Surface acoustic wave filter parameter optimization method and system, and related device

Abstract

The present invention is applicable to the technical field of numerical optimization, and particularly relates to a surface acoustic wave filter parameter optimization method and system, and a related device. The method comprises: S1, constructing an optimization model on the basis of a parameter needing to be optimized of a surface acoustic wave filter, and performing optimization computation on the parameter on the basis of the optimization model to obtain an optimized parameter; S2, constructing an adaptive particle swarm optimization algorithm capable of parallel computation of particle fitness, wherein the particle fitness comprises velocity and position; and S3, performing iterative computation by taking a particle swarm comprising the optimized parameter as input of the adaptive particle swarm optimization algorithm, to obtain a parameter optimization result corresponding to the parameter. The present invention solves the problems of difficulty in optimization and low optimization efficiency in a parameter optimization process by means of parameter coupling and swarm parallelism methods, improves the computation efficiency in filter parameter optimization, and further optimizes the simulation process.

Description

Surface acoustic wave filter parameter optimization method, system and related equipment Technical Field

[0001] The present invention is applicable to the technical field of numerical optimization, and in particular relates to a method, system and related equipment for optimizing parameters of a surface acoustic wave filter. Background Art

[0002] Surface acoustic wave (SAW) filters have become an indispensable component in smartphones due to their miniaturization, low cost, and high selectivity. Designing SAW filters requires simulation tools, typically using the MBVD (modified Butterworth-Van Dyke) equivalent circuit model, the COM (Couple Of Model) model, and the finite element method. Low insertion loss and high out-of-band rejection filters are required in the RF Rx band (Receive). Such filters require a dual-mode SAW (DMS) resonator and a ladder resonator. The MBVD model is unable to simulate DMS resonators, requiring the finite element method. However, the finite element method is slow, significantly limiting the design iteration speed of the Rx filter. The COM model is widely used due to its high accuracy and simulation speed. However, filter design relies heavily on the designer's experience, which significantly limits the product iteration speed. For example, for a typical duplex receiving band 34+39, there are approximately 50 to 80 variables. Due to the large number of variables, designers need a lot of time to adjust various parameters to achieve optimal performance.

[0003] Therefore, a numerical optimization method is urgently needed to assist in the design of surface acoustic wave filters. Typical filter design problems can be divided into two categories: optimization and basic calculations. Optimization involves dealing with more than a dozen objectives and 50-80 design variables, which is already a challenging task. Basic calculations, on the other hand, require deriving simulation parameters for more than a dozen resonators, and dual-mode surface acoustic wave resonators are particularly slow to simulate. Therefore, improving simulation speed and reducing the difficulty of optimization are key issues in filter design.

[0004] Summary of the Invention

[0005] The present invention provides a surface acoustic wave filter parameter optimization method, system and related equipment, aiming to solve the problems of slow COM model calculation and low simulation design efficiency caused by large data volume when optimizing filter parameters in the prior art.

[0006] In a first aspect, the present invention provides a method for optimizing parameters of a surface acoustic wave filter, the optimization method comprising the following steps:

[0007] S1. Constructing an optimization model according to the parameters of the surface acoustic wave filter to be optimized, and performing optimization calculation on the parameters based on the optimization model to obtain optimized parameters;

[0008] S2. Constructing an adaptive particle swarm optimization algorithm capable of parallel calculation of particle fitness, wherein the particle fitness includes velocity and position;

[0009] S3. Using the particle swarm containing the optimized parameters as input of the adaptive particle swarm optimization algorithm to perform iterative calculations to obtain parameter optimization results corresponding to the parameters.

[0010] Furthermore, the optimization model satisfies the following relationship: Min IL(x); stlb≤x≤ub; G(x)≤0;

[0011] Wherein, IL represents the insertion loss of the surface acoustic wave filter, x represents the parameter, lb and ub represent the upper limit and lower limit of the parameter respectively, and G(x) represents the preset additional parameter of the surface acoustic wave filter.

[0012] Furthermore, the adaptive particle swarm optimization algorithm includes the following sub-steps:

[0013] S21, using multiple data as the initial value of the particle swarm and setting the boundary of the optimal solution to initialize the speed and position of the particles;

[0014] S22, calculating the fitness of particles in the particle swarm in parallel according to the initial value to obtain a local optimal solution and a global optimal solution;

[0015] S23, calculating the fitness of particles in the particle swarm in parallel according to the local optimal solution and the global optimal solution to obtain a current local optimal solution and a current global optimal solution;

[0016] S24, determining whether the current local optimal solution and / or the current global optimal solution reaches the boundary; if so, enlarging the value of the boundary and returning to step S23; if not, executing step S25;

[0017] S25, determining whether the current local optimal solution and the current global optimal solution meet a preset convergence condition; if so, executing step S26; if not, updating the current local optimal solution and the current global optimal solution to the local optimal solution and the global optimal solution, respectively, and returning to step S23;

[0018] S26. Output the current local optimal solution and the current global optimal solution.

[0019] Furthermore, in step S24 , the value of the boundary is defined as L, and the value of the expanded boundary is L′=L+L*10%.

[0020] Furthermore, the parameters include at least one of period length, metallization rate, aperture, spacing, and number of roots.

[0021] Furthermore, in step S1, in the step of optimizing the parameters based on the optimization model, each of the parameters is optimized and calculated respectively, and the results of the respective calculations are transformed and cascaded.

[0022] In a second aspect, the present invention further provides a system for optimizing surface acoustic wave filter parameters, comprising:

[0023] A parameter coupling module is used to construct an optimization model according to the parameters of the surface acoustic wave filter that need to be optimized, and to optimize the parameters based on the optimization model to obtain the optimized parameters;

[0024] A particle swarm optimization module is used to construct an adaptive particle swarm optimization algorithm capable of parallel calculation of particle fitness, where the particle fitness includes velocity and position;

[0025] The joint optimization module is used to perform iterative calculation using the particle swarm containing the optimization parameters as input of the adaptive particle swarm optimization algorithm to obtain parameter optimization results corresponding to the parameters.

[0026] Furthermore, the optimization model satisfies the following relationship: Min IL(x); stlb≤x≤ub; G(x)≤0;

[0027] Wherein, IL represents the insertion loss of the surface acoustic wave filter, x represents the parameter, lb and ub represent the upper limit and lower limit of the parameter respectively, and G(x) represents the preset additional parameter of the surface acoustic wave filter.

[0028] In a third aspect, the present invention also provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor implements the steps in the method for optimizing the surface acoustic wave filter parameters as described in any one of the above embodiments.

[0029] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for optimizing the surface acoustic wave filter parameters as described in any one of the above embodiments.

[0030] The beneficial effect achieved by the present invention lies in proposing a parameter optimization method for the coupling problem of filter model variables of different structures, and combining it with the cluster parallel particle swarm optimization algorithm, using multiple initial value drives and dynamic adjustment of the upper and lower limits of the optimization variables, solving the problems of difficulty in finding the best solution and low optimization efficiency in the parameter optimization process, improving the computational efficiency of filter parameter optimization, and further optimizing the simulation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] FIG1 is a method for optimizing surface acoustic wave filter parameters according to an embodiment of the present invention;

[0032] FIG2 is a schematic diagram of parameter optimization results provided by an embodiment of the present invention;

[0033] 3 is a schematic structural diagram of a system for optimizing surface acoustic wave filter parameters according to an embodiment of the present invention;

[0034] FIG4 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0036] Please refer to FIG1 , which shows a method for optimizing surface acoustic wave filter parameters according to an embodiment of the present invention. The method comprises the following steps:

[0037] S1. Constructing an optimization model according to the parameters of the surface acoustic wave filter to be optimized, and performing optimization calculation on the parameters based on the optimization model to obtain optimized parameters.

[0038] Specifically, the embodiment of the present invention designs and constructs the optimization model based on the COM (Couple Of Model) theory for calculating the admittance of surface acoustic wave devices, including ladder filters and DMS filters. Furthermore, the optimization model satisfies the following relationships: Min IL(x); stlb ≤ x ≤ ub; G(x) ≤ 0;

[0039] Wherein, IL represents the insertion loss of the surface acoustic wave filter, x represents the parameter, lb and ub represent the upper limit and lower limit of the parameter respectively, and G(x) represents the preset additional parameter of the surface acoustic wave filter.

[0040] Furthermore, the parameters include at least one of period length, metallization rate, aperture, spacing, and number of roots. For example, for a ladder filter, the specific parameters include n*Pitch (period length), Duty (metallization rate), W (aperture), Gap (spacing), and N_IDT (number of roots); while for a DMS filter, the specific parameters include the Pitch (period length), Duty (metallization rate) of each IDT (interdigitated transducer), and the gap (spacing) of the reflector between the IDTs. For some devices with special structures, such as a gradient DMS filter, geometric information such as the number of gradient roots and the pitch (spacing) of the gradient part will also be involved.

[0041] In an embodiment of the present invention, the preset additional parameters are set for the specific structure of the surface acoustic wave filter, and the target performance of the surface acoustic wave filter can be required according to actual needs. For example, the preset additional parameters G(x) include ripple, standing wave ratio (vswr), out-of-band rejection (out-rejection) of the specified passband and other parameters.

[0042] Furthermore, in step S1, the step of optimizing the parameters based on the optimization model involves separately optimizing the calculation for each parameter, and converting and concatenating the results of the separate calculations. During the actual calculation, the insertion loss IL(x) and the preset additional parameter G(x) are obtained through a single COM calculation. The admittance parameters of each device in the surface acoustic wave filter are assigned to different CPU cores for parallel calculation. Based on the upper and lower limit conditions in the optimization model, the admittance parameters are converted into scattering parameters. Finally, the scattering parameters of all devices are concatenated to obtain the complete optimized parameters for the surface acoustic wave filter. This design enables the optimization method to simultaneously couple the device parameters of different types of filters, improving computational efficiency.

[0043] S2. Construct an adaptive particle swarm optimization algorithm capable of parallel calculation of particle fitness, wherein the particle fitness includes velocity and position.

[0044] Particle Swarm Optimization (PSO) is a bionic optimal solution algorithm that continuously approaches and finds the global optimal solution through the continuous iteration of particles in the particle swarm. The speed of the particles in the particle swarm optimization algorithm represents the direction and distance the particles move in the next iteration, and the position is a solution to the problem being solved. In the embodiment of the present invention, the difference from the prior art is that the embodiment of the present invention constructs an adaptive particle swarm optimization algorithm capable of parallel computing to solve the problems of traditional algorithms that are difficult to find optimal solutions, easily fall into local optimal solutions, and have low optimization efficiency. Furthermore, the adaptive particle swarm optimization algorithm includes the following sub-steps:

[0045] S21, using multiple data as the initial value of the particle swarm and setting the boundary of the optimal solution to initialize the speed and position of the particles;

[0046] S22, calculating the fitness of particles in the particle swarm in parallel according to the initial value to obtain a local optimal solution and a global optimal solution;

[0047] S23, calculating the fitness of particles in the particle swarm in parallel according to the local optimal solution and the global optimal solution to obtain a current local optimal solution and a current global optimal solution;

[0048] S24, determining whether the current local optimal solution and / or the current global optimal solution reaches the boundary; if so, enlarging the value of the boundary and returning to step S23; if not, executing step S25;

[0049] S25, determining whether the current local optimal solution and the current global optimal solution meet a preset convergence condition; if so, executing step S26; if not, updating the current local optimal solution and the current global optimal solution to the local optimal solution and the global optimal solution, respectively, and returning to step S23;

[0050] S26. Output the current local optimal solution and the current global optimal solution.

[0051] Furthermore, in step S24, the boundary value is defined as L, and the expanded boundary value L'=L+L*10%. The 10% boundary expansion is helpful to alleviate the problem of the particle swarm being unable to evolve due to the top edge.

[0052] S3. Using the particle swarm containing the optimized parameters as input of the adaptive particle swarm optimization algorithm to perform iterative calculations to obtain parameter optimization results corresponding to the parameters.

[0053] The parameters used for the particle swarm in this embodiment of the present invention include the optimization parameters. During implementation, different parameters may be added to the particle swarm to enrich the population's diversity. For example, the insertion loss data IL(x) from the optimization parameters outputted in step S1 may be added to the initial particle swarm as part of the particles, while the other part may be the insertion loss data processed using out-of-band suppression. The speed and position updates of the adaptive particle swarm optimization algorithm during calculations can be set according to actual needs. Furthermore, as described in steps S22 and S23, parallel computing is also employed when calculating the fitness of different particles to improve computational efficiency.

[0054] For example, according to the above method, an embodiment of the present invention designs a band 40 surface acoustic wave filter using a normal-saw process and a 42° YX LiTaO3 substrate. The filter uses four ladder resonators and one DMS resonator. The final parameter optimization results are shown in Figure 2. Figure 2 shows the optimization results through S parameters (Scatter parameters, scattering parameters), where S21 represents the forward transmission coefficient, S11 and S22 represent the reflection coefficients when different ports in the filter are matched. Through continuous iterative calculation, the optimal parameter values ​​obtained according to the curve in Figure 2 are insertion loss IL = 1.4dB, standing wave ratio vswr = 1.5, and out-of-band noise suppression at 2.43GHz is -30dB. In other words, the surface acoustic wave filter parameter optimization method proposed in the embodiment of the present invention can well obtain the required optimization parameters.

[0055] The beneficial effect achieved by the present invention lies in proposing a parameter optimization method for the coupling problem of filter model variables of different structures, and combining it with the cluster parallel particle swarm optimization algorithm, using multiple initial value drives and dynamic adjustment of the upper and lower limits of the optimization variables, solving the problems of difficulty in finding the best solution and low optimization efficiency in the parameter optimization process, improving the computational efficiency of filter parameter optimization, and further optimizing the simulation process.

[0056] The embodiment of the present invention further provides a system 200 for optimizing surface acoustic wave filter parameters. Please refer to FIG3 , which is a schematic structural diagram of the system 200 for optimizing surface acoustic wave filter parameters provided by the embodiment of the present invention, and includes:

[0057] A parameter coupling module 201 is used to construct an optimization model according to the parameters of the surface acoustic wave filter to be optimized, and to optimize the parameters based on the optimization model to obtain optimized parameters;

[0058] A particle swarm optimization module 202 is used to construct an adaptive particle swarm optimization algorithm capable of parallel calculation of particle fitness, wherein the particle fitness includes velocity and position;

[0059] The joint optimization module 203 is configured to perform iterative calculations using the particle swarm containing the optimization parameters as input to the adaptive particle swarm optimization algorithm to obtain parameter optimization results corresponding to the parameters.

[0060] Furthermore, the optimization model satisfies the following relationship: Min IL(x); stlb≤x≤ub; G(x)≤0;

[0061] Wherein, IL represents the insertion loss of the surface acoustic wave filter, x represents the parameter, lb and ub represent the upper limit and lower limit of the parameter respectively, and G(x) represents the preset additional parameter of the surface acoustic wave filter.

[0062] Furthermore, when the parameter coupling module 201 optimizes the parameters based on the optimization model, it optimizes each parameter separately and transforms and concatenates the results of the separate calculations. During the actual calculation, the insertion loss IL(x) and the preset additional parameter G(x) are obtained through a single COM calculation. The admittance parameters of each device in the surface acoustic wave filter are assigned to different CPU cores for parallel calculation. Based on the upper and lower limit conditions in the optimization model, the admittance parameters are converted into scattering parameters. Finally, the scattering parameters of all devices are concatenated to obtain the complete optimized parameters for the surface acoustic wave filter. This design enables the optimization method to simultaneously couple the device parameters of different types of filters, improving computational efficiency.

[0063] The surface acoustic wave filter parameter optimization system 200 can implement the steps in the surface acoustic wave filter parameter optimization method in the above embodiment and can achieve the same technical effects. Please refer to the description in the above embodiment and will not be repeated here.

[0064] An embodiment of the present invention further provides a computer device. Please refer to Figure 4, which is a structural diagram of the computer device provided by an embodiment of the present invention. The computer device 300 includes: a memory 302, a processor 301, and a computer program stored in the memory 302 and executable on the processor 301.

[0065] The processor 301 calls the computer program stored in the memory 302 to execute the steps of the method for optimizing the parameters of the surface acoustic wave filter provided by the embodiment of the present invention. Referring to FIG. 1 , the method specifically includes the following steps:

[0066] S1. Constructing an optimization model according to the parameters of the surface acoustic wave filter to be optimized, and performing optimization calculation on the parameters based on the optimization model to obtain optimized parameters.

[0067] Furthermore, the optimization model satisfies the following relationship: Min IL(x); stlb≤x≤ub; G(x)≤0;

[0068] Wherein, IL represents the insertion loss of the surface acoustic wave filter, x represents the parameter, lb and ub represent the upper limit and lower limit of the parameter respectively, and G(x) represents the preset additional parameter of the surface acoustic wave filter.

[0069] Furthermore, the parameters include at least one of period length, metallization rate, aperture, spacing, and number of roots.

[0070] Furthermore, in step S1, in the step of optimizing the parameters based on the optimization model, each of the parameters is optimized and calculated respectively, and the results of the respective calculations are transformed and cascaded.

[0071] S2. Construct an adaptive particle swarm optimization algorithm capable of parallel calculation of particle fitness, wherein the particle fitness includes velocity and position.

[0072] Furthermore, the adaptive particle swarm optimization algorithm includes the following sub-steps:

[0073] S21, using multiple data as the initial value of the particle swarm and setting the boundary of the optimal solution to initialize the speed and position of the particles;

[0074] S22, calculating the fitness of particles in the particle swarm in parallel according to the initial value to obtain a local optimal solution and a global optimal solution;

[0075] S23, calculating the fitness of particles in the particle swarm in parallel according to the local optimal solution and the global optimal solution to obtain a current local optimal solution and a current global optimal solution;

[0076] S24, determining whether the current local optimal solution and / or the current global optimal solution reaches the boundary; if so, enlarging the value of the boundary and returning to step S23; if not, executing step S25;

[0077] S25, determining whether the current local optimal solution and the current global optimal solution meet a preset convergence condition; if so, executing step S26; if not, updating the current local optimal solution and the current global optimal solution to the local optimal solution and the global optimal solution, respectively, and returning to step S23;

[0078] S26. Output the current local optimal solution and the current global optimal solution.

[0079] Furthermore, in step S24, the boundary value is defined as L, and the expanded boundary value L'=L+L*10%. The 10% boundary expansion is helpful to alleviate the problem of the particle swarm being unable to evolve due to the top edge.

[0080] S3. Using the particle swarm containing the optimized parameters as input of the adaptive particle swarm optimization algorithm to perform iterative calculations to obtain parameter optimization results corresponding to the parameters.

[0081] The computer device 300 provided in the embodiment of the present invention can implement the steps in the method for optimizing the parameters of the surface acoustic wave filter in the above embodiment and can achieve the same technical effects. Please refer to the description in the above embodiment and will not be repeated here.

[0082] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes and steps in the method for optimizing the surface acoustic wave filter parameters provided in the embodiment of the present invention are implemented, and the same technical effects can be achieved. To avoid repetition, they are not described here.

[0083] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0084] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0085] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0086] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only a preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, which are all within the protection of the present invention.

Claims

1. A method for optimizing surface acoustic wave filter parameters, characterized in that: The optimization method comprises the following steps: S1, constructing an optimization model according to the parameters of the surface acoustic wave filter to be optimized, and optimizing and calculating the parameters based on the optimization model to obtain optimized parameters; S2. Constructing an adaptive particle swarm optimization algorithm capable of parallel calculation of particle fitness, wherein the particle fitness includes speed and position; S3. Using the particle swarm containing the optimization parameters as input of the adaptive particle swarm optimization algorithm to perform iterative calculations to obtain parameter optimization results corresponding to the parameters.

2. The method for optimizing the parameters of a surface acoustic wave filter according to claim 1, wherein: The optimization model satisfies the following relationship: Min IL(x); stlb≤x≤ub; G(x)≤0; Among them, IL represents the insertion loss of the surface acoustic wave filter, x represents the parameter, lb and ub represent the upper limit and lower limit of the parameter respectively, and G(x) represents the preset additional parameter of the surface acoustic wave filter.

3. The method for optimizing the parameters of a surface acoustic wave filter according to claim 1, characterized in that: The adaptive particle swarm optimization algorithm includes the following sub-steps: S21, using multiple data as the initial value of the particle swarm, and setting the boundary of the optimal solution to initialize the speed and position of the particles; S22, calculating the fitness of particles in the particle swarm in parallel according to the initial value to obtain a local optimal solution and a global optimal solution; S23, calculating the fitness of particles in the particle swarm in parallel according to the local optimal solution and the global optimal solution to obtain a current local optimal solution and a current global optimal solution; S24, determining whether the current local optimal solution and / or the current global optimal solution reaches the boundary, if so, expanding the value of the boundary and returning to step S23; if not, executing step S25; S25, determining whether the current local optimal solution and the current global optimal solution meet the preset convergence condition, if so, executing step S26; if not, updating the current local optimal solution and the current global optimal solution to the local optimal solution and the global optimal solution respectively, and returning to step S23; S26. Output the current local optimal solution and the current global optimal solution.

4. The method for optimizing the parameters of a surface acoustic wave filter as claimed in claim 3, characterized in that: In step S24, the value of the boundary is defined as L, and the value of the expanded boundary is L'=L+L*10%.

5. The method for optimizing the parameters of a surface acoustic wave filter according to claim 1, characterized in that: The parameters include at least one of period length, metallization rate, aperture, spacing, and number of roots.

6. The method for optimizing the parameters of a surface acoustic wave filter according to claim 1, wherein: In step S1, the step of optimizing the parameters based on the optimization model is to optimize the parameters respectively and transform and cascade the results of the respective calculations.

7. A system for optimizing surface acoustic wave filter parameters, characterized in that: include: A parameter coupling module is used to construct an optimization model according to the parameters of the surface acoustic wave filter that need to be optimized, and optimize the parameters based on the optimization model to obtain the optimized parameters; A particle swarm optimization module is used to construct an adaptive particle swarm optimization algorithm capable of parallel calculation of particle fitness, wherein the particle fitness includes speed and position; The joint optimization module is used to perform iterative calculation using the particle swarm containing the optimization parameters as the input of the adaptive particle swarm optimization algorithm to obtain parameter optimization results corresponding to the parameters.

8. The system for optimizing the parameters of a surface acoustic wave filter as claimed in claim 7, characterized in that: The optimization model satisfies the following relationship: Min IL(x); stlb≤x≤ub; G(x)≤0; Among them, IL represents the insertion loss of the surface acoustic wave filter, x represents the parameter, lb and ub represent the upper limit and lower limit of the parameter respectively, and G(x) represents the preset additional parameter of the surface acoustic wave filter.

9. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps in the method for optimizing the parameters of a surface acoustic wave filter as claimed in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the method for optimizing the parameters of a surface acoustic wave filter as claimed in any one of claims 1 to 6 are implemented.

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