Piezoelectric thin film acoustic sensor packaging design method, device and system

Abstract

The application discloses a piezoelectric thin film acoustic sensor packaging design method, device and system, the piezoelectric thin film acoustic sensor packaging design method includes obtaining a pre-constructed multi-physical field coupling optimization model, the objective function of the multi-physical field coupling optimization model is constructed to maximize the sound pressure sensitivity, minimize the frequency offset and minimize the target function of the parasitic resonance amplitude; a Bayesian global optimization algorithm is used to solve the multi-physical field coupling optimization model to obtain the piezoelectric thin film acoustic sensor packaging parameters, the CNN model is used as the proxy optimization model in the Bayesian global optimization algorithm, which is used to fit the objective function of the multi-physical field coupling optimization model, and in the solving process, the weights of different optimization sub-targets are dynamically adjusted according to the environmental parameters, and the optimization sub-targets include maximizing the sound pressure sensitivity, minimizing the frequency offset and minimizing the parasitic resonance amplitude. The application can improve the anti-interference and stability of the piezoelectric thin film acoustic sensor.

Description

Technical Field

[0001] This invention belongs to the field of acoustic sensing technology, specifically relating to a piezoelectric thin film acoustic sensor packaging design method, device, and system. Background Technology

[0002] With the continuous development of industrial automation and intelligence, fault detection and health monitoring of electrical equipment are becoming increasingly important. Especially in fields such as high-voltage electrical equipment, substations, power distribution networks, and motor systems, mechanical vibration signals and partial discharge signals are crucial parameters reflecting the operating status and potential faults of equipment. Timely and accurate monitoring of these signals can effectively prevent equipment failures, improve the reliability of equipment operation, and reduce the risk of accidents.

[0003] Traditional vibration sensors typically employ piezoelectric sensors, capacitive sensors, or accelerometers. However, these sensors are often large and heavy, sensitive to environmental interference, and prone to performance degradation in high-temperature, high-humidity, or strong electromagnetic interference environments. Partial discharge detection usually relies on traditional electrical or ultrasonic sensors, which are difficult to integrate tightly with equipment, and the accuracy and stability of the measurement signals are poor.

[0004] Micro-Electro-Mechanical Systems (MEMS) technology, due to its miniaturization, high precision, low power consumption, and high integration, has gradually become a key technology in the sensor field. In particular, MEMS acoustic sensors can accurately capture weak mechanical vibrations and electrical discharge signals by responding to vibrations or sound waves through sensitive diaphragms. Therefore, using MEMS acoustic sensors for monitoring mechanical vibrations and partial discharge signals in electrical equipment has great application potential.

[0005] However, because MEMS acoustic sensors are very delicate and easily affected by the external environment, especially in the complex environment of high-voltage electrical equipment, how to effectively package MEMS acoustic sensors to improve their anti-interference and stability has become a technical problem that urgently needs to be solved. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a piezoelectric thin-film acoustic sensor packaging design method, device, and system, which can improve the anti-interference and stability of piezoelectric thin-film acoustic sensors.

[0007] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution:

[0008] In a first aspect, the present invention provides a piezoelectric thin-film acoustic sensor packaging design method, comprising:

[0009] A pre-constructed multiphysics coupling optimization model is obtained, wherein the objective function of the multiphysics coupling optimization model is constructed with the objectives of maximizing sound pressure sensitivity, minimizing frequency shift, and minimizing parasitic resonance amplitude.

[0010] The Bayesian global optimization algorithm is used to solve the multiphysics coupling optimization model to obtain the piezoelectric thin film acoustic sensor packaging parameters. The Bayesian global optimization algorithm uses a CNN model as a surrogate optimization model to fit the objective function. During the solution process, the weights of different optimization sub-objectives are dynamically adjusted according to environmental parameters. The optimization sub-objectives include maximizing sound pressure sensitivity, minimizing frequency offset, and minimizing parasitic resonance amplitude.

[0011] In conjunction with the first aspect, optionally, the mathematical expression of the objective function of the multiphysics coupling optimization model is:

[0012] ,

[0013] In the formula, The objective function of the multiphysics coupling optimization model is... For sound pressure sensitivity, For frequency offset, For parasitic resonance amplitude, , , All are weighting coefficients; the sound pressure sensitivity, frequency shift, and parasitic resonance amplitude are all calculated based on the elastic stiffness, piezoelectric constant, dielectric constant, structural parameters, and constitutive equation of the piezoelectric film.

[0014] The constraints of the multiphysics coupling optimization model include:

[0015] piezoelectric film thickness ,in, This represents the minimum thickness of the piezoelectric film. This represents the maximum value of the piezoelectric film thickness;

[0016] cavity depth ,in, The operating wavelength;

[0017] piezoelectric thin film radius , in, This is the minimum value of the piezoelectric film radius. This represents the maximum radius of the piezoelectric thin film.

[0018] resonant frequency ,in, The center frequency of the target frequency band, The target bandwidth.

[0019] In conjunction with the first aspect, optionally, the input parameters of the CNN model include the elastic stiffness, piezoelectric constant, dielectric constant, and structural parameters of the piezoelectric film, and its output parameters are the objective function. The true value of is given by the loss function:

[0020] ,

[0021] In the formula, The loss function; This represents the total number of data collection points. Each data collection point includes the elastic stiffness, piezoelectric constant, dielectric constant, and structural parameters of the piezoelectric film. For the first One collection point, The output value of the CNN model. For the true value, For regularization terms, is the coefficient of the regularization term.

[0022] In conjunction with the first aspect, optionally, the step of employing a Bayesian global optimization algorithm to solve the multiphysics coupling optimization model to obtain the piezoelectric thin-film acoustic sensor packaging parameters specifically includes:

[0023] Based on the current agent model, find the parameter points of the optimal acquisition function, whereby the parameter points include... , , Elastic stiffness, piezoelectric constant, dielectric constant, and structural parameters of piezoelectric films;

[0024] Based on this parameter point, the value of the objective function of the multiphysics coupling optimization model is calculated;

[0025] parameters , , The values ​​of the elastic stiffness, piezoelectric constant, dielectric constant, structural parameters, and objective function of the piezoelectric film are used as new parameter points and added to the observation dataset to obtain the updated observation dataset.

[0026] The surrogate model is retrained and updated using the updated observation dataset until the objective function of the multiphysics coupled optimization model converges.

[0027] In conjunction with the first aspect, optionally, the step of dynamically adjusting the weights of different optimization sub-objectives based on environmental parameters specifically includes:

[0028] Weighting based on temperature parameters The adjustment is made using the following formula:

[0029] ,

[0030] ,

[0031] In the formula, The adjusted weights, This is the absolute difference between the current temperature and the reference temperature. The current temperature. For reference temperature, As the initial weights, Temperature threshold;

[0032] Weights based on vibration amplitude parameters The adjustment is made using the following formula:

[0033] ,

[0034] In the formula, The adjusted weights, As the initial weights, The vibration amplitude detected by the vibration sensor. This is the vibration amplitude threshold.

[0035] In conjunction with the first aspect, optionally, the step of dynamically adjusting the weights of different optimization sub-objectives based on environmental parameters further includes:

[0036] Weighting based on temperature parameters and vibration amplitude parameters The adjustment is made using the following formula:

[0037] .

[0038] In conjunction with the first aspect, optionally, after the step of solving the multiphysics coupling optimization model to obtain the piezoelectric thin-film acoustic sensor packaging parameters, the method further includes:

[0039] Obtain the resonant frequency shift detected by the vibration sensor;

[0040] Based on the resonant frequency shift fed back by the vibration sensor, the bias voltage is adjusted using a PID algorithm. The adjustment of bias voltage This is used to control the deformation of the piezoelectric film so that the absolute value of the frequency offset is less than a set frequency offset threshold.

[0041] In conjunction with the first aspect, optionally, the PID algorithm calculates the adjusted bias voltage using the following formula. :

[0042] ,

[0043] In the formula, , , These are the proportional gain, integral gain, and differential gain, respectively. express time, This is the resonant frequency shift; express At time t, is the integral operator; express The resonant frequency shift at a given moment.

[0044] In a second aspect, the present invention provides a piezoelectric thin-film acoustic sensor packaging design device, comprising:

[0045] The multiphysics coupling optimization model acquisition module is used to acquire a pre-constructed multiphysics coupling optimization model. The objective function of the multiphysics coupling optimization model is constructed with the objectives of maximizing sound pressure sensitivity, minimizing frequency shift, and minimizing parasitic resonance amplitude.

[0046] The encapsulation parameter design module is used to solve the multiphysics coupling optimization model using a Bayesian global optimization algorithm to obtain the encapsulation parameters of the piezoelectric thin film acoustic sensor. The Bayesian global optimization algorithm uses a CNN model as a surrogate optimization model to fit the objective function. During the solution process, the weights of different optimization sub-objectives are dynamically adjusted according to environmental parameters. The optimization sub-objectives include maximizing sound pressure sensitivity, minimizing frequency offset, and minimizing parasitic resonance amplitude.

[0047] Thirdly, the present invention provides a piezoelectric thin film acoustic sensor packaging design system, including a storage medium and a processor;

[0048] The storage medium is used to store instructions;

[0049] The processor is configured to operate according to the instructions to perform the method according to any one of the first aspects.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] This invention constructs a multi-physics coupling optimization model with the objectives of maximizing sound pressure sensitivity, minimizing frequency shift, and minimizing parasitic resonance amplitude. In the solution process, a CNN model is used as a surrogate optimization model for the Bayesian optimization algorithm, which breaks through the limitations of traditional single-objective optimization and effectively improves the anti-interference and stability of MEMS piezoelectric thin film acoustic sensors.

[0052] This invention also proposes to dynamically adjust the weights of the multiphysics coupling optimization model of the target based on the environment, so as to adapt to the influence of the environment and significantly improve acoustic performance and environmental adaptability.

[0053] To further reduce environmental interference, this invention employs active piezoelectric tuning for compensation improvement. As a feedback signal, it achieves weight adjustment in constructing a multiphysics coupling optimization model, ultimately reducing environmental impact. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. 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 effort, wherein:

[0055] Figure 1 This is a schematic flowchart illustrating a piezoelectric thin-film acoustic sensor packaging design method according to an embodiment of the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0057] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0058] Example 1

[0059] This invention provides a piezoelectric thin-film acoustic sensor packaging design method, such as... Figure 1 As shown, it includes the following steps:

[0060] (1) Obtain a pre-constructed multi-physics coupling optimization model, wherein the objective function of the multi-physics coupling optimization model is constructed with the objectives of maximizing sound pressure sensitivity, minimizing frequency shift and minimizing parasitic resonance amplitude;

[0061] (2) The Bayesian global optimization algorithm is used to solve the multi-physics coupling optimization model to obtain the piezoelectric thin film acoustic sensor packaging parameters. The Bayesian global optimization algorithm uses a CNN model as a surrogate optimization model to fit the objective function. During the solution process, the weights of different optimization sub-objectives are dynamically adjusted according to the environmental parameters. The optimization sub-objectives include maximizing sound pressure sensitivity, minimizing frequency shift and minimizing parasitic resonance amplitude.

[0062] In the above scheme, a multi-physics coupling optimization model is constructed with the objectives of maximizing sound pressure sensitivity, minimizing frequency shift, and minimizing parasitic resonance amplitude. In the solution process, a CNN model is used as a surrogate optimization model for the Bayesian optimization algorithm, which breaks through the limitations of traditional single-objective optimization and effectively improves the anti-interference and stability of MEMS piezoelectric thin film acoustic sensors.

[0063] In one specific embodiment of the present invention, the mathematical expression of the objective function of the multiphysics coupling optimization model is:

[0064] ,

[0065] In the formula, The objective function of the multiphysics coupling optimization model is... For sound pressure sensitivity, For frequency offset, For parasitic resonance amplitude, , , All are weighting coefficients; the sound pressure sensitivity, frequency shift, and parasitic resonance amplitude are all calculated based on the elastic stiffness, piezoelectric constant, dielectric constant, structural parameters, and constitutive equation of the piezoelectric film.

[0066] The constraints of the multiphysics coupling optimization model include:

[0067] piezoelectric film thickness ,in, This represents the minimum thickness of the piezoelectric film. This represents the maximum value of the piezoelectric film thickness;

[0068] cavity depth ,in, The operating wavelength;

[0069] piezoelectric thin film radius , in, This is the minimum value of the piezoelectric film radius. This represents the maximum radius of the piezoelectric thin film.

[0070] resonant frequency ,in, The center frequency of the target frequency band, The target bandwidth.

[0071] In the above scheme, the objective function and constraints of the multiphysics coupling optimization model are specifically defined to facilitate later implementation. The constitutive equation of piezoelectric thin films is the core mathematical model describing the electromechanical coupling behavior of piezoelectric materials, revealing the bidirectional relationship between mechanical stress / strain and electric field / electric displacement. For MEMS piezoelectric thin films (such as aluminum nitride AlN or lead zirconate titanate PZT), the constitutive equation is the theoretical basis for designing sensor packaging and optimizing sensitivity and stability.

[0072] The piezoelectric effect includes the direct piezoelectric effect (mechanical energy → electrical energy) and the inverse piezoelectric effect (electrical energy → mechanical energy), and its constitutive equation can be expressed as:

[0073] Linear piezoelectric equation (tensor form):

[0074] ,

[0075] in:

[0076] Stress tensor (Pa) describes the stress per unit area within a material.

[0077] : Strain tensor, which describes the degree of material deformation (dimensionless).

[0078] Electric field strength (V / m) is generated by external voltage or polarization.

[0079] Electric displacement (C / m²) reflects the charge distribution under the combined effect of electric field and material polarization.

[0080] The elastic stiffness matrix (Pa) represents the stiffness characteristics of a material under a constant electric field.

[0081] : Piezoelectric stress constant (C / m²), which describes the stress caused by a unit electric field or the electric displacement caused by a unit strain.

[0082] : Dielectric constant matrix (F / m), representing the dielectric properties under constant strain.

[0083] , , , This is the direction index, with values ​​1, 2, and 3 corresponding to... , , In the linear piezoelectric equations, their order and combination explicitly describe the directional relationship of the electromechanical coupling. Simplification reduces the number of non-zero components and lowers computational complexity.

[0084] Due to the complexity of computation in tensor form, a simplified matrix representation (Voigt notation) is used to compress the fourth-order tensor into a matrix:

[0085] Stress and strain vectors: ,correspond .

[0086] The piezoelectric equation simplifies to:

[0087] ,

[0088] in:

[0089] : 6×6 elastic stiffness matrix (symmetric);

[0090] : 3×6 piezoelectric constant matrix;

[0091] : 3×3 dielectric constant matrix (symmetric)

[0092] : Unit diagonal matrix.

[0093] : 6×1 strain vector, reflecting the degree of material deformation, dimensionless.

[0094] For AlN piezoelectric thin films, the piezoelectric constant matrix (unit: C / m²) is as follows:

[0095] ,

[0096] Typical value , , .

[0097] The specific methods for determining sound pressure sensitivity, frequency shift, and parasitic resonance amplitude include:

[0098] Perform multiphysics coupling (parameter configuration phase):

[0099] In COMSOL, constitutive equations are embedded through the following steps:

[0100] Define material properties: Input , , matrix.

[0101] Boundary condition settings:

[0102] Mechanical boundary: fixed support beam end point (displacement = 0).

[0103] Electrical boundary: Applying voltage or short-circuit condition (E=0).

[0104] Multiphysics coupling results:

[0105] 1. The acoustic module in COMSOL calculates the sound pressure distribution and then uses this distribution to calculate the sound pressure sensitivity. The resonant frequency shift was obtained through the acoustic frequency simulation module. .

[0106] 2. Solve the strain field ϵ using the solid mechanics module in COMSOL.

[0107] 3. The electrostatics module in COMSOL calculates the electric field E and electric displacement D, thereby obtaining the parasitic resonance amplitude. .

[0108] In one specific embodiment of the present invention, the input parameters of the CNN model include the elastic stiffness, piezoelectric constant, dielectric constant, and structural parameters of the piezoelectric thin film, and its output parameter is the objective function. The true value of is given by the loss function:

[0109] ,

[0110] In the formula, The loss function; This represents the total number of data collection points. Each data collection point includes the elastic stiffness, piezoelectric constant, dielectric constant, and structural parameters of the piezoelectric film. For the first One collection point, The output value of the CNN model. For the true value, For regularization terms, is the coefficient of the regularization term.

[0111] Bayesian optimization is an efficient strategy for global optimization of black-box functions. Its core idea is to balance exploration (unknown regions) and development (known optimal regions) through iterative optimization of the surrogate model and the acquisition function. CNN models are a method that uses deep learning to replace traditional surrogate models (such as Gaussian processes), suitable for handling high-dimensional parameter spaces and complex nonlinear relationships. The network structure of the CNN model includes convolutional layers, pooling layers, and fully connected layers; the convolutional layers are used to extract local features; the pooling layers are used to reduce dimensionality and enhance robustness; the fully connected layers are used to map features to predicted values ​​of the objective function. The network structure of the CNN model in this embodiment is existing technology. In a specific implementation of this embodiment, the Bayesian global optimization algorithm is used to solve the multi-physics coupling optimization model to obtain the piezoelectric thin-film acoustic sensor packaging parameters, specifically including:

[0112] Based on the current agent model, find the parameter points of the optimal acquisition function, whereby the parameter points include... , , Elastic stiffness, piezoelectric constant, dielectric constant, and structural parameters of piezoelectric films;

[0113] Based on this parameter point, the value of the objective function of the multiphysics coupling optimization model is calculated;

[0114] parameters , , The elastic stiffness, piezoelectric constant, dielectric constant, structural parameters, and objective function values ​​of the piezoelectric thin film are used as new parameter points and added to the observation dataset to obtain the updated observation dataset.

[0115] The surrogate model is retrained and updated using the updated observation dataset until the objective function of the multiphysics coupled optimization model converges.

[0116] In one specific embodiment of the present invention, the step of dynamically adjusting the weights of different optimization sub-objectives based on environmental parameters specifically includes:

[0117] Weighting based on temperature parameters The adjustment is made using the following formula:

[0118] ,

[0119] ,

[0120] In the formula, The adjusted weights, This is the absolute difference between the current temperature and the reference temperature. The current temperature. For reference temperature, As the initial weights, Temperature threshold;

[0121] Weights based on vibration amplitude parameters The adjustment is made using the following formula:

[0122] ,

[0123] In the formula, The adjusted weights, As the initial weights, The vibration amplitude detected by the vibration sensor. This is the vibration amplitude threshold.

[0124] The method of dynamically adjusting the weights of different optimization sub-objectives based on environmental parameters also includes:

[0125] Weighting based on temperature parameters and vibration amplitude parameters The adjustment is made using the following formula:

[0126] .

[0127] In the above scheme, the weights of each optimization objective are dynamically adjusted based on real-time environmental feedback, aiming to prioritize the suppression of the most serious performance degradation factors (such as thermal drift caused by high temperature or resonance caused by strong vibration).

[0128] Piezoelectric deformation equation:

[0129] ,

[0130] in, : Strain generated by the piezoelectric thin film (dimensionless); piezoelectric constant The thickness of the piezoelectric thin film. : The applied bias voltage.

[0131] Cavity depth adjustment amount:

[0132] ,

[0133] in, The effective length of the piezoelectric film. The cavity depth adjustment amount can compensate for the cavity resonant frequency shift caused by environmental vibration. Therefore, in a specific embodiment of the present invention, after solving the multi-physics coupling optimization model to obtain the piezoelectric thin-film acoustic sensor packaging parameters, the method further includes:

[0134] Obtain the resonant frequency shift detected by the vibration sensor;

[0135] Based on the resonant frequency shift fed back by the vibration sensor, the bias voltage is adjusted using a PID algorithm. The adjustment of bias voltage This is used to control the deformation of the piezoelectric thin film so that the absolute value of the frequency offset is less than a set frequency offset threshold. In specific applications, the set frequency offset threshold can be set to 0.

[0136] In one specific embodiment of the present invention, the PID algorithm calculates the adjusted bias voltage using the following formula. :

[0137] ,

[0138] In the formula, , , These are the proportional gain, integral gain, and differential gain, respectively. express time, This is the resonant frequency shift. express At time t, is the integral operator; express The resonant frequency shift at a given moment.

[0139] In the above scheme, the inverse piezoelectric effect of piezoelectric materials is utilized by applying a bias voltage. By controlling the deformation of the piezoelectric film and dynamically adjusting the encapsulation structure parameters (such as cavity depth and support beam stiffness), active anti-interference can be achieved.

[0140] Example 2

[0141] Based on the same inventive concept as in Embodiment 1, this embodiment of the invention provides a piezoelectric thin-film acoustic sensor packaging design device, comprising:

[0142] The multiphysics coupling optimization model acquisition module is used to acquire a pre-constructed multiphysics coupling optimization model, wherein the optimization sub-objectives of the multiphysics coupling optimization model are to maximize sound pressure sensitivity, minimize frequency shift and minimize parasitic resonance amplitude;

[0143] The packaging parameter design module is used to solve the multi-physics coupling optimization model using a Bayesian global optimization algorithm to obtain the packaging parameters of the piezoelectric thin film acoustic sensor. During the solution process, the weights of different optimization sub-objectives are dynamically adjusted according to environmental parameters. The optimization sub-objectives include maximizing sound pressure sensitivity, minimizing frequency shift, and minimizing parasitic resonance amplitude.

[0144] The rest are the same as in Example 1.

[0145] Example 3

[0146] Based on the same inventive concept as in Embodiment 1, this embodiment of the invention provides a piezoelectric thin film acoustic sensor packaging design system, including a storage medium and a processor;

[0147] The storage medium is used to store instructions;

[0148] The processor is configured to operate according to the instructions to execute the method according to any one of Embodiment 1.

[0149] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] The embodiments of the present invention have been described above with reference to the accompanying drawings. 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 other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0154] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A piezoelectric thin-film acoustic sensor packaging design method, characterized in that, include: A pre-constructed multiphysics coupling optimization model is obtained, wherein the objective function of the multiphysics coupling optimization model is constructed with the objectives of maximizing sound pressure sensitivity, minimizing frequency shift, and minimizing parasitic resonance amplitude. The Bayesian global optimization algorithm is used to solve the multiphysics coupling optimization model to obtain the piezoelectric thin film acoustic sensor packaging parameters. The Bayesian global optimization algorithm uses a CNN model as a surrogate optimization model to fit the objective function. During the solution process, the weights of different optimization sub-objectives are dynamically adjusted according to environmental parameters. The mathematical expression for the objective function of the multiphysics coupling optimization model is: ， In the formula, The objective function of the multiphysics coupling optimization model is... For sound pressure sensitivity, For frequency offset, For parasitic resonance amplitude, , , All are weighting coefficients; the sound pressure sensitivity, frequency shift, and parasitic resonance amplitude are all calculated based on the elastic stiffness, piezoelectric constant, dielectric constant, structural parameters, and constitutive equation of the piezoelectric film. The constraints of the multiphysics coupling optimization model include: piezoelectric film thickness ,in, This represents the minimum thickness of the piezoelectric film. This represents the maximum value of the piezoelectric film thickness; cavity depth ,in, The operating wavelength; piezoelectric thin film radius , in, This is the minimum value of the piezoelectric film radius. This represents the maximum radius of the piezoelectric thin film. resonant frequency ,in, The center frequency of the target frequency band, The target bandwidth.

2. The piezoelectric thin-film acoustic sensor packaging design method according to claim 1, characterized in that: The input parameters of the CNN model include the elastic stiffness, piezoelectric constant, dielectric constant, and structural parameters of the piezoelectric film, and its output parameters are the objective function. The true value of is given by the loss function: ， In the formula, The loss function; This represents the total number of data collection points. Each data collection point includes the elastic stiffness, piezoelectric constant, dielectric constant, and structural parameters of the piezoelectric film. For the first One collection point, The output value of the CNN model. For the true value, This is a regularization term.

3. The piezoelectric thin-film acoustic sensor packaging design method according to claim 2, characterized in that: The Bayesian global optimization algorithm is used to solve the multi-physics coupling optimization model to obtain the piezoelectric thin-film acoustic sensor packaging parameters, specifically including: Based on the current agent model, find the parameter points of the optimal acquisition function, whereby the parameter points include... , , Elastic stiffness, piezoelectric constant, dielectric constant, and structural parameters of piezoelectric films; Based on this parameter point, the value of the objective function of the multiphysics coupling optimization model is calculated; parameters, , The elastic stiffness, piezoelectric constant, dielectric constant, structural parameters, and objective function values ​​of the piezoelectric thin film are added to the observation dataset as new parameter points. The surrogate model is retrained and updated using the updated observation dataset until the objective function of the multiphysics coupled optimization model converges.

4. The piezoelectric thin-film acoustic sensor packaging design method according to claim 3, characterized in that: The dynamic adjustment of the weights of different optimization sub-objectives based on environmental parameters specifically includes: Weighting based on temperature parameters The adjustment is made using the following formula: ， ， In the formula, The adjusted weights, This is the absolute difference between the current temperature and the reference temperature. The current temperature. Temperature threshold These are the initial weights; Weights based on vibration amplitude parameters The adjustment is made using the following formula: ， In the formula, The adjusted weights, As the initial weights, The vibration amplitude detected by the vibration sensor. This is the vibration amplitude threshold.

5. The piezoelectric thin-film acoustic sensor packaging design method according to claim 4, characterized in that: The method of dynamically adjusting the weights of different optimization sub-objectives based on environmental parameters also includes: Weighting based on temperature parameters and vibration amplitude parameters The adjustment is made using the following formula: 。 6. The piezoelectric thin-film acoustic sensor packaging design method according to claim 1, characterized in that: After solving the multiphysics coupling optimization model to obtain the piezoelectric thin-film acoustic sensor packaging parameters, the method further includes: Obtain the resonant frequency shift detected by the vibration sensor; Based on the resonant frequency shift fed back by the vibration sensor, the bias voltage is adjusted using a PID algorithm. The adjustment of bias voltage This is used to control the deformation of the piezoelectric thin film, so that the frequency shift is close to 0.

7. The piezoelectric thin-film acoustic sensor packaging design method according to claim 6, characterized in that: The PID algorithm uses the following formula to calculate and adjust the bias voltage. : ， In the formula, , , These are the proportional gain, integral gain, and differential gain, respectively. express time, This represents the resonant frequency offset.

8. A piezoelectric thin-film acoustic sensor packaging design device, characterized in that, include: The multiphysics coupling optimization model acquisition module is used to acquire a pre-constructed multiphysics coupling optimization model. The objective function of the multiphysics coupling optimization model is constructed with the objectives of maximizing sound pressure sensitivity, minimizing frequency shift, and minimizing parasitic resonance amplitude. The packaging parameter design module is used to solve the multi-physics coupling optimization model using a Bayesian global optimization algorithm to obtain the packaging parameters of the piezoelectric thin film acoustic sensor. The Bayesian global optimization algorithm uses a CNN model as a surrogate optimization model to fit the objective function. During the solution process, the weights of different optimization sub-objectives are dynamically adjusted according to environmental parameters. The mathematical expression for the objective function of the multiphysics coupling optimization model is: ， In the formula, The objective function of the multiphysics coupling optimization model is... For sound pressure sensitivity, For frequency offset, For parasitic resonance amplitude, , , All are weighting coefficients; the sound pressure sensitivity, frequency shift, and parasitic resonance amplitude are all calculated based on the elastic stiffness, piezoelectric constant, dielectric constant, structural parameters, and constitutive equation of the piezoelectric film. The constraints of the multiphysics coupling optimization model include: piezoelectric film thickness ,in, This represents the minimum thickness of the piezoelectric film. This represents the maximum value of the piezoelectric film thickness; cavity depth ,in, The operating wavelength; piezoelectric thin film radius , in, This is the minimum value of the piezoelectric film radius. This represents the maximum radius of the piezoelectric thin film. resonant frequency ,in, The center frequency of the target frequency band, The target bandwidth.

9. A piezoelectric thin-film acoustic sensor packaging design system, characterized in that, Including storage media and processor; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the method according to any one of claims 1-7.

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