Simulation method and system for surface acoustic wave device, and related device

Abstract

The present invention is applicable to the technical field of communications, and in particular to a simulation method and system for a surface acoustic wave device, and a related device. According to the present invention, measured response parameters of a surface acoustic wave device are acquired; an accurate simulation model of the surface acoustic wave device is established on the basis of a first preset algorithm, and geometric design parameters corresponding to respective resonators are used as input of the accurate simulation model of the surface acoustic wave device for calculation, to obtain simulated response parameters corresponding to the respective resonators; iterative optimization is performed on the accurate simulation model of the surface acoustic wave device according to an error function on the basis of a second preset algorithm, to obtain an optimized accurate simulation model of the surface acoustic wave device; and a simulation result is obtained by means of the optimized accurate simulation model of the surface acoustic wave device. Compared with the prior art, the present invention can effectively reduce simulation impact on a surface acoustic wave device caused by process errors during production and changes in packaging and testing environments, improve design efficiency of the surface acoustic wave device, and reduce costs of repeated production testing.

Description

Simulation methods, systems and related equipment for surface acoustic wave devices Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a simulation method, system and related equipment for surface acoustic wave devices. Background Technology

[0002] Surface acoustic wave (SAW) filters are a crucial component of the radio frequency (RF) front-end in the communications industry, widely used due to their excellent frequency selectivity, low power consumption, and small size. Simulation technology plays a vital role in the design and manufacture of SAW filters, as accurate simulations help designers optimize filter performance. Accurate simulation models for SAW filters include finite element analysis (FEM), finite element / boundary element method (FEM / BEM), and hierarchical cascaded algorithm (HCT). These models are based on the fundamental physical laws of the system, combining mechanical, electrical, and other multi-physics fields to describe the system's behavior. By selecting appropriate material parameters, adjusting the device geometry (including finger width, finger spacing, number of interdigitates, etc.), and setting boundary conditions, the response parameters of the SAW device can be simulated and calculated, thereby predicting its actual performance, reducing repeated experiments in actual manufacturing, and saving costs and time.

[0003] However, the accuracy of simulation results for surface acoustic wave (SAW) devices is closely related to material parameters. Although several publications have verified and published material parameters obtained under experimental conditions, these parameters do not yield highly accurate results in practical applications. Particularly in high-frequency applications, significant discrepancies exist between simulation and test results, limiting their application. Reasons for these simulation-test discrepancies include: process errors during device fabrication, minor differences between simplified simulation conditions and actual conditions, and performance impacts caused by variations in packaging and testing environments.

[0004] Therefore, there is an urgent need for a new simulation method, system, and related equipment for surface acoustic wave devices to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention provides a simulation method, system, and related equipment for surface acoustic wave (SAW) devices, aiming to improve the simulation cost and efficiency of SAW devices.

[0006] In a first aspect, the present invention provides a simulation method for a surface acoustic wave device, the simulation method comprising the following steps:

[0007] S1. Obtain the test response parameters of the surface acoustic wave device; wherein the surface acoustic wave device includes multiple resonators with different geometric design parameters;

[0008] S2. Based on the first preset algorithm, establish an accurate simulation model of the surface acoustic wave device, and use the geometric design parameters corresponding to each resonator as input to the accurate simulation model of the surface acoustic wave device to calculate the simulation response parameters corresponding to each resonator.

[0009] S3. Establish an error function based on the resonant frequency point and anti-resonant frequency point in the test response parameters and the resonant frequency point and anti-resonant frequency point in the simulation response parameters. Based on the second preset algorithm, iteratively optimize the accurate simulation model of the surface acoustic wave device according to the error function to obtain an optimized accurate simulation model of surface acoustic wave.

[0010] S4. The geometric design parameters corresponding to each resonator in the surface acoustic wave device to be simulated are used as inputs to the accurate simulation model of the optimized surface acoustic wave device to obtain the simulation results.

[0011] Preferably, step S3 includes the following steps:

[0012] S31. Calculate the first center point based on the resonant frequency point and anti-resonant frequency point in the test response parameters, and take the area of ​​the first center point within a preset range as the first fundamental frequency region, and extract the extreme point in the first fundamental frequency region as the first feature point.

[0013] S32. Calculate the second center point based on the resonant frequency point and anti-resonant frequency point in the simulation response parameters, and take the area of ​​the second center point within a preset range as the second fundamental frequency region, and extract the extreme points in the second fundamental frequency region as the second feature points.

[0014] S33. Calculate the error function based on the first feature point and the second feature point;

[0015] S34. Define the coefficients corresponding to the material parameters of the resonator as fitting parameters, and iteratively optimize the fitting parameters according to the error function using the second preset algorithm to obtain optimized fitting parameters; wherein, the material parameters include piezoelectric substrate material and metal electrode material;

[0016] S35. Optimize the surface acoustic wave (SAW) accurate simulation model according to the optimized fitting parameters to obtain the optimized SAW accurate simulation model.

[0017] Preferably, in step S2, the simulation response parameters are calculated based on the following relationship:

[0018] Where c represents the elastic constant, e represents the piezoelectric constant, ρ represents the density, and ε represents the dielectric constant. Let E represent displacement, E represent electric field, and S represent strain.

[0019] Preferably, in step S34, the fitting parameters and the material parameters satisfy the following relationship:

[0020] Where r1 represents the density fitting parameter, r2 represents the elastic constant fitting parameter, r3 represents the piezoelectric constant fitting parameter, r4 represents the dielectric constant fitting parameter, r5 represents the first imaginary number fitting parameter, r6 represents the second imaginary number fitting parameter, and i represents the imaginary number.

[0021] Preferably, the error function is defined as Ea, and the error function satisfies the following relationship:

[0022] Among them, w A w represents the amplitude error weighting coefficient. f The frequency error weighting coefficient is represented by , n represents the number of resonators, A represents the amplitude of the second feature point, A′ represents the amplitude of the first feature point, f represents the frequency of the second feature point, and f′ represents the frequency of the first feature point.

[0023] Preferably, the first preset algorithm includes one or more of the finite element method, finite element / boundary element method, and hierarchical cascade algorithm.

[0024] Preferably, the second preset algorithm includes one or more of the following: genetic algorithm, particle swarm optimization algorithm, and simulated annealing algorithm.

[0025] Secondly, the present invention also provides a simulation system for surface acoustic wave devices, comprising:

[0026] A test module is used to acquire test response parameters of a surface acoustic wave (SAW) device; wherein the SAW device includes multiple resonators with different geometric design parameters.

[0027] The simulation parameter calculation module is used to establish an accurate simulation model of the surface acoustic wave device based on a first preset algorithm. The geometric design parameters corresponding to each resonator are used as inputs to the accurate simulation model of the surface acoustic wave device to calculate the simulation response parameters corresponding to each resonator.

[0028] The iterative optimization module is used to establish an error function based on the test response parameters and the simulation response parameters, and to iteratively optimize the accurate simulation model of the surface acoustic wave device based on the error function using a second preset algorithm, so as to obtain an optimized accurate simulation model of the surface acoustic wave.

[0029] The simulation module is used to simulate the surface acoustic wave device by taking the geometric design parameters corresponding to each resonator as input to the optimized surface acoustic wave device accurate simulation model, and obtain the simulation results.

[0030] Thirdly, the present invention also provides a computer device, comprising: a memory, a processor, and a simulation program for a surface acoustic wave device stored in the memory and executable on the processor, wherein when the processor executes the simulation program for the surface acoustic wave device, it implements the steps in the simulation method for the surface acoustic wave device as described in any of the above embodiments.

[0031] Fourthly, the present invention also provides a computer-readable storage medium storing a simulation program for a surface acoustic wave (SAW) device, wherein when the simulation program is executed by a processor, it implements the steps in the simulation method for a SAW device as described in any of the above embodiments.

[0032] Compared with existing technologies, this invention obtains the test response parameters of a surface acoustic wave (SAW) device. The SAW device comprises multiple resonators with different geometric design parameters. A precise simulation model of the SAW device is established based on a first preset algorithm. The geometric design parameters corresponding to each resonator are used as inputs to the precise simulation model to calculate the simulation response parameters for each resonator. An error function is established based on the test response parameters and the simulation response parameters. The precise simulation model is iteratively optimized based on the error function using a second preset algorithm to obtain an optimized precise simulation model. The geometric design parameters corresponding to each resonator in the SAW device to be simulated are used as inputs to the optimized precise simulation model to obtain the simulation results. This invention effectively reduces the simulation impact of process errors and changes in the packaging and testing environment during the production of SAW devices, improves the design efficiency of SAW devices, and reduces the cost of repeated production testing. Attached Figure Description

[0033] The present invention will now be described in detail with reference to the accompanying drawings. The above and other aspects of the present invention will become clearer and more readily understood through the detailed description following the accompanying drawings. In the drawings:

[0034] Figure 1 is a flowchart of the simulation method for surface acoustic wave devices provided in an embodiment of the present invention;

[0035] Figure 2 is a schematic diagram of the first fundamental frequency region of the simulation method for the surface acoustic wave device provided in the embodiment of the present invention;

[0036] Figure 3 is a simulation test comparison diagram of the response parameters of the simulation method of the surface acoustic wave device provided in the embodiment of the present invention;

[0037] Figure 4 is a simulation test comparison diagram of the response parameters of the surface acoustic wave dual-mode device according to the simulation method of the surface acoustic wave device provided in the embodiment of the present invention;

[0038] Figure 5 is a schematic diagram of the simulation system of the surface acoustic wave device provided in the embodiment of the present invention;

[0039] Figure 6 is a schematic diagram of the structure of the computer device provided in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0041] Example 1

[0042] Please refer to Figures 1-4. This invention provides a simulation method for a surface acoustic wave (SAW) device, which includes the following steps:

[0043] S1. Obtain the test response parameters of the surface acoustic wave device; wherein the surface acoustic wave device includes multiple resonators with different geometric design parameters.

[0044] In this embodiment of the invention, the test response parameters of the surface acoustic wave (SAW) device are obtained by testing it with other SAW devices under similar process conditions and test conditions. Similar process conditions refer to SAW devices having clearly defined process flows and parameters, specific application scenarios, and typically belonging to the same batch of fabricated devices. Similar test conditions refer to all SAW devices being tested under the same external environment and measurement equipment within a short period of time during the testing phase. The test response parameters include admittance parameters and scattering parameters, and the geometric design parameters include parameters such as finger period, metallization rate, and aperture.

[0045] S2. Based on the first preset algorithm, establish an accurate simulation model of the surface acoustic wave device, and use the geometric design parameters corresponding to each resonator as input to the accurate simulation model of the surface acoustic wave device to calculate the simulation response parameters corresponding to each resonator.

[0046] In this embodiment of the invention, the simulation response parameters, admittance parameters, and simulation parameters, the first preset algorithm includes one or more of the finite element method, finite element / boundary element method, and hierarchical cascade algorithm, but is not limited to the above algorithms, other algorithms are also feasible.

[0047] In this embodiment of the invention, in step S2, the simulation response parameters are calculated based on the following relationship (piezoelectric physics equation):

[0048] Where c represents the elastic constant, e represents the piezoelectric constant, ρ represents the density, and ε represents the dielectric constant. Indicates displacement ( (where E is the second derivative), E represents the electric field, and S represents the strain. Both E and S are expressions.

[0049] S3. Establish an error function based on the resonant frequency point and anti-resonant frequency point in the test response parameters and the resonant frequency point and anti-resonant frequency point in the simulation response parameters. Based on the second preset algorithm, iteratively optimize the accurate simulation model of the surface acoustic wave device according to the error function to obtain an optimized accurate simulation model of surface acoustic wave.

[0050] In this embodiment of the invention, the second preset algorithm includes one or more of the following: genetic algorithm, particle swarm optimization algorithm, and simulated annealing algorithm. Of course, it is not limited to the above algorithms, and other algorithms are also feasible.

[0051] In this embodiment of the invention, step S3 includes the following steps:

[0052] S31. Calculate the first center point based on the resonant frequency point and anti-resonant frequency point in the test response parameters, and take the area within a preset range of the first center point as the first fundamental frequency region. Extract the extreme points in the first fundamental frequency region as the first feature points (i.e., select the points where each test response parameter has obvious peaks or troughs on the curve). As shown in Figure 2, Figure 2 is a schematic diagram of the first fundamental frequency region of the simulation method of the surface acoustic wave device provided in the embodiment of the present invention. In the figure, f1 is the resonant frequency point, f2 is the anti-resonant frequency point, and the first center point is (f1+f2) / 2. Taking a preset range of 200MHz as an example, the extreme points in the first fundamental frequency region include the resonant frequency point and the anti-resonant frequency point, as well as the other 4 points (i.e., f3~f6). Therefore, the first feature points include f1~f6, six feature points.

[0053] S32. Calculate the second center point based on the resonant frequency point and anti-resonant frequency point in the simulation response parameters, and take the area of ​​the second center point within a preset range as the second fundamental frequency region, and extract the extreme points in the second fundamental frequency region as the second feature points; it should be noted that the first feature points are the maximum and minimum values ​​within the preset range, and the second feature points are other extreme points besides the first feature points.

[0054] S33. Calculate the error function based on the first feature point and the second feature point;

[0055] S34. Define the coefficients corresponding to the material parameters of the resonator as fitting parameters, and iteratively optimize the fitting parameters according to the error function using the second preset algorithm to obtain optimized fitting parameters; wherein, the material parameters include piezoelectric substrate material and metal electrode material, the material parameters of the piezoelectric substrate material include elastic constant c, piezoelectric constant e, dielectric constant ε, and density ρ, and the material parameters of the metal electrode material include elastic constant c and density ρ.

[0056] In this embodiment of the invention, in step S34, the fitting parameters and the material parameters satisfy the following relationship:

[0057] Where r1 represents the density fitting parameter, r2 represents the elastic constant fitting parameter, r3 represents the piezoelectric constant fitting parameter, r4 represents the dielectric constant fitting parameter, r5 represents the first imaginary number fitting parameter, r6 represents the second imaginary number fitting parameter, and i represents the imaginary number.

[0058] In this embodiment of the invention, the error function is defined as Ea, and the error function satisfies the following relationship:

[0059] Among them, w A w represents the amplitude error weighting coefficient. f The frequency error weighting coefficient is represented by , n represents the number of resonators, A represents the amplitude of the second feature point, A′ represents the amplitude of the first feature point, f represents the frequency of the second feature point, and f′ represents the frequency of the first feature point.

[0060] Specifically, the amplitude and frequency of the second feature point include the amplitude and frequency of all feature points within the second fundamental frequency region, and the amplitude and frequency of the first feature point include the amplitude and frequency of all feature points within the first fundamental frequency region. The difference between the amplitude and frequency of the first feature point and the amplitude and frequency of the second feature point is the error function. By automatically iteratively calculating and reducing the error function, the optimized fitting parameters corresponding to the material parameters that meet the conditions are finally obtained.

[0061] S35. Optimize the surface acoustic wave (SAW) precise simulation model according to the optimized fitting parameters to obtain the optimized SAW precise simulation model. When the optimized SAW precise simulation model simulates the SAW device to be simulated, it satisfies the relationship shown in formula (2).

[0062] S4. The geometric design parameters corresponding to each resonator in the surface acoustic wave device to be simulated are used as inputs to the accurate simulation model of the optimized surface acoustic wave device to obtain the simulation results.

[0063] In this embodiment of the invention, the geometric design parameters corresponding to each resonator in the surface acoustic wave (SAW) device to be simulated are used as the optimized SAW device accurate simulation model. The results are shown in Figures 3 and 4. Figure 3 is a simulation test comparison diagram of the response parameters of the SAW device simulation method provided in this embodiment of the invention; Figure 4 is a simulation test comparison diagram of the response parameters of the SAW dual-mode device using the SAW device simulation method provided in this embodiment of the invention. It can be seen that the simulation results are more accurate when using the optimized SAW device accurate simulation model proposed in this invention.

[0064] Compared with existing technologies, this invention obtains the test response parameters of a surface acoustic wave (SAW) device. The SAW device comprises multiple resonators with different geometric design parameters. A precise simulation model of the SAW device is established based on a first preset algorithm. The geometric design parameters corresponding to each resonator are used as inputs to the precise simulation model to calculate the simulation response parameters for each resonator. An error function is established based on the test response parameters and the simulation response parameters. The precise simulation model is iteratively optimized based on the error function using a second preset algorithm to obtain an optimized precise simulation model. The geometric design parameters corresponding to each resonator in the SAW device to be simulated are used as inputs to the optimized precise simulation model to obtain the simulation results. This invention effectively reduces the simulation impact of process errors and changes in the packaging and testing environment during the production of SAW devices, improves the design efficiency of SAW devices, and reduces the cost of repeated production testing.

[0065] Example 2

[0066] This invention also provides a simulation system for a surface acoustic wave (SAW) device. Referring to Figure 5, Figure 5 is a schematic diagram of the structure of the SAW simulation system 200 provided in this invention, which includes:

[0067] S201, a test module, used to acquire test response parameters of a surface acoustic wave device; wherein, the surface acoustic wave device includes multiple resonators with different geometric design parameters;

[0068] S202, Simulation parameter calculation module, used to establish an accurate simulation model of surface acoustic wave device based on the first preset algorithm, and to calculate the simulation response parameters corresponding to each resonator by taking the geometric design parameters corresponding to each resonator as input to the accurate simulation model of surface acoustic wave device.

[0069] S203, Iterative optimization module, used to establish an error function based on the resonant frequency point and anti-resonant frequency point in the test response parameters and the resonant frequency point and anti-resonant frequency point in the simulation response parameters, and to iteratively optimize the accurate simulation model of the surface acoustic wave device based on the error function using a second preset algorithm to obtain an optimized accurate simulation model of the surface acoustic wave.

[0070] S204, Simulation Module, is used to simulate the surface acoustic wave device by taking the geometric design parameters corresponding to each resonator as input to the accurate simulation model of the optimized surface acoustic wave device, and obtain the simulation results.

[0071] The simulation system 200 for the surface acoustic wave device can implement the steps in the simulation method for the surface acoustic wave device as described in the above embodiments, and can achieve the same technical effect. Refer to the description in the above embodiments, which will not be repeated here.

[0072] Example 3

[0073] This invention also provides a computer device. Please refer to Figure 6, which is a schematic diagram of the structure of the computer device provided in this invention. The computer device 300 includes: a memory 302, a processor 301, and a simulation program for a surface acoustic wave device stored in the memory 302 and capable of running on the processor 301.

[0074] The processor 301 calls the simulation program for the surface acoustic wave device stored in the memory 302 and executes the steps in the simulation method for the surface acoustic wave device provided in this embodiment of the invention. Referring to Figure 1, the specific steps include:

[0075] S1. Obtain the test response parameters of the surface acoustic wave device; wherein the surface acoustic wave device includes multiple resonators with different geometric design parameters.

[0076] In this embodiment of the invention, the test response parameters of the surface acoustic wave (SAW) device are obtained by testing it with other SAW devices under similar process conditions and test conditions. Similar process conditions refer to SAW devices having clearly defined process flows and parameters, specific application scenarios, and typically belonging to the same batch of fabricated devices. Similar test conditions refer to all SAW devices being tested under the same external environment and measurement equipment within a short period of time during the testing phase. The test response parameters include admittance parameters and scattering parameters, and the geometric design parameters include parameters such as finger period, metallization rate, and aperture.

[0077] S2. Based on the first preset algorithm, establish an accurate simulation model of the surface acoustic wave device, and use the geometric design parameters corresponding to each resonator as input to the accurate simulation model of the surface acoustic wave device to calculate the simulation response parameters corresponding to each resonator.

[0078] In this embodiment of the invention, the simulation response parameters, admittance parameters, and simulation parameters, the first preset algorithm includes one or more of the finite element method, finite element / boundary element method, and hierarchical cascade algorithm, but is not limited to the above algorithms, other algorithms are also feasible.

[0079] In this embodiment of the invention, in step S2, the simulation response parameters are calculated based on the following relationship (piezoelectric physics equation):

[0080] Where c represents the elastic constant, e represents the piezoelectric constant, ρ represents the density, and ε represents the dielectric constant. Indicates displacement ( (where E is the second derivative), E represents the electric field, and S represents the strain. Both E and S are expressions.

[0081] S3. Establish an error function based on the resonant frequency point and anti-resonant frequency point in the test response parameters and the resonant frequency point and anti-resonant frequency point in the simulation response parameters. Based on the second preset algorithm, iteratively optimize the accurate simulation model of the surface acoustic wave device according to the error function to obtain an optimized accurate simulation model of surface acoustic wave.

[0082] In this embodiment of the invention, the second preset algorithm includes one or more of the following: genetic algorithm, particle swarm optimization algorithm, and simulated annealing algorithm. Of course, it is not limited to the above algorithms, and other algorithms are also feasible.

[0083] In this embodiment of the invention, step S3 includes the following steps:

[0084] S31. Calculate the first center point based on the resonant frequency point and anti-resonant frequency point in the test response parameters, and take the area within a preset range of the first center point as the first fundamental frequency region. Extract the extreme points in the first fundamental frequency region as the first feature points (i.e., select the points with obvious peaks or troughs on the curve for each test response parameter). As shown in Figure 2, Figure 2 is a schematic diagram of the first fundamental frequency region of the simulation method of the surface acoustic wave device provided in the embodiment of the present invention. In the figure, f1 is the resonant frequency point, f2 is the anti-resonant frequency point, and the first center point is (f1+f2) / 2. Taking a preset range of 200MHz as an example, the extreme points in the first fundamental frequency region include the resonant frequency point and the anti-resonant frequency point, as well as the other 4 points (i.e., f3~f6). Therefore, the first feature points include f1~f6, six feature points.

[0085] S32. Calculate the second center point based on the resonant frequency point and anti-resonant frequency point in the simulation response parameters, and take the area of ​​the second center point within a preset range as the second fundamental frequency region, and extract the extreme points in the second fundamental frequency region as the second feature points.

[0086] S33. Calculate the error function based on the first feature point and the second feature point;

[0087] S34. Define the coefficients corresponding to the material parameters of the resonator as fitting parameters, and iteratively optimize the fitting parameters according to the error function using the second preset algorithm to obtain optimized fitting parameters; wherein, the material parameters include piezoelectric substrate material and metal electrode material, the material parameters of the piezoelectric substrate material include elastic constant c, piezoelectric constant e, dielectric constant ε, and density ρ, and the material parameters of the metal electrode material include elastic constant c and density ρ.

[0088] In this embodiment of the invention, in step S34, the fitting parameters and the material parameters satisfy the following relationship:

[0089] Where r1 represents the density fitting parameter, r2 represents the elastic constant fitting parameter, r3 represents the piezoelectric constant fitting parameter, r4 represents the dielectric constant fitting parameter, r5 represents the first imaginary number fitting parameter, r6 represents the second imaginary number fitting parameter, and i represents the imaginary number.

[0090] In this embodiment of the invention, the error function is defined as Ea, and the error function satisfies the following relationship:

[0091] Among them, w A w represents the amplitude error weighting coefficient. f The frequency error weighting coefficient is represented by , n represents the number of resonators, A represents the amplitude of the second feature point, A′ represents the amplitude of the first feature point, f represents the frequency of the second feature point, and f′ represents the frequency of the first feature point.

[0092] Specifically, the amplitude and frequency of the second feature point include the amplitude and frequency of all feature points within the second fundamental frequency region, and the amplitude and frequency of the first feature point include the amplitude and frequency of all feature points within the first fundamental frequency region. The difference between the amplitude and frequency of the first feature point and the amplitude and frequency of the second feature point is the error function. By automatically iteratively calculating and reducing the error function, the optimized fitting parameters corresponding to the material parameters that meet the conditions are finally obtained.

[0093] S35. Optimize the surface acoustic wave (SAW) precise simulation model according to the optimized fitting parameters to obtain the optimized SAW precise simulation model. When the optimized SAW precise simulation model simulates the SAW device to be simulated, it satisfies the relationship shown in formula (2).

[0094] S4. The geometric design parameters corresponding to each resonator in the surface acoustic wave device to be simulated are used as inputs to the accurate simulation model of the optimized surface acoustic wave device to obtain the simulation results.

[0095] In this embodiment of the invention, the geometric design parameters corresponding to each resonator in the surface acoustic wave (SAW) device to be simulated are used as the optimized SAW device accurate simulation model. The results are shown in Figures 3 and 4. Figure 3 is a simulation test comparison diagram of the response parameters of the SAW device simulation method provided in this embodiment of the invention; Figure 4 is a simulation test comparison diagram of the response parameters of the SAW dual-mode device using the SAW device simulation method provided in this embodiment of the invention. It can be seen that the simulation results are more accurate when using the optimized SAW device accurate simulation model proposed in this invention.

[0096] The computer device 300 provided in this embodiment of the invention can implement the steps in the simulation method of surface acoustic wave device as described in the above embodiments, and can achieve the same technical effect. Refer to the description in the above embodiments, which will not be repeated here.

[0097] Example 4

[0098] This invention also provides a computer-readable storage medium storing a simulation program for a surface acoustic wave (SAW) device. When the simulation program is executed by a processor, it implements the various processes and steps in the simulation method for the SAW device provided in this invention and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0099] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0100] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0102] The embodiments of the present invention have been described above with reference to the accompanying drawings. The disclosed embodiments are merely preferred embodiments of the present invention. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many equivalent changes in form without departing from the spirit and scope of the claims of the present invention, and all such changes are within the protection scope of the present invention.

Claims

1. A simulation method for a surface acoustic wave device, characterized in that, The simulation method includes the following steps: S1. Obtain the test response parameters of the surface acoustic wave device; wherein the surface acoustic wave device includes multiple resonators with different geometric design parameters; S2. Based on the first preset algorithm, establish an accurate simulation model of the surface acoustic wave device, and use the geometric design parameters corresponding to each resonator as input to the accurate simulation model of the surface acoustic wave device to calculate the simulation response parameters corresponding to each resonator. S3. Establish an error function based on the resonant frequency point and anti-resonant frequency point in the test response parameters and the resonant frequency point and anti-resonant frequency point in the simulation response parameters. Based on the second preset algorithm, iteratively optimize the accurate simulation model of the surface acoustic wave device according to the error function to obtain an optimized accurate simulation model of surface acoustic wave. S4. The geometric design parameters corresponding to each resonator in the surface acoustic wave device to be simulated are used as inputs to the accurate simulation model of the optimized surface acoustic wave device to obtain the simulation results.

2. The simulation method for surface acoustic wave devices as described in claim 1, characterized in that, Step S3 includes the following steps: S31. Calculate the first center point based on the resonant frequency point and anti-resonant frequency point in the test response parameters, and take the area of ​​the first center point within a preset range as the first fundamental frequency region, and extract the extreme point in the first fundamental frequency region as the first feature point. S32. Calculate the second center point based on the resonant frequency point and anti-resonant frequency point in the simulation response parameters, and take the area of ​​the second center point within a preset range as the second fundamental frequency region, and extract the extreme points in the second fundamental frequency region as the second feature points. S33. Calculate the error function based on the first feature point and the second feature point; S34. Define the coefficients corresponding to the material parameters of the resonator as fitting parameters, and iteratively optimize the fitting parameters according to the error function using the second preset algorithm to obtain optimized fitting parameters; wherein, the material parameters include piezoelectric substrate material and metal electrode material; S35. Optimize the surface acoustic wave (SAW) accurate simulation model according to the optimized fitting parameters to obtain the optimized SAW accurate simulation model.

3. The simulation method for surface acoustic wave devices as described in claim 2, characterized in that, In step S2, the simulation response parameters are processed and calculated based on the following relationship: Where c represents the elastic constant, e represents the piezoelectric constant, ρ represents the density, and ε represents the dielectric constant. Let E represent displacement, E represent electric field, and S represent strain.

4. The simulation method for surface acoustic wave devices as described in claim 3, characterized in that, In step S34, the fitting parameters and the material parameters satisfy the following relationship: Where r1 represents the density fitting parameter, r2 represents the elastic constant fitting parameter, r3 represents the piezoelectric constant fitting parameter, r4 represents the dielectric constant fitting parameter, r5 represents the first imaginary number fitting parameter, r6 represents the second imaginary number fitting parameter, and i represents the imaginary number.

5. The simulation method for surface acoustic wave devices as described in claim 4, characterized in that, The error function is defined as Ea, and the error function satisfies the following relationship: Among them, w A w represents the amplitude error weighting coefficient. f The frequency error weighting coefficient is represented by , n represents the number of resonators, A represents the amplitude of the second feature point, A′ represents the amplitude of the first feature point, f represents the frequency of the second feature point, and f′ represents the frequency of the first feature point.

6. The simulation method for surface acoustic wave devices as described in claim 1, characterized in that, The first preset algorithm includes one or more of the following: finite element method, finite element / boundary element method, and hierarchical cascade algorithm.

7. The simulation method for surface acoustic wave devices as described in claim 1, characterized in that, The second preset algorithm includes one or more of the following: genetic algorithm, particle swarm optimization algorithm, and simulated annealing algorithm.

8. A simulation system for a surface acoustic wave device, characterized in that, include: A test module is used to acquire test response parameters of a surface acoustic wave (SAW) device; wherein the SAW device includes multiple resonators with different geometric design parameters. The simulation parameter calculation module is used to establish an accurate simulation model of the surface acoustic wave device based on a first preset algorithm. The geometric design parameters corresponding to each resonator are used as inputs to the accurate simulation model of the surface acoustic wave device to calculate the simulation response parameters corresponding to each resonator. The iterative optimization module is used to establish an error function based on the resonant frequency point and anti-resonant frequency point in the test response parameters and the resonant frequency point and anti-resonant frequency point in the simulation response parameters, and to iteratively optimize the accurate simulation model of the surface acoustic wave device based on the error function using a second preset algorithm to obtain an optimized accurate simulation model of the surface acoustic wave. The simulation module is used to simulate the surface acoustic wave device by taking the geometric design parameters corresponding to each resonator as input to the optimized surface acoustic wave device accurate simulation model, and obtain the simulation results.

9. A computer device, characterized in that, include: The device includes a memory, a processor, and a simulation program for a surface acoustic wave (SAW) device stored in the memory and executable on the processor. When the processor executes the simulation program for the SAW device, it implements the steps in the simulation method for the SAW device as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a simulation program for a surface acoustic wave (SAW) device, which, when executed by a processor, implements the steps of the simulation method for a SAW device as described in any one of claims 1-7.

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