WO2024179238A1 - Method for optimizing design of acoustic surface filter, related device, and storage medium - Google Patents

Abstract

Embodiments of the present invention provide a method for optimizing design of an acoustic surface filter, a design optimizing device, and a computer-readable storage medium. The method for optimizing design of an acoustic surface filter comprises: performing modeling to obtain a model, then outputting optimization parameters, and setting optimization objectives; determining whether an insertion loss parameter meets an optimization objective corresponding thereto; if not, performing calculation on the model by using a preset first optimization function, updating a calculated result to the optimization parameters, and then returning to a previous step; if yes, performing calculation on the model by using a preset second optimization function and updating a calculated result to the optimization parameters; determining whether an out-of-band suppression parameter and the insertion loss parameter meet relative optimization objectives at the same time; if not, returning to a previous step; or if yes, outputting an optimization result of the model. Compared with the existing technology, by using the technical solution of the present invention, the dependence on an initial value can be reduced and the effect of an optimization result is good.

Description

Surface acoustic wave filter optimization design method, related equipment and storage medium Technical Field

The present invention relates to the technical field of surface acoustic wave filter design, and in particular to a surface acoustic wave filter optimization design method, optimization design equipment and computer-readable storage medium applied to the surface acoustic wave filter.

Background Art

Surface acoustic wave filter is a very common filter technology nowadays. Its basic principle is to convert electromagnetic signals into mechanical vibrations through piezoelectric materials, such as lithium niobate (LiNbO3) or lithium tantalate (LiTaO3) crystals, and then filter the band near the mechanical resonance frequency through mechanical resonance. Because the wavelength of mechanical waves is much shorter than that of electromagnetic waves at the same frequency, it is only one in three hundred thousand. Therefore, compared with traditional cavity filters, surface acoustic wave filters have the advantages of small size and high roll-off, and have been widely used in terminal scenarios such as mobile phones and base stations. In recent years, with the increasing application of surface acoustic wave filters, various surface acoustic wave filters have been applied to different scene requirements.

At present, in the prior art, the structures of surface acoustic wave filters generally include two types: one structure is a standard single cavity structure of a surface acoustic wave filter, which includes an interdigital transducer (IDT), a bus bar connecting the interdigital transducer, and a reflection grating spaced apart on opposite sides of the interdigital transducer. Among them, the interdigital transducer is used for the mutual conversion of electric and acoustic signals, the reflection grating is used to enhance the energy concentration of the sound wave to improve the resonant Q value, and the bus bar is used to connect and conduct the IDT. Multiple cavities are electrically or acoustically connected in a certain way to form a complete filter, which can achieve the function of filtering electromagnetic waves of a specific frequency. Another structure is: a dual-mode surface acoustic wave (DMS) structure. This structure uses two acoustically coupled surface acoustic wave resonators to achieve the filtering function, which can improve out-of-band suppression, reduce insertion loss and the overall performance of the surface acoustic wave filter. In practical applications, ordinary surface acoustic wave cavities are connected in series or in parallel with each other, or are electrically connected to the dual-mode surface acoustic wave cavity to form a complete surface acoustic wave filter.

However, the geometric parameters of the cavity interpolation fingers, aperture, width and thickness of the surface acoustic wave filter of the related art can be adjusted in the design. A surface acoustic wave filter contains several filter cavities. Therefore, in the actual design, a computer optimization algorithm that can simultaneously optimize multiple parameters is extremely important for the design of the surface acoustic wave filter. The existing surface acoustic wave filter optimization algorithm often has a large amount of calculation when optimizing the ordinary single cavity and the cavity of the dual-mode surface acoustic wave at the same time, and it is difficult to find the optimal solution. Sometimes, if the initial conditions are not good enough, it is even difficult to find a feasible solution. The optimization objectives of the surface acoustic wave filter often include in-band parameters, such as minimum insertion loss, minimum standing wave ratio, etc., as well as out-of-band parameters, such as maximum out-of-band suppression. When these different optimization parameters are optimized at the same time, the order of magnitude difference is often large, such as the in-band insertion loss is only 1 to 2 dB, and the out-of-band suppression is generally tens of dB. In this case, in order to optimize these parameters at the same time, the optimization weights of each parameter often need to be fine-tuned, or a similar initial value is manually adjusted first, and then improved through optimization. The above-mentioned design optimization operations make it difficult for the optimization algorithm to achieve the best effect. The optimization results are often strongly dependent on the initial values and fall into the local optimal solution, making it difficult to find the truly optimal design of the composite design indicators.

Therefore, it is necessary to provide a new method and device to solve the above technical problems.

Summary of the invention

The purpose of the present invention is to overcome the above technical problems and provide a surface acoustic wave filter optimization design method, optimization design device and computer-readable storage medium that can reduce the dependence on initial values and have good optimization results.

In a first aspect, an embodiment of the present invention provides a surface acoustic wave filter optimization design method, the method comprising the following steps:

Step S1, modeling the surface acoustic wave filter to be optimized and obtaining a model, then outputting the optimization parameters of the model, and setting the optimization target according to the optimization parameters; the optimization parameters include in-band parameters and out-of-band parameters, the in-band parameters include insertion loss parameters, the out-of-band parameters include out-of-band suppression parameters, and the optimization target includes multiple parameters related to the optimization parameters. Parameters correspond one to one;

Step S2: Obtain the insertion loss parameter output by the model, and determine whether the insertion loss parameter meets the corresponding optimization target:

If not, proceed to step S3;

If yes, proceed to step S4;

Step S3, calculating the model using a preset first optimization function, and updating the optimization parameters with the calculated results to optimize the in-band parameters of the surface acoustic wave filter, and then returning to step S2;

Step S4, calculating the model using a preset second optimization function, and updating the optimization parameters with the calculated results to achieve overall weighted optimization of the in-band parameters and the out-of-band parameters of the surface acoustic wave filter, and then outputting the updated optimization parameters;

Step S5, obtaining the out-of-band suppression parameter and the insertion loss parameter output after the step S4 is completed, and determining whether the out-of-band suppression parameter and the insertion loss parameter simultaneously meet the optimization target with respect to:

If not, the process returns to step S4;

If so, the optimization result of the model is output.

Preferably, in step S1, the modeling is to set characteristic parameters of the surface acoustic wave filter in the code, and the characteristic parameters include cavity size, the number of interdigital transducers, the number of reflection gratings and geometric parameters, and the geometric reference includes cavity aperture and wavelength.

Preferably, in step S3, the first optimization function is: target_IL-S21; wherein target_IL is the optimization target value, S21 is the insertion loss parameter, and the optimization target value and the insertion loss parameter are both negative numbers; in step S2, when the absolute value of S21 is less than the absolute value of the optimization target value, the value of the first optimization function is a positive number, and the insertion loss parameter satisfies the optimization target corresponding thereto.

Preferably, in step S4, the second optimization function is:
weight_OOB1*target_OOB1+weight_OOB2*target_OOB2
+…weight_OOBn*target_OOBn+weight_IL*target_IL-S21;

Among them, target_OOB and target_IL constitute the second optimization target value, Weight is the optimization weight, S21 is the insertion loss parameter, the second optimization target value and the insertion loss parameter are both negative numbers, and n is a positive integer greater than 2; when the absolute value of S21 is less than the absolute value of the second optimization target value, the value of the second optimization function is a positive number, and the surface acoustic wave filter is subjected to overall weighted optimization through the second optimization function, and then the updated optimization parameters are output.

Preferably, in step S5, the optimization result includes an S parameter curve and the characteristic parameters.

In a second aspect, an embodiment of the present invention further provides an optimization design device, comprising a processor and a memory, wherein the processor is used to read a program in the memory and execute the steps in the above-mentioned surface acoustic wave filter optimization design method provided in an embodiment of the present invention.

In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the steps in the above-mentioned surface acoustic wave filter optimization design method provided in the embodiment of the present invention are implemented.

Compared with the prior art, the acoustic surface filter optimization design method of the present invention implements steps S1 to S5, specifically, modeling to obtain a model, then outputting optimization parameters, setting optimization targets; judging whether the insertion loss parameter meets the corresponding optimization target: if not, the model is calculated using a preset first optimization function and the calculated result is updated with the optimization parameter, and then returns to the previous step; if so, the model is calculated using a preset second optimization function, and the calculated result is updated with the optimization parameter; judging whether the out-of-band suppression parameter and the insertion loss parameter simultaneously meet the corresponding optimization target: if not, it will return to the previous step; if so, the optimization result of the model is output. By implementing steps S1 to S5 to split the optimization design of the acoustic surface filter into dual optimization target operations, the dependence on the initial value can be greatly reduced, and it is prevented from falling into the local optimal solution, so that the optimization result can be closer to the global optimal solution. Therefore, the acoustic surface filter optimization design method, optimization design device and computer-readable storage medium of the present invention can reduce the dependence on the initial value and the optimization result is good.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

FIG1 is a flowchart of a surface acoustic wave filter optimization design method provided by an embodiment of the present invention;

2 is a graph showing the relationship between the amplitude and frequency of the output parameters S21 and S22 before the implementation step S3 of the surface acoustic wave filter optimization design method provided by the embodiment of the present invention;

3 is a graph showing the relationship between the amplitude and frequency of the output parameters S21 and S22 before the implementation step S4 of the surface acoustic wave filter optimization design method provided by an embodiment of the present invention;

4 is a graph showing the relationship between the amplitude and frequency of the output parameters S21 and S22 after the implementation step S5 of the surface acoustic wave filter optimization design method provided by the embodiment of the present invention;

FIG5 is a schematic diagram of the structure of an optimization design device provided in an embodiment of the present invention.

DETAILED DESCRIPTION

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

The terms "including" and "having" and any variations thereof in the specification, claims and drawings of the present application are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification, claims or drawings of the present application are used to distinguish different objects rather than to describe a specific order. Reference to "an embodiment or this implementation method" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative implementation method that is mutually exclusive with other embodiments. Those skilled in the art understand explicitly and implicitly that the embodiments described herein may be combined with other embodiments.

The present invention provides a surface acoustic wave filter optimization design method. The surface acoustic wave filter optimization design method is applied to the design of the surface acoustic wave filter, and specifically, the surface acoustic wave filter optimization design method is applied to the electronic design automation (English: Electronic design automation, abbreviation: EDA) software required for the automatic design of the surface acoustic wave filter.

Please refer to FIG. 1 , which is a flowchart of a surface acoustic wave filter optimization design method provided by an embodiment of the present invention.

The surface acoustic wave filter optimization design method comprises the following steps:

Step S1, modeling the surface acoustic wave filter to be optimized and obtaining the model, then outputting the optimization parameters of the model, and setting the optimization target according to the optimization parameters.

In this embodiment, the modeling is to set the characteristic parameters of the surface acoustic wave filter in the code. The characteristic parameters include the cavity size, the number of interdigital transducers, the number of reflection gratings and geometric parameters. The geometric reference includes the cavity aperture and wavelength.

The optimization parameters include in-band parameters and out-of-band parameters.

The in-band parameters include insertion loss parameters.

The out-of-band parameters include out-of-band suppression parameters.

The optimization objectives include multiple ones and correspond one-to-one to the optimization parameters.

The optimization is a computer calculation technology commonly used in the art. The optimization is a random search. When the optimization target is not met, the computer will automatically adjust the parameters to try to meet the optimization target. There is no need to manually adjust the parameters.

Step S2: Obtain the insertion loss parameter output by the model, and determine whether the insertion loss parameter meets the corresponding optimization target:

If not, proceed to step S3;

If yes, go to step S4.

Step S3, calculating the model using a preset first optimization function, and using the calculated result to update the optimization parameter to optimize the in-band parameters of the surface acoustic wave filter, and then returning to step S2.

In the step S3, the first optimization function is: target_IL-S21.

Please refer to FIG. 2, which is a graph showing the amplitude-frequency relationship of the output parameters S21 and S22 before the implementation step S3 of the surface acoustic filter optimization design method provided in an embodiment of the present invention. In FIG. 2, W1 is the curve corresponding to S21; W2 is the curve corresponding to S22; A1 is the area where the out-of-band suppression parameter in the out-of-band parameter needs to be optimized; A2 is the area where the insertion loss parameter in the in-band parameter needs to be optimized. S21 and S22 are two of the four S parameters of a two-port network with an input port and an output port, respectively. Among them, S21 is the forward transmission coefficient (i.e., gain) from the input port to the output port when the output port is matched. S22 is the reflection coefficient (i.e., output return loss) of the output port when the input port is matched. In this embodiment, the insertion loss parameter is S21. That is, only S21 is used for calculation. Of course, it is not limited to this. In some other embodiments, S22 or S21 and S22 can also be used as the insertion loss parameter.

In this embodiment, the first optimization function target_IL-S21 is the set optimization target. In Figure 2, the B1 line segment represents the S21 optimization target in the first optimization function, and the B2 line segment represents target_IL. The B1 line segment and the B2 line segment are parallel line segments, where B1 is the line segment located above and B2 is the line segment located below. The difference between the optimization target and the actual value is the difference between the B1 line segment and the B2 line segment. The first optimization function is only valid within the band, that is, within the length of the B2 line segment.

As can be seen from FIG. 2 , the amplitude of W1 in the A2 region is relatively low, and it is necessary to optimize the in-band parameters of the surface acoustic wave filter by using the first optimization function by implementing the step S3 .

Wherein, target_IL is the optimization target value, and both the optimization target value and the insertion loss parameter are negative numbers.

In this embodiment, S21 is the insertion loss parameter. Therefore, in step S2, when the absolute value of S21 is less than the absolute value of the optimization target value, the value of the first optimization function is a positive number, and the insertion loss parameter satisfies the optimization target corresponding thereto.

Step S4, calculating the model using a preset second optimization function, and updating the optimization parameters with the calculated result to achieve the band of the surface acoustic wave filter. The internal parameters and the out-of-band parameters are optimized by weighted optimization as a whole, and then the updated optimized parameters are output.

In step S4, the second optimization function is:
weight_OOB1*target_OOB1+weight_OOB2*target_OOB2
+…weight_OOBn*target_OOBn+weight_IL*target_IL-S21.

Wherein, target_OOB and target_IL constitute the second optimization target value, and Weight is the optimization weight, which indicates that the importance of suppressing different out-of-band parameters is different, and the importance of each out-of-band parameter is highlighted by manually setting the weight. The second optimization target value and the insertion loss parameter are both negative numbers. n is a positive integer greater than 2.

In this embodiment, S21 is the insertion loss parameter. Therefore, when the absolute value of S21 is less than the absolute value of the second optimization target value, the value of the second optimization function is a positive number, the surface acoustic wave filter is overall weighted optimized through the second optimization function, and then the updated optimization parameter is output.

Please refer to Figure 3, which is a graph showing the amplitude-frequency relationship of the output parameters S21 and S22 before step S4 of the surface acoustic filter optimization design method provided by an embodiment of the present invention. That is, Figure 3 is a graph after optimization by the first optimization function.

In FIG3 , W3 is the curve corresponding to S21; W4 is the curve corresponding to S22; A3 is the area where the out-of-band suppression parameters in the out-of-band parameters need to be optimized; and A4 is the area where the insertion loss parameters in the in-band parameters need to be optimized.

As can be seen from FIG. 3 , the S21 curves in the A3 region and the A4 region need to optimize the overall weighted performance of the in-band parameters and the out-of-band parameters of the surface acoustic wave filter by implementing the second optimization function in step S4 .

Step S5, obtaining the out-of-band suppression parameter and the insertion loss parameter output after the step S4 is completed, and determining whether the out-of-band suppression parameter and the insertion loss parameter simultaneously meet the optimization target with respect to:

If not, the process returns to step S4;

If so, the optimization result of the model is output.

In the step S5, the optimization result includes an S parameter curve and the characteristic parameters. In this embodiment, the S parameter curve includes an S21 parameter curve and an S22 parameter curve.

Please refer to FIG. 4, which is a graph showing the amplitude-frequency relationship of the output parameters S21 and S22 after the implementation of step S5 of the surface acoustic filter optimization design method provided by an embodiment of the present invention. In FIG. 4, W5 is the curve corresponding to S21; W6 is the curve corresponding to S22; A5 is the area where the out-of-band suppression parameter in the out-of-band parameter is optimized; A6 is the area where the insertion loss parameter in the in-band parameter is optimized.

As shown in FIG4 , the S21 curves in the A5 region and the A6 region are the final curves to be optimized, and the optimization results can be closer to the global optimal solution.

The present invention further provides an optimization design device 1000. Please refer to Figure 5, which is a schematic diagram of the structure of the optimization design device 1000 of the present invention.

The optimization design device 1000 includes a processor 1001, a memory 1002, a network interface 1003, and a computer program stored in the memory 1002 and executable on the processor 1001. The processor 1001 is used to read the program in the memory 1002. When the processor 1001 executes the computer program, the steps in the surface acoustic wave filter optimization design method provided in the embodiment are implemented. That is, the processor 1001 executes the steps in the surface acoustic wave filter optimization design method.

Specifically, the processor 1001 is configured to perform the following steps:

Step S1, modeling the surface acoustic wave filter to be optimized and obtaining a model, then outputting the optimization parameters of the model, and setting the optimization target according to the optimization parameters. The optimization parameters include in-band parameters and out-of-band parameters. The in-band parameters include insertion loss parameters. The out-of-band parameters include out-of-band suppression parameters. The optimization targets include multiple ones and correspond one to one with the optimization parameters.

Step S2: Obtain the insertion loss parameter output by the model, and determine whether the insertion loss parameter meets the corresponding optimization target:

If not, proceed to step S3;

If yes, go to step S4.

Step S3, calculating the model using a preset first optimization function, and using the calculated result to update the optimization parameter to optimize the in-band parameters of the surface acoustic wave filter, and then returning to step S2.

Step S4, calculating the model using a preset second optimization function, and using the calculated result to update the optimization parameters to achieve overall weighted optimization of the in-band parameters and the out-of-band parameters of the surface acoustic wave filter, and then outputting the updated optimization parameters.

Step S5, obtaining the out-of-band suppression parameter and the insertion loss parameter output after the step S4 is completed, and determining whether the out-of-band suppression parameter and the insertion loss parameter simultaneously meet the optimization target with respect to:

If not, the process returns to step S4;

If so, the optimization result of the model is output.

The optimization design device 1000 provided in the embodiment of the present invention can implement various implementation methods in the embodiment of the surface acoustic wave filter optimization design method, as well as the corresponding beneficial effects, which will not be described again here to avoid repetition.

It should be noted that FIG. 5 only shows 1001-1003 with components, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the optimization design device 1000 here is a device that can automatically perform numerical calculations and/or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASIC), programmable gate arrays (FPGA), digital signal processors (DSP), embedded devices, etc.

The memory 1002 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 1002 can be an internal storage unit of the optimization design device 1000, such as a hard disk or memory of the optimization design device 1000. In other embodiments, the memory 1002 can also be an external storage device of the optimization design device 1000. For example, the plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card (Flash Card) and the like equipped on the optimization design device 1000. Of course, the memory 1002 can also include both the internal storage unit of the optimization design device 1000 and its external storage device. In this embodiment, the memory 1002 is usually used to store the operating system and various application software installed in the optimization design device 1000, such as the program code of the surface acoustic wave filter optimization design method of the optimization design device 1000. In addition, the memory 1002 can also be used to temporarily store various data that have been output or are to be output.

The processor 1001 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 1001 is generally used to control the overall operation of the optimization design device 1000. In this embodiment, the processor 1001 is used to run the program code or process data stored in the memory 1002, such as running the program code of the surface acoustic wave filter optimization design method of the optimization design device 1000.

The network interface 1003 may include a wireless network interface or a wired network interface. The network interface 1003 is generally used to establish a communication connection between the optimization design device 1000 and other electronic devices.

The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by the processor 1001, the steps in the surface acoustic wave filter optimization design method as described above are implemented.

A person skilled in the art can understand that all or part of the processes in the method for optimizing the design of the surface acoustic filter of the optimization design device 1000 of the embodiment can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of each method. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

The implementation method mentioned in the embodiments of the present invention is for the convenience of description. It is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope covered by the present invention.

Compared with the prior art, the acoustic surface filter optimization design method of the present invention implements steps S1 to S5, specifically, modeling to obtain a model, then outputting optimization parameters, setting optimization targets; judging whether the insertion loss parameter meets the corresponding optimization target: if not, the model is calculated using a preset first optimization function and the calculated result is updated with the optimization parameter, and then returns to the previous step; if so, the model is calculated using a preset second optimization function, and the calculated result is updated with the optimization parameter; judging whether the out-of-band suppression parameter and the insertion loss parameter simultaneously meet the corresponding optimization target: if not, it will return to the previous step; if so, the optimization result of the model is output. By implementing steps S1 to S5 to split the optimization design of the acoustic surface filter into dual optimization target operations, the dependence on the initial value can be greatly reduced, and it is prevented from falling into the local optimal solution, so that the optimization result can be closer to the global optimal solution. Therefore, the acoustic surface filter optimization design method, optimization design device and computer-readable storage medium of the present invention can reduce the dependence on the initial value and the optimization result is good.

The above description is only an implementation mode of the present invention. It should be pointed out that, for ordinary technicians in this field, improvements can be made without departing from the creative concept of the present invention, but these all belong to the protection scope of the present invention.

Claims (7)

A surface acoustic wave filter optimization design method, characterized in that the method comprises the following steps: Step S1, modeling the surface acoustic wave filter to be optimized and obtaining a model, then outputting the optimization parameters of the model, and setting the optimization target according to the optimization parameters; the optimization parameters include in-band parameters and out-of-band parameters, the in-band parameters include insertion loss parameters, the out-of-band parameters include out-of-band suppression parameters, and the optimization targets include multiple ones and correspond one to one with the optimization parameters; Step S2: Obtain the insertion loss parameter output by the model, and determine whether the insertion loss parameter meets the corresponding optimization target: If not, proceed to step S3; If yes, proceed to step S4; Step S3, calculating the model using a preset first optimization function, and updating the optimization parameters with the calculated results to optimize the in-band parameters of the surface acoustic wave filter, and then returning to step S2; Step S4, calculating the model using a preset second optimization function, and updating the optimization parameters with the calculated results to achieve overall weighted optimization of the in-band parameters and the out-of-band parameters of the surface acoustic wave filter, and then outputting the updated optimization parameters; Step S5, obtaining the out-of-band suppression parameter and the insertion loss parameter output after the step S4 is completed, and determining whether the out-of-band suppression parameter and the insertion loss parameter simultaneously meet the optimization target with respect to: If not, the process returns to step S4; If so, the optimization result of the model is output. The surface acoustic wave filter optimization design method according to claim 1 is characterized in that in the step S1, the modeling is to set the characteristic parameters of the surface acoustic wave filter in the code, the characteristic parameters include the cavity size, the number of interdigital transducers, the number of reflection gratings and geometric parameters, and the geometric reference includes the cavity aperture and wavelength. The surface acoustic wave filter optimization design method according to claim 1 is characterized in that, in the step S3, the first optimization function is: target_IL-S21; wherein, target_IL is the optimization target value, S21 is the insertion loss parameter, and the optimization target value and the insertion loss parameter are both negative numbers; in the step S2, when the absolute value of S21 is less than the absolute value of the optimization target value, the value of the first optimization function is a positive number, and the insertion loss parameter satisfies the optimization target corresponding thereto. The surface acoustic wave filter optimization design method according to claim 1, characterized in that in step S4, the second optimization function is:
weight_OOB1*target_OOB1+weight_OOB2*target_OOB2
+…weight_OOBn*target_OOBn+weight_IL*target_IL-S21; Among them, target_OOB and target_IL constitute the second optimization target value, Weight is the optimization weight, S21 is the insertion loss parameter, the second optimization target value and the insertion loss parameter are both negative numbers, and n is a positive integer greater than 2; when the absolute value of S21 is less than the absolute value of the second optimization target value, the value of the second optimization function is a positive number, and the surface acoustic wave filter is subjected to overall weighted optimization through the second optimization function, and then the updated optimization parameters are output. The surface acoustic wave filter optimization design method according to claim 1 is characterized in that, in the step S5, the optimization result includes an S parameter curve and the characteristic parameter. An optimization design device, characterized in that the optimization design device includes a processor and a memory, and the processor is used to read the program in the memory and execute the steps in the surface acoustic wave filter optimization design method as described in any one of claims 1 to 5. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, the steps in the surface acoustic wave filter optimization design method as described in any one of claims 1 to 5 are implemented.