A method for simulating design of a simulated surface acoustic wave filter

Abstract

The application provides a simulation design method applied to a simulation surface acoustic wave filter, comprising the following steps: measuring an actual surface acoustic wave filter model, obtaining Y parameters of the filter, and carrying out de-embedding processing on an admittance characteristic curve; extracting COM model parameters and static capacitance according to the admittance characteristic curve; simulating coupling mode model data according to the extracted COM model parameters, and comparing with measured data; carrying out geometric modeling on the SAW filter, obtaining a multi-physical field model and a grid; adding corresponding boundary conditions and excitation conditions to the geometric model, and generating a grid file; importing the grid file into a spectral element algorithm, and adopting distributed operation to save calculation time; and comparing the simulation curve of the generated COM model with the simulation curve obtained by the spectral element algorithm and the measured data. The simulation time length of the current acoustic wave filter is greatly shortened without affecting the simulation accuracy.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radio frequency chips, and particularly relates to a simulation design method applied to a simulation surface acoustic wave filter. BACKGROUND

[0002] In recent years, with the rapid development of communication technology and the popularity of electronic devices, the performance requirements for radio frequency modules are becoming higher and higher. In the radio frequency module, the surface acoustic wave filter (SAW Filter) as an important filter type is widely used in wireless communication, radar, television and wireless sensor fields. The performance of the SAW filter directly affects the performance and stability of the entire radio frequency module.

[0003] However, due to the complexity of materials and the limitation of manufacturing process, the design and optimization process of the SAW filter is still challenging. The traditional design method usually relies on limited theoretical models, empirical parameters and finite element simulation, but due to the too much computing resources occupied by the multi-physical field simulation software. Therefore, the arrangement design of the new structure more depends on the simulation iteration of the coupled mode algorithm, and finally the optimal geometric parameters of the SAW filter are obtained to simulate the thermal and electrical characteristics of the spectral element method.

[0004] Therefore, it is of great significance to propose a simulation design method applied to a simulation surface acoustic wave filter. SUMMARY

[0005] In order to solve the problems of too large calculation amount and too long calculation time in the existing SAW model simulation, the application provides a simulation design method applied to a simulation surface acoustic wave filter to solve the above technical defect problems.

[0006] In the first aspect, the application provides a simulation design method applied to a simulation surface acoustic wave filter, which comprises the following steps:

[0007] Measuring the actual surface acoustic wave filter model to obtain the Y parameters of the filter, including the open circuit and short circuit Y parameters, and performing de-embedding processing on the admittance characteristic curve;

[0008] According to the admittance characteristic curve, the COM model parameter extraction and the static capacitance extraction are performed;

[0009] According to the extracted COM model parameters, the coupled mode model data simulation is performed, and the simulation results are compared with the measured data;

[0010] Geometric modeling of the SAW filter, including setting the related parameters of the filter model and the calculation region, to obtain the multi-physical field model and the grid thereof;

[0011] Adding corresponding boundary conditions and excitation conditions to the geometric model, and generating a mesh file;

[0012] Importing the mesh file into the spectral element algorithm, and taking distributed operation to save calculation time;

[0013] Comparing the simulation curve of the generated COM model with the simulation curve obtained by the spectral element algorithm and the measured data.

[0014] Preferably, the Y parameters of the filter, including open-circuit and short-circuit Y parameters, comprise Y transistor = ((Y dut -Y open ) -1 -(Y short -Y open ) -1 ) -1 , wherein Y dut is the overall admittance curve of the device, Y open is the interdigital open-circuit admittance curve, and Y short is the interdigital short-circuit admittance curve. Preferably, the COM model parameter extraction according to the admittance characteristic curve comprises: obtaining f r , f ar , f0 and Z imag for the de-embedding processed measured data, f r and f ar are the resonance frequency and anti-resonance frequency of the admittance curve, respectively, f0 is a low frequency point (linear variation region) away from the resonance frequency, used for extracting the static capacitance, and Z imag is the impedance imaginary part corresponding to the point, and then the COM model parameters are calculated by the following formula:

[0015]

[0016]

[0017] wherein η is a representation of the average propagation characteristics of the surface acoustic wave device in the resonance and anti-resonance states, |ε| represents the coupling strength difference parameter in the surface acoustic wave device, v(δ0 represents the propagation proportion or proportion of energy in the device under certain conditions, δ B and δ V represent some special frequency characteristics or intermediate variables of the energy state in the surface acoustic wave device, к represents the energy coupling between different parts of the device, including amplitude and phase information, к B represents the calculation and analysis of the energy coupling strength of the device under certain working conditions and the relationship with other parameters, η BAn enhancement factor representing an equivalent propagation or coupling ability under certain conditions, δ is a comprehensive parameter containing multiple variables related to the operating characteristics of the surface acoustic wave device, ω is an angular frequency, p is an electrode period, v B is a wave velocity, and γ is a Q value loss coefficient.

[0018] Preferably, the extraction of the static capacitance includes: C t is the static capacitance of the IDT device, which is affected by the interdigital logarithm and the numerical aperture, the low frequency point f0 and the corresponding impedance imaginary part Z imag C t 0 can be obtained by the following formula: C t = n * W * ∈, ∈ = p1 * ∈0, Where n is the interdigital logarithm, W is the numerical aperture, and ∈0 is the vacuum permittivity.

[0019] Preferably, the coupling mode model data simulation according to the extracted COM model parameters and the comparison with the measured data include: establishing a coupling mode model for the surface acoustic wave filter, and establishing a differential equation for the acoustic wave propagation phenomenon thereof: The differential equation is expressed as a matrix operation: The P matrix element is expressed as an interdigital resonator, and the expression of each element of the P matrix is as follows:

[0020]

[0021] P 33 = -K1P 31 -K2P 32 exp(jk c L) + 2 (α2K1-α1K2) L + jωC t

[0022] Where D = s cos (sL) + jδ sin (sL0, c 12 = c 21 · = к is a reflection coefficient, α1=α2 * is a coupling coefficient, k c is a wave number, L is the resonator length, generally L = 2n * λ, n is the IDT number; λ = 2p, p is the electrode period; C t is the static capacitance of the IDT device, C t = C0*L*W; P 11 and P 22 represent the reflection coefficient of the interdigital electrode, P 12 = P 21transmission coefficient of the interdigital electrode, P 13 and P 23 mechanical-electrical coupling, which is the transfer function of the electrical quantity to the acoustic quantity at the left and right ends, P 13 and P 23 acoustic-electrical transfer function, P 33 is the electrical quantity, i.e., the input admittance.

[0023] Further preferably, it further comprises: assembling the P matrix of the obtained reflective grating, gap and IDT unit into series and parallel of the filter according to the transfer function of the ABCD matrix, combining the coupling mode method and the transfer matrix to realize simulation of the surface acoustic wave filter, and for a trapezoidal filter, the transfer matrix of the microwave network is as follows: According to the ABCD matrix, the series and parallel resonators are expressed in the following form:

[0024]

[0025] The ABCD matrix is converted into S parameters according to the following equation:

[0026]

[0027] wherein S 11 represents 1-port microwave signal incidence, the ratio of the input intensity to the reflected intensity at the 1-port, and represents the attenuation of the signal, S 21 represents 1-port microwave signal incidence, the ratio of the input intensity at the 2-port to the input intensity at the 1-port, and represents the passing amount of the signal; S 22 represents 2-port microwave signal incidence, the ratio of the input intensity to the reflected intensity at the 2-port, and represents the attenuation of the signal, S 12 represents 2-port microwave signal incidence, the ratio of the input intensity at the 1-port to the input intensity at the 2-port, and represents the passing amount of the signal.

[0028] Preferably, the geometric modeling of the SAW filter comprises setting relevant parameters of the filter model and calculating regions to obtain a multi-physical field model and a grid thereof, including:

[0029] The spectral element method is used to calculate the multi-physical field model to obtain two calculation regions, i.e., an electric field and a displacement field, and obtain a control matrix equation to be solved:

[0030]

[0031] wherein p is the density of the piezoelectric material, ω is the working angular frequency of the filter, u is a displacement vector of deformation, τ is a stress tensor of the piezoelectric material, D is an electric displacement vector of the piezoelectric material, p e is an applied charge density, Ω p is an electric field region, and then Ωs+p is the displacement field region;

[0032] Under the constitutive relation of τ and D, the coupling between the electro-acoustic signals will be realized as follows:

[0033] D = e: ε + ∈ · E, in Ω s

[0034] τ = c: e - e · E, in Ω s+p

[0035] where c is the fourth-order elastic coefficient matrix, e is the third-order piezoelectric coupling coefficient, ∈ is the second-order dielectric constant, ε is the strain tensor, E is the electric field intensity, Ω p is the electric field region, Ω s+p is the displacement field region;

[0036] where the relationship between the electric field and the strain tensor and the electric potential and the displacement field is as follows:

[0037]

[0038]

[0039] where V is the voltage field of the acoustic wave filter, thus realizing the mutual conversion relationship between the electro-acoustic signals, Ω p is the electric field region, Ω s+p is the displacement field region.

[0040] Preferably, the spectral element algorithm solves the acoustic surface wave filter equation as follows:

[0041]

[0042] where Ω is the entire calculation region, Ω p is the electric field region, Ω s+p is the displacement field region, Γ is the boundary surface, n is the normal of the boundary surface, and n · D represents the output voltage; the physical region Ω of the acoustic wave filter is divided into K non-overlapping hexahedral grid elements, and the grid elements in the physical space are mapped to the reference elements through coordinate transformation, and numerical integration, derivation and interpolation are directly performed in the reference elements.

[0043] Preferably, the spectral element algorithm uses Lagrange functions as the basis functions, selects Gauss-Lobatto Legendre (GLL) points as the interpolation points, and constructs the spectral element algorithm for solving the matrix equation of the physical model. In the reference element, the N-order basis function has N+1 GLL points, which are defined as:

[0044]

[0045] Wherein, i is [-1, 1], i=1, 2,..., N+1, is the i-th base function, L N (ζ i ) is the N-order Lagrange polynomial, L' N (ζ i ) is the first derivative of the N-order Lagrange polynomial;

[0046] The assembled matrix A and the excitation source matrix b are obtained, and then the direct method or the iterative method is used to solve the linear equation set (Ax=b), so that the whole model displacement field and voltage field are obtained, the Galerkin method is used to test the weak form, and the reference domain integral is discretized:

[0047]

[0048] Wherein, M ij represents the mass matrix, ü j represents the acceleration of the node, is an external force, is an internal force due to the piezoelectric effect, K ij represents the stiffness matrix, and is the modified stiffness matrix u j represents the displacement of the node, V j represents the node voltage, Q i is a load term related to the electric field.

[0049] Preferably, the simulation curve of the generated COM model is compared with the simulation curve obtained by the spectral element algorithm and the measured data, including: the correctness of the simulation result is verified by comparing the Q value obtained by the Q_bode method, and the Q value formula is as follows:

[0050]

[0051] Wherein, S 11 is the S parameter converted by the admittance curve, and group_delay (S 11 ) is the group delay expression, that is, the gradient operation is performed on the imaginary part of S11.

[0052] Compared with the prior art, the beneficial results of the present application are:

[0053] The present application provides a kind of acoustic wave filter simulation method based on coupled mode method and spectral element method, which can be simulated in the absence of material parameters based on coupled mode method, and the simulation of filter composition is carried out;In addition, in the aspect of spectral element method, grid degradation, matrix equivalence and distributed hosting concept are adopted, which greatly speeds up the problem of long simulation time of current acoustic wave filter without affecting the simulation accuracy, and makes a certain contribution to the thermal-electric simulation analysis of acoustic wave filter. Attached Figure Description

[0054] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and, together with the description, serve to explain the principles of the invention. Other embodiments and many anticipated advantages of the embodiments will be readily recognized as they become better understood through reference to the following detailed description. Elements in the drawings are not necessarily to scale. The same reference numerals refer to corresponding similar parts.

[0055] Figure 1 This is a flowchart illustrating a method for simulation design of a simulated surface acoustic wave filter according to an embodiment of the present invention.

[0056] Figure 2 This is a comparison chart of measured data and de-embedding data from an embodiment of the present invention;

[0057] Figure 3 This is an optical microscopic schematic diagram of an actual model of an embodiment of the present invention;

[0058] Figure 4 This is a schematic diagram illustrating the establishment of a coupled-mode model for a surface acoustic wave filter problem according to an embodiment of the present invention;

[0059] Figure 5 This is a comparison diagram of the COM algorithm and the de-embedding data in an embodiment of the present invention;

[0060] Figure 6 This is a schematic diagram of the topology model of the trapezoidal filter according to an embodiment of the present invention;

[0061] Figure 7 This is a simulation diagram of a surface acoustic wave filter based on the COM algorithm according to an embodiment of the present invention;

[0062] Figure 8 This is a comparison chart of de-embedding data, COM simulation data, and SEM simulation data from an embodiment of the present invention.

[0063] Figure 9 This is a schematic diagram of the SEM geometrically simplified model of the actual model in an embodiment of the present invention;

[0064] Figure 10 This is a mesh partitioning diagram of a single-pair electrode model according to an embodiment of the present invention;

[0065] Figure 11 This is a comparison chart of the de-embedding data and SEM data of the Q_bode value in an embodiment of the present invention. Detailed Implementation

[0066] The application will be described in further detail below with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not intended to limit the scope of the application. In addition, it should be noted that, for the sake of brevity, only portions of the drawings that are necessary for describing the application are shown.

[0067] Figure 1 An embodiment of the application discloses a method for simulating the design of a surface acoustic wave filter, as shown in Figure 1 The method comprises the following steps:

[0068] S1. Measuring an actual surface acoustic wave filter model to obtain Y parameters of the filter, including open-circuit and short-circuit Y parameters, and de-embedding the admittance characteristic curve;

[0069] S2. Extracting COM model parameters and static capacitance according to the admittance characteristic curve;

[0070] S3. Simulating the coupling mode model data according to the extracted COM model parameters, and comparing with the measured data;

[0071] S4. Geometrically modeling the SAW filter, including setting relevant parameters of the filter model and calculating the region to obtain a multi-physical field model and a grid;

[0072] S5. Adding corresponding boundary conditions and excitation conditions to the geometric model, and generating a grid file;

[0073] S6. Importing the grid file into a spectral element algorithm, and adopting distributed operation to save calculation time;

[0074] S7. Comparing the simulation curve of the generated COM model with the simulation curve obtained by the spectral element algorithm and the measured data.

[0075] Specifically, the measured data is de-embedded to obtain the admittance characteristic curve of the interdigital electrode of the surface acoustic wave filter. The measured admittance characteristic curve includes two parts, one part is the admittance characteristic curve of the interdigital electrode itself, and the other part is the parasitic parameter generated by the transmission line at high frequency, which is not embodied in the finite element method simulation and the coupling mode algorithm simulation.

[0076] To solve the de-embedding problem, the overall admittance curve, the interdigital short-circuit admittance curve and the interdigital open-circuit admittance curve are measured first. The admittance expression of the interdigital electrode is (1), as follows:

[0077] Y transistor = ((Y dut -Y open ) -1 -(Yshort - Y open ) -1 ) -1 1

[0078] where Y dut is the overall admittance curve of the device, Y open is the interdigital open admittance curve, and Y short is the interdigital short admittance curve. The comparison results before and after de-embedding are shown in FIG. 8, and the parasitic parameters of the measured data have a greater impact on the admittance data. Figure 2

[0079] In this embodiment, the actual model geometric parameters are as shown in Table 1, and the microscopic optical image is shown in FIG. 9. Figure 3

[0080] Parameter Value Electric period 2.0 [μm] Electrode material Al & Cu / Ti Piezoelectric material [YX42° LiTaO3] Electrode thickness 0.9 * 2.0 * 2.0 [μm] Piezoelectric thickness 2 * 2.0 [μm] Electrode pair number 60 Reflection grating pair number 40 Numerical aperture 40 * 2 * 2 [μm]

[0081] Table 1. Geometric parameters of the filter model measured data

[0082] For the measured data after de-embedding, f r , f ar , f0and Z imag are obtained. f r and f ar are the resonance frequency and anti-resonance frequency of the admittance curve, respectively, f0is a low frequency point (linear variation region) away from the resonance frequency, used to extract the static capacitance, Z imag is the impedance imaginary part corresponding to the point, and then the COM model parameters are calculated by the following formula:

[0083]

[0084]

[0085] where η is a representation of the average propagation characteristics of the surface acoustic wave device in the resonance and anti-resonance states, |ε| represents the coupling strength difference parameter in the surface acoustic wave device, v(δ0represents the propagation proportion or proportion of energy in the device under certain conditions, δ B and δ V represent some special frequency characteristics or intermediate variables of the energy state in the surface acoustic wave device, к represents the energy coupling between different parts of the device, including amplitude and phase information, к B represents the calculation and analysis of the energy coupling strength of the device under certain working conditions and the relationship with other parameters, η B represents an equivalent propagation or coupling enhancement factor under certain conditions, δ is a comprehensive parameter containing multiple variables related to the working characteristics of the surface acoustic wave device, ω is the angular frequency, p is the electrode period, v B ​​γ is the wave velocity, and γ is the Q-value loss coefficient.

[0086] A coupled-mode model is established for the surface acoustic wave filter problem, such as... Figure 4 As shown.

[0087] A differential equation can be established for the above sound wave propagation phenomenon:

[0088]

[0089] Expressing differential equations using matrix operations:

[0090]

[0091] At this point, the P matrix element is represented as an interdigital resonator. Modifying its parameters can feed back the corresponding data onto the admittance curve without needing to model and simulate it again, thus saving a significant amount of computational resources. The expressions for each element of the P matrix are as follows:

[0092]

[0093] P 33 =-K1P 31 -K2P 32 exp(jk c L)+2(α2K1-α1K2)L+jωC t (2.18)

[0094] Where, D=s cos(sL)+jδsin(sL), c 12 =c 21 * =к is the reflection coefficient, α1 = α2 * For coupling coefficients, k c wave number, P 11 and P 22 P represents the reflection coefficient of the interdigitated electrode. 12 =P 21 P represents the transmission coefficient of the interdigitated electrode. 13 and P 23 Representing electromechanical coupling, it is the transfer function between electrical quantities and acoustic quantities at both ends, P 13 and P 23 P represents the transfer function from acoustic quantities to electrical quantities. 33 It is an electrical quantity, namely input admittance; C t The static capacitance of this IDT device is affected by the interdigital logarithm, numerical aperture, and the previously mentioned f0 and Z. imag C can be obtained using the following formula t :

[0095] Ct = n * W * ∈ (2.19)

[0096] = p1 * ∈0 (2.20)

[0097]

[0098] where n is the interdigital logarithm, W is the numerical aperture, ∈0 is the vacuum permittivity. From the above, by analogy, since the IDT of the reflector has only acoustic characteristics, its port expression only needs to be changed to c 12 = c 21 * = 0, thus obtaining the COM algorithm and the comparison of the data after de-embedding as shown in Figure 5 , the curve fitting degree is very high, which proves the accuracy of the method, and based on this method, the simulation design process of the surface acoustic wave filter can be greatly accelerated.

[0099] Based on this, we get the P matrix of the reflector, gap, and IDT unit, and according to the transmission function of the ABCD matrix, we can assemble the series and parallel of the filter. For a ladder filter, we generally use the topology model as Figure 6 .

[0100] According to the microwave network transmission matrix:

[0101]

[0102] According to the ABCD matrix, we express the series and parallel resonators as follows:

[0103]

[0104] And the conversion of the ABCD matrix to the S parameter is according to the following equation:

[0105]

[0106] where S 11 indicates that the 1-port microwave signal is incident, the ratio of the 1-port input intensity to the reflected intensity, and the signal attenuation amount, S 21 indicates that the 1-port microwave signal is incident, the ratio of the 2-port input intensity to the 1-port input intensity, and the signal passing amount; S 22 indicates that the 2-port microwave signal is incident, the ratio of the 2-port input intensity to the reflected intensity, and the signal attenuation amount, S 12 indicates that the 2-port microwave signal is incident, the ratio of the 1-port input intensity to the 2-port input intensity, and the signal passing amount.

[0107] We combine the coupled mode method and the transmission matrix to realize the simulation of the surface acoustic wave filter, such asFigure 7 and Figure 8 as shown.

[0108] Further, the embodiment of the present application also proposes a method for solving piezoelectric multi-physical field coupling equations based on the spectral element method, comprising:

[0109] Firstly, a SAW filter is geometrically modeled, including setting relevant parameters of the filter model and calculating regions, so as to obtain a multi-physical field model and a grid, as shown in Figure 9 and Figure 10 as shown, Figure 9 which is a SEM simplified model diagram of an actual model of the embodiment of the present application; Figure 10 which is a grid division diagram of a single pair electrode model of the embodiment of the present application.

[0110] The spectral element method is used to calculate the multi-physical field model, so that two calculation regions, i.e. an electric field and a displacement field, can be obtained, and a control matrix equation to be solved is obtained:

[0111]

[0112]

[0113] wherein, ρ is the density of the piezoelectric material, ω is the working angular frequency of the filter, u is a displacement vector of deformation, τ is a stress tensor of the piezoelectric material, D is an electric displacement vector of the piezoelectric material, ρ e is an applied charge density, Ω p is an electric field region, and then Ω s+p is a displacement field region.

[0114] In addition to solving the matrix relationship of the two physical fields, the coupling relationship between the two physical fields is also considered, and under the constitutive relationship of τ and D, the coupling between the electric and acoustic signals will be realized as follows:

[0115] D = e: ε + ∈ · E, in Ω s (4.3)

[0116] τ = c: e - e · E, in Ω s+p (4.4)

[0117] wherein, c is a fourth-order elastic coefficient matrix, e is a third-order piezoelectric coupling coefficient, ∈ is a second-order dielectric constant, ε is a strain tensor, E is an electric field intensity, Ω p is an electric field region, and then Ω s+p is a displacement field region.

[0118] In the above two equations, the first equation is the piezoelectric effect that the stress field applied to the object will generate an electrical signal; the second equation is the inverse piezoelectric effect that the electric field applied to the piezoelectric material will generate an acoustic wave signal. Among them, the relationship between the electric field and the strain tensor and the electric potential and the displacement field is as follows:

[0119]

[0120] Where V is the voltage field of the acoustic wave filter, thus realizing the mutual conversion relationship between the electrical and acoustic signals, Ω p is the electric field region, and then Ω s+p is the displacement field region.

[0121] The spectral element method for solving the acoustic surface wave filter equation is based on the weighted residual method to calculate the acoustic surface wave filter equation, so the original equation needs to be rewritten as follows:

[0122]

[0123] Where Ω p is the electric field region, and then Ω s+p is the displacement field region, Γ is the boundary surface, and n is the boundary surface normal. The physical region Ω of the acoustic wave filter is divided into K non-overlapping hexahedral grid elements. Then the grid elements on the physical space are mapped to the reference elements through coordinate transformation, and numerical integration, derivation and interpolation can be directly performed in the reference elements.

[0124] The spectral element method of the application uses Lagrange functions as basis functions, and selects Gauss-LobattoLegendre (GLL) points as interpolation points. In the reference element, the N-order basis function has N+1 GLL points, which is defined as:

[0125]

[0126] Where i∈[-1,1], i=1,2,…,N+1, is the i-th basis function, L N (ζ i ) is the N-order Lagrange polynomial, and L' N (ζ i ) is the first derivative of the N-order Lagrange polynomial.

[0127] The above has constructed the matrix equation for solving the physical model by the spectral element method. Based on the actual application requirements, initial conditions need to be added to the matrix equation, which is commonly known as boundary conditions. For the electrically coupled model of the acoustic wave filter, its boundary conditions can be divided into solid mechanics boundary conditions and electrostatic field boundary conditions, at this time the corresponding boundary conditions need to be added according to the actual model.

[0128] Get assembled matrix A and excitation source matrix b, and then solve the linear equations (Ax = b) using direct method or iterative method, that is, the entire model displacement field and voltage field can be obtained. The weak form is tested by Galerkin method, and the reference domain is integrated and discrete:

[0129]

[0130] Where, M ij represents the mass matrix, ü j represents the acceleration of the node, is the external force, is the internal force due to piezoelectric effect, K ij represents the stiffness matrix, and is the modified, stiffness matrix u j represents the displacement of the node, V j represents the node voltage, Q i is the load term related to the electric field.

[0131] Distributed computing is performed on the above obtained linear equations, which is divided into n tasks according to the frequency point, using distributed register technology, the total equation solving is divided into n task nodes and deployed on the server, realizing the integration of computing resources, using computing memory to exchange computing time, greatly shortening the simulation time of wideband SAW resonator, and the calculation time is also greatly reduced when the order is the same. The actual resonator model parameters are shown in Table 2.1, and the simulation data analysis is shown in Table 2.2:

[0132]

[0133] Table 2.1 Geometric parameters of spectral element method simulation model

[0134]

[0135]

[0136] Table 2.2 Comparison of spectral element method acceleration

[0137] From the chart data analysis, it is found that the spectral element algorithm greatly shortens the simulation time while maintaining high simulation accuracy, and the multi-core distributed computing further speeds up the simulation time.

[0138] Q value is an important characteristic of a resonator, and finally the Q_bode method is used to calculate Q value to verify the correctness of the simulation results. The results are shown in Figure 11 , the spectral element method not only speeds up the simulation speed, but also gets a curve that is quite consistent with the measured curve. The Q value formula is as follows:

[0139]

[0140] wherein S 11 is the S parameter converted from admittance curve, group_delay(S 11 ) is its group delay expression, that is, the gradient operation is performed on the imaginary part of S11.

[0141] To sum up, the application proposes a sound wave filter simulation method based on the coupling mode method and the spectral element method, which can simulate the actual model and simulate the filter composition without material parameters based on the coupling mode method. In addition, in the aspect of the spectral element method, the grid degradation, matrix equivalence and distributed register concept are adopted, which greatly speeds up the long simulation time of the current sound wave filter without affecting the simulation accuracy, and makes a certain contribution to the thermal and electrical simulation analysis of the sound wave filter.

[0142] The above description is merely preferred embodiments of the application and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with the technical features disclosed in the application (but not limited to) having similar functions to form a technical solution.

Claims

1. A method for simulation design of surface acoustic wave (SAW) filters, characterized in that, The method includes the following steps: The actual surface acoustic wave filter model is measured to obtain the Y parameters of the filter, including the Y parameters of open circuit and short circuit, and the admittance characteristic curve is de-embedded. Based on the admittance characteristic curve, COM model parameters and static capacitance are extracted; including: obtaining f from the measured data after de-embedding processing. r f ar f0 and Z imag f r and f ar These are the resonant and anti-resonant frequencies of the admittance curve, respectively. f0 represents a low-frequency point far from the resonant frequency, a linearly varying region used to extract static capacitance. Z imag The imaginary part of the impedance at that point is given, and then the COM model parameters are calculated using the following formula: Where η is a characterizing quantity of the average propagation characteristics of the surface acoustic wave device in resonant and anti-resonant states, |ε| represents the coupling strength difference parameter in the surface acoustic wave device, and v(δ) represents the proportion or percentage of energy propagation in the device under specific conditions. B and δ V к represents intermediate variables that indicate specific frequency characteristics or energy states within a surface acoustic wave (SAW) device. к represents the energy coupling between different parts of the device, including amplitude and phase information. B η represents the energy coupling strength of a device under specific operating conditions and its relationship with other parameters. B δ represents an enhancement factor for equivalent propagation or coupling capability under specific conditions, where δ is a comprehensive parameter containing multiple variables related to the operating characteristics of the surface acoustic wave device, ω is the angular frequency, p is the electrode period, and v is the frequency. B Where γ is the wave velocity and γ is the Q-value loss coefficient; Based on the extracted COM model parameters, coupled-mode model data simulation is performed and compared with measured data; this includes: establishing a coupled-mode model for the surface acoustic wave filter and establishing differential equations for its acoustic wave propagation phenomena. Expressing differential equations using matrix operations: A unit cell of the P matrix is ​​represented as an interdigital resonator. The expressions for each element of the P matrix are as follows: P 33 =-K1P 31 -K2P 32 exp(jk c L)+2(α2K1-α1K2)L+jωC t Where, D = s cos(sL) + jδsin(sL), c 12 =c 21 * =к is the reflection coefficient, α1=α2 * k is the coupling coefficient. c For wave number, L is the resonator length, L = 2n * λ, where n is the number of IDTs; λ = 2p, where p is the electrode period; C t C is the static capacitance of the IDT device. t =C0*L*W;P 11 and P 22 P represents the reflection coefficient of the interdigitated electrode. 12 =P 21 P represents the transmission coefficient of the interdigitated electrode. 13 and P 23 Representing electromechanical coupling, it is the transfer function between electrical quantities and acoustic quantities at both ends, P 13 and P 23 P represents the transfer function from acoustic quantities to electrical quantities. 33 It is an electrical quantity, namely input admittance; Geometric modeling of the SAW filter is performed, including setting the relevant parameters of the filter model and the computational domain, to obtain the multiphysics model and its mesh. Add corresponding boundary conditions and excitation conditions to the geometric model, and generate a mesh file; The grid file is imported into the spectral element algorithm, and distributed computing is used to save computation time; The simulation curves of the generated COM model are compared with the simulation curves and measured data obtained by the spectral element algorithm.

2. The simulation design method for simulated surface acoustic wave filters according to claim 1, characterized in that, The acquisition of the Y parameters of the filter, including the Y parameters for open circuit and short circuit, includes: Y transistor = ((Y dut - Y open ) -1 - (Y short - Y open ) -1 ) -1 where Y dut is the overall admittance curve of the device, Y open is the interdigital open admittance curve, and Y short is the interdigital short admittance curve.

3. The simulation design method for simulated surface acoustic wave filters according to claim 1, characterized in that, The extraction of the static capacitance includes: C t The static capacitance of the IDT device is affected by the interdigital pair number and the numerical aperture, the low frequency point f0 and the corresponding impedance imaginary part Z imag The C t can be obtained by the following formula: C t = n * W * ∈ ∈＝p1*∈0 Where n is the interdigitated logarithm, W is the numerical aperture, and ∈0 is the vacuum permittivity.

4. The simulation design method for simulated surface acoustic wave filters according to claim 1, characterized in that, Also includes: The obtained P-matrices of the reflector grating, gap, and IDT elements are assembled into a series-parallel filter based on the transfer function of the ABCD matrix. The coupled-mode method and the transfer matrix are combined to simulate the surface acoustic wave filter. For a trapezoidal filter, the matrix based on the microwave network transmission is as follows: Based on the ABCD matrix, series and parallel resonators can be represented in the following forms: Transform the ABCD matrix into S-parameters according to the following equation: wherein S 11 represents 1-port microwave signal incidence, the ratio of 1-port input intensity to reflection intensity, representing the attenuation of the signal, S 21 represents 1-port microwave signal incidence, the ratio of 2-port input intensity to 1-port input intensity, representing the passing amount of the signal; S 22 represents 2-port microwave signal incidence, the ratio of 2-port input intensity to reflection intensity, representing the attenuation of the signal; S 12 represents 2-port microwave signal incidence, the ratio of 1-port input intensity to 2-port input intensity, representing the passing amount of the signal.

5. The simulation design method for simulated surface acoustic wave filters according to claim 1, characterized in that, The geometric modeling of the SAW filter includes setting relevant parameters of the filter model and the computational domain to obtain a multiphysics model and its mesh, including: The spectral element method was used to calculate the multiphysics model, resulting in two computational domains: the electric field and the displacement field. The resulting control matrix equations were then obtained. where p is the density of the piezoelectric material, w is the operating angular frequency of the filter, u is the displacement vector where deformation occurs, T is the stress tensor of the piezoelectric material, D is the electric displacement vector of the piezoelectric material, p e is the applied charge density, Ω p is the electric field region, then Ω s+p is the displacement field region; Under the constitutive relations of τ and D, the electroacoustic signals will achieve the following coupling: D = e : ε + ∈ · E, in Ω s τ = c : e - e · E, in Ω s+p where c is the fourth order elastic coefficient matrix, e is the third order piezoelectric coupling coefficient, ∈ is the second order dielectric constant, ε is the strain tensor, E is the electric field intensity, Ω p is the electric field region, Ω s+p is the displacement field region; The relationships between the electric field and strain tensor and the electric potential and displacement field are as follows: where V is the voltage field of the acoustic wave filter, thus realizing the relationship of mutual conversion between the electrical and acoustic signals, Ω p is the electrical field region, Ω s+p is the displacement field region.

6. The simulation design method for simulated surface acoustic wave filters according to claim 1, characterized in that, The spectral element algorithm is used to solve the surface acoustic wave filter equations as follows: Where Ω represents the entire computational domain, Ω p For the electric field region, Ω s+p Let Γ be the displacement field region, Γ be the boundary surface, n be the boundary surface normal, and n·D represent the output voltage. The physical region Ω of the acoustic filter is divided into K non-overlapping hexahedral mesh elements. The mesh elements in the physical space are then mapped to the reference element through coordinate transformation. Numerical integration, differentiation, and interpolation are then performed directly in the reference element.

7. The simulation design method for simulated surface acoustic wave filters according to claim 1, characterized in that, The spectral element algorithm uses Lagrange functions as basis functions and selects Gauss-LobattoLegendre (GLL) points as interpolation points to construct the solution matrix equations for the physical model. In the reference element, the Nth-order basis function has N+1 GLL points and is defined as follows: Where ζ∈[-1,1],i=1,2,…,N+1, It is the i-th basis function, L N (ζ i ) is an Nth-order Lagrange polynomial, L′ N (ζ i ) is the first derivative of an Nth-order Lagrange polynomial; The assembled matrix A and the excitation source matrix b are obtained. Then, the linear equation system Ax = b is solved using the direct method or iterative method to obtain the displacement field and voltage field of the entire model. For the weak form, the Galerkin method is used for testing, and the model is discretized by integration over the reference domain. Among them, M ij Represents the mass matrix, Represents the acceleration of the node. For external force, K is the internal force generated due to the piezoelectric effect. ij This represents the stiffness matrix, while The corrected stiffness matrix u j V represents the displacement of the node. j Q represents the node voltage. i It is a load term related to the electric field.

8. The simulation design method for simulated surface acoustic wave filters according to claim 1, characterized in that, The simulation curves of the generated COM model are compared with the simulation curves obtained by the spectral element algorithm and the measured data, including: calculating the Q value using the Q-bode method to verify the correctness of the simulation results. The Q value formula is as follows: Among them, S 11 For the S-parameters converted from admittance curves, group_delay(S 11 ) is its group delay expression, which is the gradient operation of the imaginary part of S11.

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