplitude of the th optimized working state; is the ideal amplitude of the th working state, and takes the value of 3;

[0136] The fifth module is used to judge whether the optimized objective function of each topological structure meets the design requirements. If it meets, the size of the design area of the topological structure is reduced; if it does not meet, the size of the design area of the topological structure is enlarged; after correcting the size of the design area of the topological structure according to the judgment, it returns to the fourth module until the topological structure with the minimum design area is obtained;

[0137] The sixth module is used to construct an optimized objective function combined with the number of discrete patches in the topological structure by using the phases, amplitudes of different working states, and the number of discrete patches in the topological structure, and to optimize the topological structure with the minimum design area. The optimized objective function combined with the number of discrete patches in the topological structure is:

[0138]

[0139] The optimization process is defined as:

[0140]

[0141] Wherein, is the weight coefficient of the miniaturization function in the proportion of the objective function; is the sum of the elements with "1" in

[0142] The seventh module is used to analyze the current distribution of the topological structure of the optimized minimum design region, remove the unconnected discrete patches, adjust the positional relationship between the patches, and obtain the intelligent metasurface unit.

[0143] On the other hand, the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the intelligent metasurface topological structure design method provided in any one of the above embodiments. This computer device can be a server. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, 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 the computer program in the non-volatile storage medium. The database of the computer device is used to store sample data. The network interface of the computer device is used to communicate with an external terminal through a network connection.

[0144] On the other hand, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the intelligent metasurface topological structure design method provided in any one of the above embodiments.

[0145] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0146] Matters not described in the present invention are well-known technologies.

[0147] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0148] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

[0149] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for designing the topology of an intelligent metasurface, characterized in that, It includes the following steps: S1. Establish an initial model of the intelligent metasurface unit, including a metal patch layer, a dielectric layer, and a metal bottom plate grounding layer; S2. Add multiple discrete patches on the metal patch layer, and use a multi-port network to establish a reflection coefficient model of the initial model of the intelligent metasurface unit: Among them, is the reflection coefficient function obtained by using a multi-port network; and are the internal discrete port vector and the active device parameter vector respectively; is the multi-port network model; is the load impedance of the internal discrete port; is the impedance matrix of the multi-port; is the load impedance of the external electromagnetic wave port; S3. Traverse the number of rows and columns of the discrete patches, start from the maximum number of rows and columns, and sequentially reduce the number of rows or columns to construct different topological structures of the intelligent metasurface unit; S4. Based on the reflection coefficient function, construct an objective function using the phase and amplitude in different operating states, and use an optimization algorithm to screen the topological structures to minimize the optimization objective function. The objective function is: ； The optimization process is defined as: ； Among them, and are the internal discrete port vector and the active device parameter vector respectively; and are the optimal internal discrete port vector and the optimal active device parameter vector respectively; and are the weight coefficients of the phase constraint and the amplitude constraint in the objective function respectively; is the phase of the i th operating state; is the ideal phase of the i th operating state; is a comparison function used to find the minimum value between · and ; is the amplitude of the i th optimized operating state; is the ideal amplitude of the i th operating state, takes the value of 3; S5. Judge whether the optimization objective function of each topological structure meets the design requirements. If it meets, reduce the size of the design area of the topological structure; if it does not meet, increase the size of the design area of the topological structure; after correcting the size of the design area of the topological structure according to the judgment, return to S4 until the topological structure with the minimum design area is obtained; S6. Use the phase, amplitude in different operating states, and the number of discrete patches in the topological structure to construct an optimization objective function combined with the number of discrete patches in the topological structure, and optimize the topological structure with the minimum design area. The optimization objective function combined with the number of discrete patches in the topological structure is: ； The optimization process is defined as: ； Among them, is the weight coefficient of the proportion of the miniaturization function in the objective function; is the sum of the elements with "1" in; S7. Analyze the current distribution of the topological structure with the minimum design area after optimization, remove the unconnected discrete patches, and adjust the positional relationship between the patches to obtain the intelligent metasurface unit.

2. The intelligent metasurface topology design method according to claim 1, wherein In the initial model of the intelligent metasurface unit, the period length of the intelligent metasurface unit is calculated according to the following formula: ； wherein, is the speed of light in vacuum; is the maximum optimizable operating frequency.

3. The intelligent metasurface topology design method according to claim 1, wherein The discrete patch is a square patch, and the length of the discrete patch is K between / 20 and K / 30, which is the period length of the intelligent metasurface unit.

4. The intelligent metasurface topology design method according to claim 1, wherein, In S2, the steps of establishing a reflection coefficient model of the initial model of the intelligent metasurface unit using a multi-port network include: Determine the spacing between patches according to the patch length and the period length; Add an external electromagnetic wave port to the initial structure of the intelligent metasurface unit, add internal discrete ports between the discrete patches, and use full-wave electromagnetic simulation to extract the impedance parameters of the external electromagnetic wave port and the internal discrete ports to obtain a multi-port impedance matrix; ； Among them, is the self-impedance between the same internal discrete ports; , is the internal discrete port and the equivalent impedance between the external electromagnetic wave port , where and represent directions; is the self-impedance between the same external electromagnetic wave ports; Construct a reflection coefficient model based on the multi-port impedance matrix.

5. The intelligent metasurface topology design method according to claim 1, wherein The screening of the topological structures using the optimization algorithm includes: Evaluate the electromagnetic performance of each topological structure according to the reflection coefficient, phase shift range, and loss value; Select topological structures with a reflection coefficient less than -3 dB within the operating frequency range, a phase shift range of 0 to 315°, and a loss value of 0.002 to 0.0128.

6. The intelligent metasurface topology design method according to claim 1, wherein The design requirements that the optimization objective function needs to meet are: The sum of the differences between the optimized values and the ideal values of each parameter in the optimization objective function does not exceed 30.

7. The intelligent metasurface topology design method according to claim 1, wherein The optimization objective of the optimization objective function combined with the number of discrete patches in the topological structure is: Optimized objective function The function value is less than 30, or the number of iterations exceeds the set value.

8. Intelligent metasurface topology design device, characterized in that, It includes: The first module is used to establish an initial model of the intelligent metasurface unit, including a metal patch layer, a dielectric layer, and a metal bottom plate grounding layer; The second module is used to add multiple discrete patches on the metal patch layer and establish a reflection coefficient model of the initial model of the intelligent metasurface unit using a multi-port network: ； Among them, is the reflection coefficient function obtained by using a multi-port network; and are the internal discrete port vector and the active device parameter vector respectively; is the multi-port network model; is the load impedance of the internal discrete port; is the impedance matrix of the multi-port; is the load impedance of the external electromagnetic wave port; The third module is used to traverse the number of rows and columns of the discrete patches, start from the maximum number of rows and columns, and sequentially reduce the number of rows or columns to construct different topological structures of the intelligent metasurface unit; The fourth module, based on the reflection coefficient function, is used to construct an objective function by using the phase and amplitude of different working states, and adopts an optimization algorithm to screen the topological structure and minimize the optimization objective function. The objective function is as follows: ； The optimization process is defined as: ； Among them, and are the internal discrete port vector and the active device parameter vector respectively; and are the optimal internal discrete port vector and the optimal active device parameter vector respectively; and are the weight coefficients of the phase constraint and the amplitude constraint in the objective function respectively; is the phase of the i th working state; is the ideal phase of the i th working state; is a comparison function used to find the minimum value between · and ; is the amplitude of the i th optimized working state; is the ideal amplitude of the i th working state, takes the value of 3; The fifth module is used to judge whether the optimization objective function of each topological structure meets the design requirements. If it meets the requirements, the size of the design area of the topological structure is reduced; if it does not meet the requirements, the size of the design area of the topological structure is enlarged; after correcting the size of the design area of the topological structure according to the judgment, it returns to the fourth module until the topological structure with the smallest design area is obtained; The sixth module is used to construct an optimization objective function combined with the number of discrete patches in the topological structure by using the phase, amplitude of different working states, and the number of discrete patches in the topological structure, and optimize the topological structure with the smallest design area. The optimization objective function combined with the number of discrete patches in the topological structure is as follows: ； The optimization process is defined as: ； Among them, is the weight coefficient of the proportion of the miniaturization function in the objective function; is the sum of the elements with "1" in; The seventh module is used to analyze the current distribution of the optimized topological structure with the smallest design area, remove the unconnected discrete patches, and adjust the positional relationship between the patches to obtain the intelligent metasurface unit.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent metasurface topological structure design method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed by the processor, it implements the steps of the intelligent metasurface topological structure design method according to any one of claims 1-7.

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