A method, device and storage medium for optimizing design of bulk acoustic wave resonator

Abstract

The present invention discloses a method, device and storage medium for optimizing the design of a bulk acoustic wave resonator based on a Kriging model, wherein the method includes: determining the structure and material of the resonator, establishing a corresponding MASON model, performing one-dimensional simulation on the MASON model, and obtaining simulation results; determining the design variables for optimizing the resonator based on the simulation results, and constructing a Kriging proxy model; determining the optimization target, constructing an optimization problem model according to the optimization target and the Kriging proxy model, and solving to obtain the optimal solution; reducing the upper and lower limits of the design variables to improve the optimization accuracy. The present invention can predict the performance indicators of unknown areas based on the data characteristics of existing variables, saving the time cost of actually preparing the device, and effectively shortening the calculation time and labor cost compared to the complex simulation calculations that take several days. The present invention can be widely used in the field of semiconductor materials and devices.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor materials and devices, and in particular to a bulk acoustic wave resonator optimization design method, device and storage medium based on a Kriging model. Background Art

[0002] In recent years, Film Bulk Acoustic Resonator (FBAR) has the advantages of high frequency, miniaturization, high performance, low power consumption, high power capacity, etc., and its manufacturing process is compatible with IC process and can be integrated, which is conducive to reducing device power consumption and reducing device size. It is currently the only integrated RF front-end filter. Therefore, FBAR filters will become the core components of future 5G high-frequency communications.

[0003] The film bulk acoustic wave resonator is mainly composed of three parts: substrate, acoustic wave reflection layer, and a sandwich piezoelectric oscillator stack composed of upper and lower electrodes and a piezoelectric film sandwiched between the upper and lower electrodes. When an RF voltage is applied between the two electrodes, an alternating electric field is generated in the piezoelectric oscillator stack. Through the inverse piezoelectric effect of the piezoelectric film, part of the electric energy is converted into a bulk acoustic wave that propagates along the thickness of the film and reflects back and forth between the two electrodes. When the propagation of the bulk acoustic wave in the piezoelectric oscillator stack is exactly half the wavelength or an odd multiple of half the wavelength, resonance will occur, that is, the fundamental frequency wavelength of the resonance is approximately equal to twice the thickness of the piezoelectric oscillator stack.

[0004] The commonly used simulation design method is to use ADS (Advanced Design System) to split the components of the MASON model, define properties, and then calculate after combination. When a large amount of data needs to be solved for the optimal value, the optimization calculation efficiency of the existing technology is low, and it often requires manual solution and analysis, which is difficult and time-consuming. Summary of the invention

[0005] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the present invention aims to provide a method, device and storage medium for optimizing the design of a bulk acoustic wave resonator based on a Kriging model.

[0006] The technical solution adopted by the present invention is:

[0007] A BAW resonator optimization design method based on a Kriging model comprises the following steps:

[0008] Determine the structure and material of the resonator, establish the corresponding MASON model, perform one-dimensional simulation on the MASON model, and obtain simulation results;

[0009] Determine the design variables for resonator optimization based on simulation results and construct a Kriging proxy model;

[0010] Determine the optimization goal, build the optimization problem model based on the optimization goal and the Kriging proxy model, and solve it to obtain the optimal solution;

[0011] Reduce the upper and lower limits of design variables to improve optimization accuracy.

[0012] Further, the structure of the resonator includes a bottom electrode, a piezoelectric layer, and a top electrode;

[0013] Among them, the material of the piezoelectric layer is any one of single-crystalline aluminum nitride, polycrystalline aluminum nitride, zinc oxide or lead zirconate titanate, and the material of the top electrode and the bottom electrode is any one or a combination of metals Pt, Mo, W, Ti or Au.

[0014] Furthermore, the step of determining the design variables for resonator optimization based on the simulation results and constructing a Kriging proxy model includes:

[0015] The material thickness H and the effective resonance area A are used as design variables for resonator optimization, and the series resonance frequency is kept unchanged.

[0016] Determine the size range of the design variables based on the relationship between the process requirements of the resonator processing and the structural dimensions;

[0017] The Latin hypercube experimental design method is used to randomly sample within the size range of the design variables to obtain sample points;

[0018] According to the MASON model of the resonator, the structural dimensions are parameterized to obtain all the models corresponding to the sample points;

[0019] After completing the parametric modeling, all sample points are simulated and calculated to obtain the simulation response values ​​of the sample points; the simulation response values ​​include the series resonant frequency impedance Z1, the parallel resonant frequency impedance Z2, the quality factor Q, and the effective electromechanical coupling coefficient K; wherein each group H, A corresponds to a group Z1, Z2, Q, K, the objective function y = Q*K, and the constraint function is the series and parallel resonant frequency impedances Z1, Z2 and the corresponding frequencies;

[0020] According to one or two main research points among the sample points H and A and their response values ​​Q and K, three Kriging proxy models of Q, K, and y are established respectively.

[0021] Furthermore, determining the size range of the design variables includes:

[0022] Obtain the boundary value of the size according to the size range, perform simulation calculation, analyze the calculation results and determine whether the size range is reasonable;

[0023] If the resonant impedance performance is not satisfied, reduce the size range and redefine the size range of the design variables to ensure the rationality of the size range.

[0024] Furthermore, the optimization goal is: when the resonant frequency meets the preset conditions, the electromechanical coupling coefficient is the best and the quality factor is the highest;

[0025] The expression of the optimization problem model is:

[0026]

[0027] Among them, Max(y(H,A)) indicates that the optimization target is the figure of merit FOM, H and A are design variables, and H L , H U is the lower and upper limits of the design variable thickness, A L , A U The lower and upper limits of the design variable area.

[0028] Furthermore, the narrowing of the upper and lower limits of the design variables to improve the optimization accuracy includes:

[0029] By using methods such as dichotomy, the range of variables can be narrowed to obtain the optimal resonator structural parameters that meet manufacturing requirements.

[0030] Furthermore, the resonator also includes a functional layer and a substrate, wherein the functional layer includes a load layer above the top electrode, a temperature compensation layer above the piezoelectric layer, a support layer above the substrate, a seed layer above the bottom electrode, and a Bragg reflection layer below the bottom electrode.

[0031] Furthermore, the structure of the resonator is any one of a bulk silicon back-etching type, a solid-state assembly type or a cavity type.

[0032] Furthermore, the research variables can be selected as any combination or variation of the material layer thickness and area.

[0033] Another technical solution adopted by the present invention is:

[0034] A Kriging model-based BAW resonator optimization design device, comprising:

[0035] at least one processor;

[0036] at least one memory for storing at least one program;

[0037] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0038] Another technical solution adopted by the present invention is:

[0039] A computer-readable storage medium stores a program executable by a processor, wherein the program executable by the processor is used to execute the method described above when executed by the processor.

[0040] The beneficial effects of the present invention are as follows: the present invention can predict the performance indicators of unknown areas according to the data characteristics of existing variables, saving the time cost of actually preparing devices, and effectively shortening the calculation time and labor costs compared to complex simulation calculations that take several days. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 is a flow chart of a BAW resonator optimization design method based on a Kriging model in an embodiment of the present invention;

[0043] Figure 2 is an impedance characteristic curve diagram of the output in the embodiment of the present invention;

[0044] Figure 3 It is an actual (predicted) diagram of the relationship between the top / bottom electrode thickness, the bottom electrode thickness and the effective electromechanical coupling coefficient of the first optimized Kriging model in an embodiment of the present invention;

[0045] Figure 4 It is an actual (predicted) diagram of the relationship between the top / bottom electrode thickness, the bottom electrode thickness and the effective electromechanical coupling coefficient of the optimized Kriging model in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0047] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0048] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0049] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0050] In view of the low efficiency of optimization calculation in the existing technology and the inability to quickly and accurately find the optimal solution, such as Figure 1 As shown, this embodiment provides a BAW resonator optimization design method based on the Kriging model, which is highly efficient and can quickly and accurately find the optimal solution. The method specifically includes the following steps:

[0051] S1. Determine the structure and material of the resonator, establish the corresponding MASON model, perform one-dimensional simulation on the MASON model, and obtain simulation results.

[0052] In this embodiment, the basic structure of the resonator is composed of a bottom electrode, a piezoelectric layer, and a top electrode. As an optional implementation, a support layer, a load layer, a protective layer, and other structures can be added as needed, and a corresponding MASON model is established according to the structure. The MASON model is simulated and solved to obtain the influence of each parameter on the performance.

[0053] S2. Determine the design variables for resonator optimization based on the simulation results and construct a Kriging proxy model.

[0054] According to the results of the one-dimensional simulation analysis obtained in step S1, when the material remains unchanged, the main factors affecting the device's resonant frequency, quality factor, and effective electromechanical coupling coefficient are the geometric parameters such as the thickness and area of ​​each functional layer of the material. Therefore, two main parameters, material thickness H and effective resonance area A, are selected as design variables for resonator optimization, keeping the series resonance frequency unchanged; then, based on the process requirements of resonator processing and the relationship between structural dimensions, the size range of the design variables is preliminarily determined, and based on the preliminarily determined size range, the boundary value of the size is selected for simulation calculation, the calculation results are analyzed and it is determined whether the size range is reasonable, and when the resonant impedance performance is not satisfied, the size range is reduced, and the size range of the four design variables is re-determined to ensure its rationality.

[0055] The optimal Latin hypercube experimental design method is adopted to randomly sample within the size range of the design variables to obtain uniform and sufficient sample points; the resonator MASON model is established, the four structural dimensions are parameterized, and all models corresponding to the sample points are obtained; after completing the parametric modeling, all sample points are simulated and calculated according to the simulation method determined in the first step to obtain the simulation response values ​​of the sample points: series resonant frequency impedance Z1, parallel resonant frequency impedance Z2, quality factor Q, effective electromechanical coupling coefficient K; so far, each group of H, A corresponds to a group of Z1, Z2, Q, K, the objective function y = Q*K, and the constraint function is the series and parallel resonant frequency impedances Z1, Z2 and the corresponding frequencies.

[0056] According to one or two main research points in the sample points H and A and their response values ​​Q and K, three Kriging proxy models of Q, K, and y are established respectively.

[0057] S3. Determine the optimization target, build the optimization problem model according to the optimization target and the Kriging proxy model, and solve it to obtain the optimal solution.

[0058] The ultimate goal of optimization is: under the premise of satisfying the resonant frequency, the electromechanical coupling coefficient is the best and the quality factor is the highest; therefore, an evaluation index is introduced: figure of merit FOM; the final optimization problem model is established as follows:

[0059]

[0060] Among them, Max(y(H,A)) indicates that the optimization goal is the figure of merit FOM, that is, the objective function is maximized, H and A are design variables, H L , H U is the lower and upper limits of the design variable thickness, A L , A U The lower and upper limits of the design variable area.

[0061] S4. Reduce the upper and lower limits of design variables to improve optimization accuracy.

[0062] The optimal resonator structural parameters are obtained by optimizing in step S3, and the variable range is narrowed down multiple times by using methods such as dichotomy to obtain the optimal resonator structural parameters that meet the manufacturing requirements.

[0063] The above method is explained in detail below with reference to the accompanying drawings and specific embodiments.

[0064] See also Figure 1 This embodiment provides a BAW resonator optimization design method based on the Kriging model, comprising the following steps:

[0065] The first step is to determine the resonator structure to be studied, establish the corresponding MASON model, perform one-dimensional simulation calculations, and analyze the simulation results.

[0066] 1.1) The basic structure of the resonator consists of a bottom electrode, a piezoelectric layer, and a top electrode;

[0067] 1.2) The MASON model is simulated and solved, and the following preliminary results are obtained: Figure 2 The impedance characteristic curve is shown.

[0068] In the second step, the design variables are determined as the top / bottom electrode thickness ratio r and the bottom electrode thickness H and their size range, and parametric modeling is performed to obtain the response values ​​of the sample points and construct the Kriging proxy model.

[0069] 2.1) Fixed piezoelectric layer 1100nm, electrode facing area 7000μm 2 , select AIN as the piezoelectric layer material and define the clamping dielectric constant as 9.5*10 -11 F / m, acoustic impedance is 3.7*10 7 kg / m 2 s, the electrode layer sound velocity is 11350m / s, the electromechanical coupling coefficient is 6%, the attenuation factor is 800dB / m, Mo is selected as the electrode material, and the acoustic impedance is defined as 6.39*10 7 kg / m 2 s, the electrode layer sound velocity is 6213m / s, and the attenuation factor is 500dB / m. Then, according to the relationship between the process requirements of the resonator processing and the structural dimensions, the design variable top / bottom electrode thickness ratio is preliminarily determined to be in the range of (0, 1.2), and the bottom electrode thickness is in the range of (0, 300)nm;

[0070] 2.2) Adopt the optimal Latin hypercube experimental design method, randomly sample 100 within the size range of the design variables, and obtain uniform and sufficient sample points; establish the resonator MASON model, substitute the sample point parameters into the model to calculate the simulation result response values: series resonant frequency and impedance Z1, parallel resonant frequency and impedance Z2, effective electromechanical coupling coefficient K; so far, each group of H, r corresponds to a group of Z1, Z2, K, the objective function is the effective electromechanical coupling coefficient K, and the constraint function is the series and parallel resonant frequency impedance Z1, Z2 and the corresponding frequency;

[0071] 2.3) According to the sample points H, r and their response values ​​Q, a Kriging proxy model of K is established.

[0072] The third step is to establish an optimization problem model and find the optimal solution.

[0073] The ultimate goal of optimization is: under the premise of satisfying the resonant frequency, the electromechanical coupling coefficient is the best. Based on this, the Kriging model is calculated and the results are as follows: Figure 3 shown.

[0074] The fourth step is to reduce the upper and lower limits of design variable changes and improve optimization accuracy.

[0075] The optimization in the previous step obtained the optimal resonator structure parameters in less than 1 minute. If the conventional method is used to modify the parameters one by one in the ADS software to simulate the results, it will take several days of work for several engineers, and it may not be possible to find the local optimal solution. In contrast, this method has high accuracy and lower space complexity. After repeatedly narrowing the variable range, the optimal resonator structure parameters that meet the manufacturing requirements are obtained. The results are as follows Figure 4 As shown, the optimal top / bottom electrode thickness ratio is 0.173913, the optimal bottom electrode thickness is 42.5652 nm, and the corresponding maximum effective electromechanical coupling coefficient is 7.27%.

[0076] In summary, the method of the present invention has at least the following advantages and beneficial effects compared to the prior art:

[0077] (1) The method of the present invention can use an algorithm to represent the relationship between the indicator variables of the design requirements and the objective function, so it can predict the performance indicators of the unknown area based on the data characteristics of the existing variables, saving the time cost of actually preparing the device.

[0078] (2) The proxy model used in the present invention is very lightweight and uses a "black box" to replace the traditional simulation calculation process. Compared with complex simulation operations that take several days, the calculation time is effectively shortened.

[0079] (3) The construction of the proxy model in the present invention only requires parameter extraction of the device under study. Through the operation of the proxy model, the direct coupling relationship between various parameters can be intuitively found, which is conducive to further understanding the coupling relationship between various parameters of the device.

[0080] (4) The surrogate model method has a wide range of applications. It can handle continuous variables while also having high adaptability to discrete variables. It is very suitable for the modeling and calculation of FBAR resonators.

[0081] This embodiment also provides a BAW resonator optimization design device based on the Kriging model, comprising:

[0082] at least one processor;

[0083] at least one memory for storing at least one program;

[0084] When the at least one program is executed by the at least one processor, the at least one processor implements Figure 1 The method shown.

[0085] A BAW resonator optimization design device based on a Kriging model in this embodiment can execute a BAW resonator optimization design method based on a Kriging model provided by a method embodiment of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0086] The present application also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes Figure 1 The method shown.

[0087] This embodiment also provides a storage medium storing instructions or programs that can execute a bulk acoustic wave resonator optimization design method based on a Kriging model provided by an embodiment of the method of the present invention. When the instructions or programs are run, any combination of implementation steps of the method embodiment can be executed, and the corresponding functions and beneficial effects of the method can be obtained.

[0088] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0089] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0090] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0091] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0092] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0093] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0094] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0095] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0096] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A bulk acoustic wave resonator optimization design method based on Kriging model, characterized in that: The following steps are involved: Determine the structure and material of the resonator, establish the corresponding MASON model, perform one-dimensional simulation on the MASON model, and obtain simulation results; Determine the design variables for resonator optimization based on simulation results and construct a Kriging proxy model; Determine the optimization goal, build the optimization problem model based on the optimization goal and the Kriging proxy model, and solve it to obtain the optimal solution; Reduce the upper and lower limits of design variables to improve optimization accuracy; The method of determining the design variables of the resonator optimization based on the simulation results and constructing the Kriging proxy model includes: The two parameters of material thickness H and effective resonance area A are used as design variables for resonator optimization; Determine the size range of the design variables based on the relationship between the process requirements of the resonator processing and the structural dimensions; The Latin hypercube experimental design method is used to randomly sample within the size range of the design variables to obtain sample points; According to the MASON model of the resonator, the structural dimensions are parameterized to obtain the models corresponding to all sample points; After completing the parametric modeling, all sample points are simulated and calculated to obtain the simulated response values ​​of the sample points; the simulated response values ​​include the series resonant frequency impedance Z1, the parallel resonant frequency impedance Z2, the quality factor Q, and the effective electromechanical coupling coefficient K; wherein each group of H, A corresponds to a group of Z1, Z2, Q, K, and the objective function , the constraint function is the series and parallel resonant frequency impedances Z1, Z2 and the corresponding frequencies; According to the sample points H, A and their response values ​​Q, K, three Kriging proxy models of Q, K, and y are established respectively; The optimization goal is: when the resonant frequency meets the preset conditions, the electromechanical coupling coefficient is the best and the quality factor is the highest; The expression of the optimization problem model is: Among them, Max(y(H,A)) indicates that the optimization target is the figure of merit FOM, H and A are design variables, and H L , is the lower and upper limits of the design variable thickness, A L , The lower and upper limits of the design variable area.

2. The BAW resonator optimization design method based on the Kriging model according to claim 1, characterized in that: The resonator comprises a bottom electrode, a piezoelectric layer, and a top electrode; Among them, the material of the piezoelectric layer is any one of single-crystalline aluminum nitride, polycrystalline aluminum nitride, zinc oxide or lead zirconate titanate, and the material of the top electrode and the bottom electrode is any one or a combination of metals Pt, Mo, W, Ti or Au.

3. The BAW resonator optimization design method based on Kriging model according to claim 1, characterized in that: Determining the size range of the design variables includes: Obtain the boundary value of the size according to the size range, perform simulation calculation, analyze the calculation results and determine whether the size range is reasonable; If the resonant impedance performance is not satisfied, reduce the size range and redefine the size range of the design variables to ensure the rationality of the size range.

4. The BAW resonator optimization design method based on the Kriging model according to claim 1, characterized in that: The narrowing of the upper and lower limits of the design variables to improve the optimization accuracy includes: By using methods such as dichotomy, the range of variables can be narrowed to obtain the optimal resonator structural parameters that meet manufacturing requirements.

5. The BAW resonator optimization design method based on Kriging model according to claim 2, characterized in that: The resonator further comprises functional layers and a substrate, wherein the functional layers comprise a load layer above the top electrode, a temperature compensation layer above the piezoelectric layer, a support layer above the substrate, a seed layer above the bottom electrode, and a Bragg reflection layer below the bottom electrode.

6. The BAW resonator optimization design method based on Kriging model according to claim 1, characterized in that: The structure of the resonator is any one of a bulk silicon back-etching type, a solid-state assembly type or a cavity type.

7. A BAW resonator optimization design device based on Kriging model, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 6.

8. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to perform the method according to any one of claims 1 to 6 when executed by the processor.

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