A method for reducing insertion loss in surface acoustic wave filters

Abstract

This invention discloses a method for reducing the insertion loss of a surface acoustic wave (SAW) filter, relating to the field of filter processing technology. The method includes: collecting piezoelectric materials for the SAW filter, measuring material parameters, and calculating the material's inherent loss; forming an initial electrode layout scheme for the SAW filter based on the piezoelectric material parameters using a topology optimization algorithm; calculating the inter-electrode coupling coefficient based on the initial electrode layout structure, and calculating and reducing the filter's insertion loss based on the inter-electrode coupling coefficient; importing the electrode parameters into coupling simulation software to simulate the propagation process of SAW waves in the filter under multiphysics, optimizing the filter performance; manufacturing the optimized SAW filter, calculating the theoretical manufacturing process loss, and adjusting and optimizing the manufacturing process based on the theoretical and actual losses. This invention reduces the insertion loss of the SAW filter by selecting piezoelectric materials with low inherent loss and continuously adjusting the electrode layout structure.

Description

Technical Field

[0001] This invention relates to the field of filter processing technology, and in particular to a method for reducing insertion loss in surface acoustic wave (SAW) filters. Background Technology

[0002] With the widespread application of 5G technology and the exploration of future communication technologies such as 6G, the performance requirements for filters in communication systems are becoming increasingly stringent. 5G networks utilize higher frequency bands, resulting in greater signal attenuation during transmission. This necessitates filters with lower insertion loss to ensure that the filtered signal retains sufficient strength for subsequent processing and transmission, thereby improving the overall system performance and coverage. In future communication systems, data transmission rates and efficient utilization of spectrum resources are crucial. Low-insertion-loss surface acoustic wave (SAW) filters contribute to achieving higher data transmission efficiency and better spectrum management. Based on this, this invention proposes a method for reducing the insertion loss of SAW filters. Summary of the Invention

[0003] This invention provides a method for reducing the insertion loss of a surface acoustic wave (SAW) filter, comprising:

[0004] S10. Collect piezoelectric materials for surface acoustic wave filters, measure material parameters, and calculate the material loss itself;

[0005] S20. Based on the piezoelectric material parameters, form the initial electrode layout scheme of the surface acoustic wave filter using a topology optimization algorithm;

[0006] S30. Based on the initial electrode layout structure, calculate the coupling coefficient between electrodes, and calculate and reduce the insertion loss of the surface acoustic wave filter based on the coupling coefficient between electrodes.

[0007] S40. Import the electrode parameters into the coupling simulation software to simulate the propagation process of surface acoustic waves in the filter under the action of multiphysics fields and optimize the filter performance.

[0008] S50. Manufacture the optimized surface acoustic wave filter, calculate the theoretical loss, and adjust and optimize the manufacturing process based on the theoretical loss and the actual loss.

[0009] The method for reducing insertion loss of a surface acoustic wave filter as described above involves calculating the energy loss of a piezoelectric material, selecting a piezoelectric material with an energy loss lower than a preset value as the filter substrate, and the energy loss of the piezoelectric material includes dielectric loss, mechanical loss, thermal loss, scattering loss, and substrate coupling loss.

[0010] The above-described method for reducing the insertion loss of a surface acoustic wave (SAW) filter involves determining the electrode layout region in the SAW filter based on a topology optimization algorithm, discretizing it into a finite number of units, and setting initial material properties for each unit. It is assumed that the entire motor layout region is initially filled with a virtual isotropic material with known elastic modulus material parameters, and the material density of each unit is used as a design variable.

[0011] The above-described method for reducing the insertion loss of a surface acoustic wave (SAW) filter uses minimizing the propagation energy loss of SAW as the objective function. Through iterative calculations, the presence or absence of materials in each unit is continuously adjusted to determine the distribution range and density of the interdigitated electrodes, i.e., the optimal material distribution.

[0012] The method described above for reducing the insertion loss of a surface acoustic wave filter involves adjusting the electrode parameters to minimize the theoretical value of the insertion loss, and using a numerical optimization algorithm to search for the optimal combination of electrode parameters within the parameter range to achieve fine control of the insertion loss.

[0013] The method for reducing insertion loss of a surface acoustic wave (SAW) filter, as described above, involves importing determined electrode parameters into a multiphysics coupling simulation software. The software simulates the piezoelectric effect, elasticity, and the interaction of multiple physical fields such as electricity. During the simulation, the filter geometry, material properties, and boundary conditions are set to simulate the propagation process of SAW waves in the filter. Based on the simulation results, the parameters are fine-tuned to optimize the filter performance.

[0014] The method for reducing insertion loss of a surface acoustic wave filter as described above includes theoretical losses such as material losses, structural losses, radiation losses, and manufacturing process losses.

[0015] The present invention also provides a system for reducing the insertion loss of a surface acoustic wave filter, comprising:

[0016] Data collection and measurement module: collects data on the piezoelectric material used in the surface acoustic wave filter, measures the material parameters, and calculates the material's own losses;

[0017] Initial electrode layout module: Based on the piezoelectric material parameters and a topology optimization algorithm, an initial electrode layout scheme for the surface acoustic wave filter is generated;

[0018] Insertion loss module: Based on the initial electrode layout structure, calculate the coupling coefficient between electrodes, and calculate and reduce the insertion loss of the surface acoustic wave filter based on the coupling coefficient between electrodes;

[0019] Coupled simulation optimization module: Imports electrode parameters into coupled simulation software to simulate the propagation process of surface acoustic waves in the filter under the action of multiphysics fields, and optimizes the filter performance;

[0020] Manufacturing process optimization module: manufactures optimized surface acoustic wave filters, calculates losses during the theoretical manufacturing process, and adjusts and optimizes the manufacturing process based on theoretical and actual losses.

[0021] The present invention also provides a computer storage medium, comprising: at least one memory and at least one processor;

[0022] The memory is used to store one or more program instructions;

[0023] A processor for running one or more program instructions to perform a method for reducing insertion loss of a surface acoustic wave filter as described in any of the preceding claims.

[0024] The beneficial effects achieved by this invention are as follows: This invention reduces the insertion loss of the surface acoustic wave filter by selecting piezoelectric materials with low inherent loss and continuously adjusting the layout structure of the electrodes. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0026] Figure 1 This is a flowchart of a method for reducing insertion loss of a surface acoustic wave filter provided in Embodiment 1 of this application.

[0027] Figure 2 This is a schematic diagram of a system for reducing the insertion loss of a surface acoustic wave filter provided in Embodiment 2 of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1

[0030] like Figure 1 As shown, Embodiment 1 of this application provides a method for reducing the insertion loss of a surface acoustic wave filter, comprising:

[0031] S10. Collect the piezoelectric material used in the surface acoustic wave filter, measure the material parameters, and calculate the material loss itself.

[0032] Material properties have a profound impact on the performance of surface acoustic wave (SAW) filters. A wide range of piezoelectric materials commonly used in SAW filters were collected, including quartz, lithium niobate, lithium tantalate, barium titanate, and lead zirconate titanate, as well as some novel piezoelectric composite materials, including polymer-based piezoelectric composites, nanostructured piezoelectric materials, magnetoelectric composites, and piezoelectric ceramic composites. For each material, its elastic constants were determined using high-precision measuring equipment and advanced testing techniques. Dielectric constant and piezoelectric constant Elastic constant The elastic response of a material is determined by the ultrasonic pulse-echo method; dielectric constant. The piezoelectric constant reflects the electrical properties of a material and is measured at a specific frequency using an impedance analyzer. The mechanical and electrical energy conversion capabilities of materials are assessed using a quasi-static method.

[0033] The dielectric loss, mechanical loss, and thermal loss of piezoelectric materials themselves lead to energy loss, as shown in the specific formula. Where W represents the total energy loss of the piezoelectric material within a certain time t. This refers to dielectric loss caused by domain reorientation under the influence of an electric field. It is the angular frequency of the electric field. It is the vacuum permittivity. It is the relative permittivity of the material. It is the dielectric loss tangent, and E is the electric field strength. This refers to the mechanical losses caused by internal friction and other factors due to mechanical vibrations within the material during surface acoustic wave propagation. Indicates stress, Indicates strain rate. This represents the tangent of the mechanical loss angle. This indicates heat loss including heat conduction, heat exchange with the outside environment, and heat loss due to the thermoelastic effect. It is thermal conductivity. It is a temperature gradient. is the thermoelastic coefficient, and u is the displacement amplitude of the surface acoustic wave. The heat loss caused by the thermoelastic effect is proportional to the square of the frequency of the surface acoustic wave and the square of the displacement amplitude. This refers to the scattering loss caused by surface acoustic waves encountering material surface roughness, defects, etc. It is the surface adsorption coefficient. It is the wave number of surface acoustic waves. is the root mean square of the surface roughness, and I is the intensity of the surface acoustic wave. This represents the substrate coupling loss caused by the coupling of surface acoustic wave energy into the substrate material. H is the elastic coefficient of the substrate material, and H is the coupling coefficient.

[0034] The energy loss of piezoelectric materials is calculated, and materials with energy losses lower than a preset value are selected as the filter substrate. The energy loss of piezoelectric materials includes dielectric loss, mechanical loss, thermal loss, scattering loss, and substrate coupling loss. This material selection method breaks through the limitations of traditional methods that rely on experience or limited material comparisons, starting from the essential properties of the material, and laying a solid foundation for reducing insertion loss.

[0035] S20. Based on the piezoelectric material parameters, a topology optimization algorithm is used to form the initial electrode layout scheme for the surface acoustic wave filter.

[0036] Topology optimization algorithms can find the optimal material distribution of a surface acoustic wave (SAW) filter under given design region and conditions, thereby optimizing its structural performance. Based on this algorithm, the possible electrode layout regions in the SAW filter are determined and discretized into a finite number of elements. For each element, initial material properties are set. It is assumed that the entire design region is initially filled with a virtual isotropic material, whose elastic modulus and other material parameters are known values. The material density of each element is used as a design variable, denoted as... n is the total number of elements. Initially, the material density of all elements can be set to the same value.

[0037] Taking the minimization of surface acoustic wave propagation energy loss as the objective function, and based on the material density of element i within the design region as follows: The objective function F can be expressed as , ,in, Indicates that unit i has a material density of Energy loss during surface acoustic wave propagation This represents the volume of unit i. For stress tensor, The elastic stiffness coefficient tensor describes the linear relationship between stress and strain during the elastic deformation of a material. i, j, k, and l are indices of the tensor, each of which is related to a three-dimensional spatial direction and can take values ​​of 1, 2, and 3, corresponding to the three directions of the Cartesian coordinate system. The stress variable describes the degree of deformation at various points within the material. k and l are indices that can each take values ​​of 1 and 2. For strain tensor, The polarization intensity, The vacuum permittivity, Where E is the electric polarizability and E is the electric field strength. Let be the voltage constant tensor, and i, j, k be indices taking values ​​of 1, 2, and 3, respectively. These indices represent the strain produced when a material is subjected to stress in a certain direction. This determines the magnitude of the resulting polarization intensity components in each direction. These are components of the strain tensor, which are the strain tensor components. A portion of it, specifically referring to the components with indices j and k. This indicates a constraint condition, representing a limit on the total mass of the electrodes. This is the upper limit for the total mass of the electrodes.

[0038] A material interpolation model is used to describe the relationship between element material properties and design variables. The interpolation formula is: , The elastic modulus of element i It is the elastic modulus of the solid material, and p is the penalty factor, with a value ranging from 3 to 5.

[0039] The sensitivity of the objective function to each design variable is calculated using the following formula: , where F is the objective function and K is the stiffness matrix, which is related to material properties and the geometry of the structure, and is used to establish the relationship between force and displacement. This is a displacement field that describes the displacement of each node of the structure under the action of forces. For design variables, representing material density, As an adjoint variable, it is an auxiliary variable introduced to solve for sensitivity, and its dimension is the same as that of the displacement field. Similarly, T represents the matrix transpose operation.

[0040] Based on the sensitivity information, the design variables are updated using an appropriate optimization algorithm. Where r represents the number of iterations, It is the step size parameter. and These are design variables The upper and lower bounds in the r-th iteration need to be adjusted based on the specific problem and the convergence during the iteration process. After each iteration, the design variables will be... The range is restricted to [0,1] to ensure the physical meaning of the material density. Simultaneously, the convergence criterion is checked; if the change in the objective function is less than a certain threshold... ,Right now If the maximum number of iterations is reached, the iteration stops and the optimal material distribution is obtained; otherwise, the next iteration continues.

[0041] Through iterative calculations, the presence or absence of materials in each element is continuously adjusted to gradually generate the initial interdigitated electrode layout structure. Each iteration updates the element state using sensitivity analysis based on the objective function and constraints. After multiple iterations, the approximate distribution range and density of the interdigitated electrodes are determined, forming the initial electrode layout scheme.

[0042] The obtained optimal material distribution is post-processed, and the material density distribution is displayed graphically using visualization tools to intuitively observe the electrode layout. The results are then smoothed or thresholded to convert the material density into a defined electrode layout scheme, classifying cells with a material density greater than a certain threshold as electrode regions and cells with a density less than the threshold as non-electrode regions.

[0043] S30. Based on the initial electrode layout structure, calculate the coupling coefficient between electrodes, and calculate and reduce the insertion loss of the surface acoustic wave filter based on the coupling coefficient between electrodes.

[0044] Based on the electrode layout structure generated in step S20, detailed calculations are performed on the specific parameters of the electrodes. The interdigitated electrode length L and the surface acoustic wave wavelength are calculated. proportional relationship ,in v is the propagation speed of surface acoustic waves in the selected material, which can be obtained from the relationship between the material's elastic constant and density. It is concluded that Let be the elastic constant of the material. This represents the material density.

[0045] Calculate the coupling coefficient between electrodes The formula is , The change in frequency The center frequency under the initial electrode structure. To adjust the center frequency after adjusting the electrode structure.

[0046] Based on the calculated proportional relationship and inter-electrode coupling coefficient Calculate the insertion loss and adjust parameters such as electrode width w and spacing s. The insertion loss formula is: ,in, The characteristic impedance of the input / output port. Equivalent resistance Angular frequency, Equivalent resistance The relationship between the electrode width w and the spacing s is as follows , As a reference resistor, For reference width, Here, m and n are empirical exponents, serving as the reference spacing. By adjusting the electrode parameters to minimize the theoretical insertion loss, a numerical optimization algorithm is used to search for the optimal combination of electrode parameters within a certain parameter range, achieving fine-grained control of the insertion loss.

[0047] S40. Import the electrode parameters into the coupling simulation software to simulate the propagation process of surface acoustic waves in the filter under the action of multiphysics fields and optimize the filter performance.

[0048] The electrode parameters determined in step S30 are imported into a multiphysics coupling simulation software to simulate the interactions of piezoelectric effect, elasticity, and electrical fields. In the simulation, the filter geometry, material properties, and boundary conditions are set to simulate the propagation process of surface acoustic waves within the filter. The constitutive equation for the piezoelectric effect is... , ,in, For stress tensor, Let E be the elastic stiffness coefficient under a constant electric field, and E be the constant electric field. For strain tensor, It is a voltage constant tensor. For electric field strength, It is the electric displacement vector. For the strain tensor components, The dielectric constant under constant strain is For constant strain conditions, Let be the component of the electric field intensity vector E in the j-direction. The elastic equilibrium equation is: , , Indicates to Calculate the partial derivative with respect to the coordinate direction of index j and perform a contraction operation. For volume force density, Representing spatial coordinate variables, j takes values ​​of 1, 2, and 3, corresponding to the x, y, and z directions respectively. In electrical applications, the formula is... , This represents curl operation, where E represents the electric field intensity vector. This represents the partial derivative of the magnetic flux density B with respect to time t.

[0049] The above equations, along with the corresponding boundary and initial conditions, are numerically solved in the software to obtain the distribution of physical quantities such as stress, strain, electric field, and electric displacement during the propagation of surface acoustic waves. The software then simulates the propagation process of surface acoustic waves in the filter under the interaction of physical fields.

[0050] The simulation results were used to analyze the energy distribution and propagation characteristics of surface acoustic waves (SAWs). An energy density cloud map and SAW propagation trajectory map were generated using a post-processing module to visually present the energy distribution and propagation. If the simulation revealed significant energy loss in a certain area, such as concentrated energy loss at the electrode edge, the electrode shape or position in that area was adjusted to facilitate smoother SAW propagation. Through multiple simulations and parameter fine-tuning, the filter performance was optimized.

[0051] S50. Manufacture the optimized surface acoustic wave filter, calculate the loss in the theoretical manufacturing process, and adjust and optimize the manufacturing process based on the theoretical loss and the actual loss.

[0052] Based on the optimized parameters from step S40, a specialized manufacturing process is designed. For the selected piezoelectric material, process parameters such as photolithography and etching are optimized. In the photolithography process, suitable photoresist and exposure light source are selected, and exposure time and dosage are precisely controlled to ensure the accuracy of electrode pattern transfer. The formula for calculating the photoresist exposure dosage D is as follows: , Let t be the intensity of the exposure light source and t be the exposure time. Different photoresists have their specific photosensitivity curves. The optimal exposure dose for different photoresists under a specific exposure light source is determined experimentally. After multiple experiments, the optimal exposure time was determined to be... This allows us to determine the intensity of the exposure light source.

[0053] In etching processes, appropriate etching solutions and etching methods are selected based on material properties to control the etching rate and uniformity. The etching rate formula is: ,in, Here are constants related to the material and the etching solution, where a is an empirical index of the etching solution concentration C, b is an empirical index of the etching temperature G, and p is the etching time. The empirical index. The etching depth h and etching rate were measured experimentally under different etchant concentrations, temperatures, and times. By adjusting the etching solution concentration C, etching temperature G, and etching time This stabilizes the etching rate at the target value.

[0054] The theoretical loss in the actual manufacturing process is , Material loss during the manufacturing process. As a weight for material loss, d is the attenuation coefficient of the material, d is the propagation distance of the surface acoustic wave, and J is the quality factor of the material. Structural losses during the manufacturing process. As the weight of structural loss, This refers to the shape factor of the electrode, including the influence of the electrode's aspect ratio, interdigitated shape, etc., on losses. The coupling coefficient between the electrode and the piezoelectric material affects the energy exchange efficiency between the surface acoustic wave and the electrode. The frequency of the surface acoustic wave is denoted as 'V'. The higher the frequency, the greater the structure-related losses may be. This is due to radiation loss during the manufacturing process. Here, represents the weight for radiation loss, and N is the geometry of the filter. The wavelength of the surface acoustic wave is given. Let z be the wave impedance of the piezoelectric material, and z be the wave impedance of the surrounding medium. Due to manufacturing process losses, Weighting for manufacturing process losses. As a baseline factor, adjust the lithography precision factor. Etching uniformity factor Material surface roughness factor The relationship between them is as follows: the lower the photolithography precision, the more uneven the etching, and the rougher the material surface.

[0055] After the sample is manufactured, its insertion loss is measured using testing equipment such as a high-precision network analyzer. Compare the measurement results with the simulation calculation results. Comparative analysis revealed significant deviations, which were then analyzed to identify the causes. These deviations included insufficient precision in the photolithography etching process leading to electrode size variations, or discrepancies between the actual material properties and the simulated settings. To address these causes, the manufacturing process parameters and electrode design parameters were readjusted, and the manufacturing and testing processes were repeated. After multiple adjustments to the manufacturing process and sample testing, the insertion loss of the final sample was controlled within the set deviation threshold, meeting the design requirements and ensuring that the designed filter achieves low insertion loss in actual manufacturing.

[0056] Example 2

[0057] like Figure 2 As shown, Embodiment 2 of this application provides a system for reducing the insertion loss of a surface acoustic wave filter, comprising:

[0058] Data collection and measurement module: Collects data on the piezoelectric material used in the surface acoustic wave filter, measures the material parameters, and calculates the material's own losses.

[0059] Initial electrode layout module: Based on the piezoelectric material parameters, a topology optimization algorithm is used to form the initial electrode layout scheme of the surface acoustic wave filter.

[0060] Insertion loss module: Based on the initial electrode layout structure, calculate the coupling coefficient between electrodes, and calculate and reduce the insertion loss of the surface acoustic wave filter based on the coupling coefficient between electrodes.

[0061] Coupled simulation optimization module: Imports electrode parameters into coupled simulation software to simulate the propagation process of surface acoustic waves in the filter under the action of multiphysics fields, thereby optimizing the filter performance.

[0062] Manufacturing process optimization module: manufactures optimized surface acoustic wave filters, calculates losses during the theoretical manufacturing process, and adjusts and optimizes the manufacturing process based on theoretical and actual losses.

[0063] Corresponding to the above embodiments, the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0064] The memory is used to store one or more program instructions;

[0065] A processor for running one or more program instructions to implement a method for reducing insertion loss in a surface acoustic wave filter.

[0066] Corresponding to the above embodiments, this embodiment of the invention provides a computer-readable storage medium containing one or more program instructions, which are executed by a processor to provide a method for reducing insertion loss of a surface acoustic wave filter.

[0067] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions that, when executed on a computer, cause the computer to perform the aforementioned method for reducing insertion loss of a surface acoustic wave filter.

[0068] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0069] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0070] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0071] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0072] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0073] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0074] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0075] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for reducing insertion loss in a surface acoustic wave (SAW) filter, characterized in that, include: S10. Collect piezoelectric materials for surface acoustic wave filters, measure material parameters, and calculate the material loss itself; Specifically, the energy loss of the piezoelectric material is calculated, and a piezoelectric material with an energy loss lower than a preset value is selected as the filter substrate. The energy loss of the piezoelectric material includes dielectric loss, mechanical loss, thermal loss, scattering loss, and substrate coupling loss. S20. Based on the piezoelectric material parameters, form the initial electrode layout scheme of the surface acoustic wave filter using a topology optimization algorithm. Specifically, based on the topology optimization algorithm, determine the electrode layout region in the surface acoustic wave filter, discretize it into a finite number of units, and set the initial material properties for each unit. Assume that in the initial state, the entire electrode layout region is filled with a virtual isotropic material, whose elastic modulus material parameters are known values, and use the material density of each unit as a design variable. S30. Based on the initial electrode layout structure, calculate the coupling coefficient between electrodes, and calculate and reduce the insertion loss of the surface acoustic wave filter based on the coupling coefficient between electrodes. S40. Import the electrode parameters into the coupling simulation software to simulate the propagation process of surface acoustic waves in the filter under the action of multiphysics fields and optimize the filter performance. S50. Manufacture the optimized surface acoustic wave filter, calculate the theoretical loss, and adjust and optimize the manufacturing process based on the theoretical loss and the actual loss.

2. The method for reducing insertion loss of a surface acoustic wave filter as described in claim 1, characterized in that, With minimizing the energy loss of surface acoustic wave propagation as the objective function, the existence of each unit material is continuously adjusted through iterative calculations to determine the distribution range and density of the interdigitated electrodes, i.e., the optimal material distribution.

3. The method for reducing insertion loss of a surface acoustic wave filter as described in claim 2, characterized in that, By adjusting the electrode parameters to minimize the theoretical value of insertion loss, and using a numerical optimization algorithm to search for the optimal combination of electrode parameters within the parameter range, fine-grained control of insertion loss can be achieved.

4. The method for reducing insertion loss of a surface acoustic wave filter as described in claim 3, characterized in that, The determined electrode parameters are imported into a multiphysics coupling simulation software to simulate the piezoelectric effect, elasticity, and the interaction of multiple physical fields such as electricity. In the simulation, the filter geometry, material properties, and boundary conditions are set to simulate the propagation process of surface acoustic waves in the filter. Based on the simulation results, the parameters are fine-tuned to optimize the filter performance.

5. A system for reducing insertion loss in a surface acoustic wave filter, characterized in that, include: Data collection and measurement module: collects data on the piezoelectric material used in the surface acoustic wave filter, measures the material parameters, and calculates the material's own losses; Specifically, the energy loss of the piezoelectric material is calculated, and a piezoelectric material with an energy loss lower than a preset value is selected as the filter substrate. The energy loss of the piezoelectric material includes dielectric loss, mechanical loss, thermal loss, scattering loss, and substrate coupling loss. Initial electrode layout module: Based on the piezoelectric material parameters, an initial electrode layout scheme for the surface acoustic wave filter is formed using a topology optimization algorithm. Specifically, based on the topology optimization algorithm, the electrode layout region in the surface acoustic wave filter is determined and discretized into a finite number of units. For each unit, initial material properties are set. It is assumed that the entire electrode layout region is filled with a virtual isotropic material in the initial state, and its elastic modulus material parameters are known values. The material density of each unit is used as a design variable. Insertion loss module: Based on the initial electrode layout structure, calculate the coupling coefficient between electrodes, and calculate and reduce the insertion loss of the surface acoustic wave filter based on the coupling coefficient between electrodes; Coupled simulation optimization module: Imports electrode parameters into coupled simulation software to simulate the propagation process of surface acoustic waves in the filter under the action of multiphysics fields, and optimizes the filter performance; Manufacturing process optimization module: manufactures the optimized surface acoustic wave filter, calculates the theoretical loss, and adjusts and optimizes the manufacturing process based on the theoretical loss and the actual loss.

6. A computer storage medium, characterized in that, include: At least one memory and at least one processor; The memory is used to store one or more program instructions; A processor for running one or more program instructions to perform a method for reducing insertion loss of a surface acoustic wave filter as described in any one of claims 1-4.

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