Elastic Wave Device Performance Sample Pretreatment Method, System, Equipment and Storage Medium

Abstract

The present invention provides a method, system, device and storage medium for preprocessing performance samples of elastic wave devices. The method includes: S101, obtaining the performance parameters of all device regions divided on the wafer of the elastic wave device through testing, where the performance parameters are S parameters; S102, performing mathematical expectation calculation on the performance parameters according to the port structure of the elastic wave device and the frequency points of the performance parameters to obtain a performance parameter matrix; S103, combining the performance parameter matrices corresponding to the performance parameters in the order of the frequency points to obtain a parameter matrix set, and outputting the parameter matrix set as the performance sample of the elastic wave device. The present invention can make the obtained performance samples closer to the real data, reduce the errors caused by factory processes and testing methods compared with the method of processing data of a single parameter extraction chip, and improve the utilization rate of data at the same time.

Description

Technical Field

[0001] The present invention is applicable to the technical field of wireless communication data processing, and particularly relates to a method, a system, a device and a storage medium for preprocessing performance samples of elastic wave devices. Background Art

[0002] With the wide application of elastic wave devices, a good simulation platform for elastic wave devices is crucial for the design of filters. Simulation design can not only shorten the chip fabrication cycle but also greatly save costs. Whether the simulation platform of elastic wave devices is based on the COM (Component Object Model) method or the HCT (Hierarchical Clustering Tree) method, its accuracy depends on the accuracy of the performance parameters of the parameter-providing chips.

[0003] The parameter-providing chip refers to a set (Shot) of a batch of designed resonator devices. The resonator is a simple two-port network device. Five sets of the same devices will be arranged on a wafer in the upper, lower, left, right, and middle positions. For wafers of the same shape, multiple wafers will be produced each time. Currently, the mainstream processing method for parameter-providing chips is as follows: randomly select a wafer, select the device set in the middle position, and then test the performance parameters (S parameters) of the resonator devices therein as the standard data of the parameter-providing chips.

[0004] However, the errors of the performance parameters of the resonators obtained by the existing processing method for parameter-providing chips are relatively large, mainly because this method does not consider the errors caused by the factory process, nor the errors caused by testing, especially manual testing. Moreover, since only the data of one set of device sets are used and the data of other sets are not utilized, data waste is caused. Summary of the Invention

[0005] The present invention provides a method, a system, a device and a storage medium for preprocessing performance samples of elastic wave devices, aiming to solve the problems of large errors and low data utilization rate in the data processing of parameter-providing chips of existing elastic wave devices.

[0006] To solve the above technical problems, in a first aspect, the present invention provides a method for preprocessing performance samples of elastic wave devices, including the following steps:

[0007] S101. Obtain the performance parameters of all device regions divided on the wafer of the elastic wave device through testing, where the performance parameters are S parameters;

[0008] S102. Perform mathematical expectation calculation on the performance parameters according to the port structure of the elastic wave device and the frequency points of the performance parameters to obtain a performance parameter matrix;

[0009] S103. Combine the performance parameter matrices corresponding to the performance parameters in the order of the frequency points to obtain a set of parameter matrices, and output the set of parameter matrices as the performance samples of the elastic wave device.

[0010] Furthermore, the performance parameter is in complex form. In step S102, the mathematical expectation of the real part data and the imaginary part data of the performance parameter is calculated respectively to obtain the real part expectation data and the imaginary part expectation data, and the real part expectation data and the imaginary part expectation data are combined to obtain the performance parameter matrix.

[0011] Furthermore, step S102 includes the following sub-steps:

[0012] S1021. Determine the range interval of the frequency points of the performance parameter;

[0013] S1022. Arbitrarily determine a test frequency point within the range interval of the frequency points;

[0014] S1023. Define the number of the device regions divided from all the wafers as n , and obtain the one-dimensional normal distribution probability density of the real part data or the imaginary part data of the performance parameter at the test frequency point, which satisfies the following conditions:

[0015] ;

[0016] where x ∈ (x1, x2, …, xn) , represents the real part data or the imaginary part data of the performance parameter, μ represents the mathematical expectation of the real part data or the imaginary part data, σ represents the standard deviation of the real part data or the imaginary part data;

[0017] S1024. Return to step S1022 until the calculation of the mathematical expectation of the real part data or the imaginary part data at each test frequency point in the range interval is completed;

[0018] S1025. Obtain the real part expectation data and the imaginary part expectation data according to the value of μ , and combine the real part expectation data and the imaginary part expectation data to obtain the performance parameter matrix.

[0019] Furthermore, step S1023 also includes:

[0020] Using the mathematical expectation μ and the standard deviation σ as optimization variables, and using a genetic algorithm for optimization, which satisfies the following conditions:

[0021] ;

[0022] Among them, represents the optimization process, and the optimization goal is to obtain the minimum value of the optimization variable.

[0023] Second, the present invention also provides a system for preprocessing performance samples of elastic wave devices, including:

[0024] A parameter-providing test module, configured to obtain the performance parameters of each device area divided on the wafer of the elastic wave device through testing, and the performance parameters are S-parameters;

[0025] An expectation calculation module, configured to perform a mathematical expectation calculation on the performance parameters according to the port structure of the elastic wave device and the frequency points of the performance parameters to obtain a performance parameter matrix;

[0026] A processing and output module, configured to combine the performance parameter matrices corresponding to the performance parameters in the order of the frequency points to obtain a set of parameter matrices, and output the set of parameter matrices as the performance samples of the elastic wave device.

[0027] Furthermore, the performance parameters are in complex form, and the expectation calculation module is further configured to perform a mathematical expectation calculation on the real part data and the imaginary part data of the performance parameters respectively to obtain real part expectation data and imaginary part expectation data, and combine the real part expectation data and the imaginary part expectation data to obtain the performance parameter matrix.

[0028] Furthermore, the expectation calculation module is further configured to execute:

[0029] S1021. Determine the range interval of the frequency points of the performance parameters;

[0030] S1022. Arbitrarily determine a test frequency point within the range interval of the frequency points;

[0031] S1023. Define the number of the device areas divided from all the wafers as n , and obtain the one-dimensional normal distribution probability density of the real part data or the imaginary part data of the performance parameters at the test frequency point, which satisfies the following conditions:

[0032] ;

[0033] Among them, x ∈ (x1, x2, …, xn) , represents the real part data or the imaginary part data of the performance parameters, μ represents the mathematical expectation of the real part data or the imaginary part data, σ represents the standard deviation of the real part data or the imaginary part data;

[0034] S1024. Return to step S1022 until the calculation of the mathematical expectation of the real part data or the imaginary part data at each test frequency point in the range interval is completed;

[0035] S1025. According to μ the value of, obtain the real part expected data and the imaginary part expected data, and combine the real part expected data and the imaginary part expected data to obtain the performance parameter matrix.

[0036] Furthermore, the expectation calculation module is further configured to:

[0037] Using the mathematical expectation μ and the standard deviation σ as optimization variables, use the genetic algorithm to optimize, which satisfies the following conditions:

[0038] ;

[0039] wherein, represents the optimization process, and the optimization goal is to obtain the minimum value of the optimization variables.

[0040] In a third aspect, the present invention further provides a computer device, including: a memory, a processor, and a performance sample preprocessing program for elastic wave devices stored on the memory and executable on the processor. When the processor executes the performance sample preprocessing program for elastic wave devices, the steps in the performance sample preprocessing method for elastic wave devices described in any one of the above embodiments are implemented.

[0041] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a performance sample preprocessing program for elastic wave devices is stored. When the performance sample preprocessing program for elastic wave devices is executed by a processor, the steps in the performance sample preprocessing method for elastic wave devices described in any one of the above embodiments are implemented.

[0042] The beneficial effect achieved by the present invention is that it proposes a performance sample preprocessing method for elastic wave devices. This method assumes that the performance parameters of all devices fabricated on the wafers follow a normal distribution, and according to the characteristics of the normal distribution, the expectations of all performance parameters are calculated and combined and summarized, making the obtained performance samples closer to the real data. Compared with the method of processing data of a single parameter extraction chip, it can reduce the errors caused by the foundry process and testing methods, and at the same time improve the utilization rate of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a step flowchart of the performance sample preprocessing method for elastic wave devices provided by an embodiment of the present invention;

[0044] Figure 2 It is a schematic structural diagram of a wafer and device regions provided by an embodiment of the present invention;

[0045] Figure 3 It is a schematic structural diagram of a performance sample preprocessing system for elastic wave devices provided by an embodiment of the present invention;

[0046] Figure 4 It is a schematic structural diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0047] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] Please refer to Figure 1 , Figure 1 It is a step flowchart of a method for preprocessing performance samples of elastic wave devices provided by an embodiment of the present invention. The method for preprocessing performance samples of elastic wave devices includes the following steps:

[0049] S101. Obtain the performance parameters of each device region divided on the wafer by the elastic wave device through testing. The performance parameters are S parameters.

[0050] Exemplarily, please refer to Figure 2 , Figure 2 It is a schematic structural diagram of a wafer and device regions provided by an embodiment of the present invention. Multiple device regions can be divided on one wafer, such as Figure 2 shown as A, B, and C in . It can be understood that due to the objective limitations of semiconductor manufacturing processes, there will also be differences in the performance of multiple elastic wave devices fabricated on the same wafer (physical differences). Therefore, the method of selecting an interval region as a parameter extraction wafer for performance testing and analysis will be too one-sided and unable to reflect the integrity.

[0051] The S parameter, that is, the radio frequency parameter, is a parameter used to reflect the characteristics (amplitude, phase) of the reflection signal and transmission signal of the elastic wave device in the frequency domain. By analyzing the measured S parameters, the radio frequency performance of the elastic wave device can be intuitively seen. In the embodiments of the present invention, the performance parameters of different device regions can be obtained by the COM method or the HCT method, and can be selected according to actual needs in the actual production process. The present invention does not limit this.

[0052] S102. Calculate the mathematical expectation of the performance parameters according to the port structure of the elastic wave device and the frequency points of the performance parameters to obtain a performance parameter matrix.

[0053] Specifically, the performance parameters (i.e., S-parameters) are in complex form. Taking a resonator as an example, a resonator is generally a two-port network device, with S 11 、S 12 、S 21 、S 22 Four S-parameters. Each S-parameter is represented in complex form. For complex-form data, the real part and the imaginary part need to be calculated separately during the processing. In step S102, the mathematical expectations of the real part data and the imaginary part data of the performance parameters are calculated respectively to obtain the real part expectation data and the imaginary part expectation data, and the real part expectation data and the imaginary part expectation data are combined to obtain the performance parameter matrix.

[0054] Furthermore, step S102 includes the following sub-steps:

[0055] S1021. Determine the range interval of the frequency points of the performance parameters;

[0056] S1022. Arbitrarily determine a test frequency point within the range interval of the frequency points;

[0057] S1023. Define the number of the device regions divided from all the wafers as n , and obtain the one-dimensional normal distribution probability density of the real part data or the imaginary part data of the performance parameters at the test frequency point, which satisfies the following conditions:

[0058] ;

[0059] Among them, x ∈ (x1, x2, …, xn) , represents the real part data or the imaginary part data of the performance parameters, μ represents the mathematical expectation of the real part data or the imaginary part data, σ represents the standard deviation of the real part data or the imaginary part data;

[0060] S1024. Return to step S1022 until the calculation of the mathematical expectation of the real part data or the imaginary part data at each test frequency point in the range interval is completed;

[0061] S1025. Obtain the real part expectation data and the imaginary part expectation data according to the value of μ , and combine the real part expectation data and the imaginary part expectation data to obtain the performance parameter matrix.

[0062] In the embodiments of the present invention, in order to generally analyze the performance of all elastic wave devices obtained from a single wafer or a single process production, assumptions are made about the obtained performance parameters, assuming that they conform to a one-dimensional normal distribution. In fact, during the implementation process, the physical properties of the devices manufactured in the middle area of a wafer are better, while those of the devices manufactured in the edge area are worse. Objectively, this is a problem caused by wafer precision and processing technology, and such a physical property distribution also conforms to the mathematical characteristics of the normal distribution. Therefore, in the embodiments of the present invention, by assuming that all the performance parameters conform to a one-dimensional normal distribution, the different performance parameters (S 11 、S 12 、S 21 、S 22 ) are used to calculate the expectations in turn, so as to obtain a value that can reflect the overall performance status.

[0063] Meanwhile, in the embodiments of the present invention, different frequency points are traversed to obtain the mathematical expectations of the performance parameters at different frequency points during the period at the required frequency. The data obtained in this way is more in line with the requirements for evaluating the performance of elastic wave devices.

[0064] Furthermore, step S1023 further includes:

[0065] Using the mathematical expectation μ and the standard deviation σ as optimization variables, and using a genetic algorithm for optimization, which satisfies the following conditions:

[0066] ;

[0067] wherein, represents the optimization process, and the optimization goal is to obtain the minimum value of the optimization variables. The optimization of the genetic algorithm can make the error of the finally obtained expected data smaller, which is beneficial to many analysis and calculations.

[0068] S103. Combine the performance parameter matrices corresponding to the performance parameters in the order of the frequency points to obtain a parameter matrix set, and output the parameter matrix set as the performance sample of the elastic wave device.

[0069] The beneficial effect achieved by the present invention is to propose a method for preprocessing the performance samples of elastic wave devices. This method assumes that the performance parameters of all devices manufactured on the wafer follow a normal distribution, and calculates the expectations and combines and summarizes all the performance parameters according to the characteristics of the normal distribution, so that the obtained performance samples are closer to the real data. Compared with the method of processing the data of a single parameter extraction chip, it can reduce the errors caused by the foundry process and testing methods, and at the same time improve the utilization rate of the data.

[0070] The embodiment of the present invention further provides an elastic wave device performance sample preprocessing system 200. Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of the elastic wave device performance sample preprocessing system provided by the embodiment of the present invention. It includes:

[0071] A parameter-providing and testing module 201, configured to obtain the performance parameters of all device regions divided on the wafer for the elastic wave device through testing, and the performance parameters are S parameters;

[0072] An expected value calculation module 202, configured to perform a mathematical expectation calculation on the performance parameters according to the port structure of the elastic wave device and the frequency points of the performance parameters to obtain a performance parameter matrix;

[0073] A processing and output module 203, configured to combine the performance parameter matrices corresponding to the performance parameters in the order of the frequency points to obtain a parameter matrix set, and output the parameter matrix set as the performance sample of the elastic wave device.

[0074] Furthermore, the performance parameters are in complex form, and the expected value calculation module 202 is further configured to perform a mathematical expectation calculation on the real part data and the imaginary part data of the performance parameters respectively to obtain real part expected value data and imaginary part expected value data, and combine the real part expected value data and the imaginary part expected value data to obtain the performance parameter matrix.

[0075] Furthermore, the expected value calculation module 202 is further configured to execute:

[0076] S1021. Determine the range interval of the frequency points of the performance parameters;

[0077] S1022. Arbitrarily determine a test frequency point within the range interval of the frequency points;

[0078] S1023. Define the number of the device regions divided from all the wafers as n , and obtain the one-dimensional normal distribution probability density of the real part data or the imaginary part data of the performance parameters at the test frequency point, which satisfies the following conditions:

[0079] ;

[0080] Wherein, x ∈ (x1, x2, …, xn) , represents the real part data or the imaginary part data of the performance parameters, μ represents the mathematical expectation of the real part data or the imaginary part data, σ represents the standard deviation of the real part data or the imaginary part data;

[0081] S1024. Return to step S1022 until the calculation of the mathematical expectation of the real part data or the imaginary part data at each test frequency point in the range interval is completed;

[0082] S1025. Obtain the real part expected data and the imaginary part expected data according to the value of μ , and combine the real part expected data and the imaginary part expected data to obtain the performance parameter matrix.

[0083] Furthermore, the expectation calculation module 202 is further configured to:

[0084] Using the mathematical expectation μ and the standard deviation σ as optimization variables, use the genetic algorithm to optimize, which satisfies the following conditions:

[0085] ;

[0086] wherein, represents the optimization process, and the optimization objective is to obtain the minimum value of the optimization variables.

[0087] The elastic wave device performance sample preprocessing system 200 can implement the steps in the elastic wave device performance sample preprocessing method in the above embodiment, and can achieve the same technical effects. Refer to the description in the above embodiment, and details are not repeated here.

[0088] An embodiment of the present invention also provides a computer device. Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of the computer device provided by the embodiment of the present invention. The computer device 300 includes: a memory 302, a processor 301, and an elastic wave device performance sample preprocessing program stored on the memory 302 and executable on the processor 301.

[0089] The processor 301 calls the elastic wave device performance sample preprocessing program stored in the memory 302 to execute the steps in the elastic wave device performance sample preprocessing method provided by the embodiment of the present invention. Please refer to Figure 1 , which specifically includes the following steps:

[0090] S101. Obtain the performance parameters of each device area divided on the wafer of the elastic wave device through testing. The performance parameters are S parameters.

[0091] S102. According to the port structure of the elastic wave device and the frequency points of the performance parameters, perform a mathematical expectation calculation on the performance parameters to obtain a performance parameter matrix.

[0092] Further, the performance parameter is in complex form. In step S102, the mathematical expectations of the real part data and the imaginary part data of the performance parameter are calculated respectively to obtain the real part expectation data and the imaginary part expectation data, and the real part expectation data and the imaginary part expectation data are combined to obtain the performance parameter matrix.

[0093] Further, step S102 includes the following sub-steps:

[0094] S1021. Determine the range interval of the frequency points of the performance parameter;

[0095] S1022. Arbitrarily determine a test frequency point within the range interval of the frequency points;

[0096] S1023. Define the number of the device regions divided from all the wafers as n , and obtain the one-dimensional normal distribution probability density of the real part data or the imaginary part data of the performance parameter at the test frequency point, which satisfies the following conditions:

[0097] ;

[0098] Wherein, x ∈ (x1, x2, …, xn) , represents the real part data or the imaginary part data of the performance parameter, μ represents the mathematical expectation of the real part data or the imaginary part data, σ represents the standard deviation of the real part data or the imaginary part data;

[0099] S1024. Return to step S1022 until the calculation of the mathematical expectation of the real part data or the imaginary part data at each test frequency point in the range interval is completed;

[0100] S1025. Obtain the real part expectation data and the imaginary part expectation data according to the value of μ , and combine the real part expectation data and the imaginary part expectation data to obtain the performance parameter matrix.

[0101] Further, step S1023 further includes:

[0102] Using the mathematical expectation μ and the standard deviation σ as optimization variables, and using a genetic algorithm for optimization, which satisfies the following conditions:

[0103] ;

[0104] Wherein, represents the optimization process, and the optimization objective is to obtain the minimum value of the optimization variables.

[0105] S103. Combine the performance parameter matrices corresponding to the performance parameters in the order of the frequency points to obtain a set of parameter matrices, and output the set of parameter matrices as the performance samples of the elastic wave device.

[0106] The computer device 300 provided by the embodiment of the present invention can implement the steps in the method for preprocessing the performance samples of the elastic wave device in the above embodiment, and can achieve the same technical effects. For the description, refer to the above embodiment and will not be repeated here.

[0107] The embodiment of the present invention also provides a computer-readable storage medium, on which a program for preprocessing the performance samples of the elastic wave device is stored. When the program for preprocessing the performance samples of the elastic wave device is executed by a processor, it implements each process and step in the method for preprocessing the performance samples of the elastic wave device provided by the embodiment of the present invention, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0108] Those of ordinary skill in the art can understand that all or part of the processes of implementing the method in the above embodiment can be completed by instructing relevant hardware (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) through a program for preprocessing the performance samples of the elastic wave device. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0109] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including the element.

[0110] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiments of the present invention. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present invention and without departing from the spirit and scope protected by the claims of the present invention, can also make many equivalent variations in form, all of which fall within the protection scope of the present invention.

Claims

1. A method for preprocessing performance samples of elastic wave devices, characterized in that: The following steps are involved: S101, obtaining performance parameters of all device regions of the elastic wave device divided on the wafer through testing, wherein the performance parameters are S parameters; S102, performing mathematical expectation calculation on the performance parameters according to the port structure of the elastic wave device and the frequency of the performance parameters to obtain a performance parameter matrix; S103, combining the performance parameter matrices corresponding to the performance parameters in the order of the frequency points to obtain a parameter matrix set, and outputting the parameter matrix set as a performance sample of the elastic wave device; Wherein, the performance parameter is in complex form, and in step S102, mathematical expectation calculation is performed on the real data and the imaginary data of the performance parameter respectively to obtain real expected data and imaginary expected data respectively, and the real expected data and the imaginary expected data are combined to obtain the performance parameter matrix; Step S102 includes the following sub-steps: S1021, determining a range of the frequency points of the performance parameter; S1022, arbitrarily determine a test frequency point within the range of the frequency points; S1023, defining the number of the device regions obtained by dividing the wafer as n , obtaining a one-dimensional normal distribution probability density of the real data or the imaginary data of the performance parameter at the test frequency point, which satisfies the following conditions: ； in, x∈(x1,x2,…,xn) , represents the real data or the imaginary data of the performance parameter, μ represents the mathematical expectation of the real data or the imaginary data, σ represents a standard deviation of the real data or the imaginary data; S1024, returning to step S1022, until the calculation of the mathematical expectation of the real data or the imaginary data of each of the test frequency points in the range is completed; S1025, according to μ The real part expected data and the imaginary part expected data are obtained by using the numerical value of , and the real part expected data and the imaginary part expected data are combined to obtain the performance parameter matrix.

2. The elastic wave device performance sample preprocessing method according to claim 1, characterized in that: Step S1023 also includes: According to the mathematical expectation μ and the standard deviation σ As the optimization variable, the genetic algorithm is used for optimization, which meets the following conditions: ； in, It represents the optimization process, and the optimization goal is to obtain the minimum value of the optimization variable.

3. An elastic wave device performance sample preprocessing system, characterized in that: include: A parameter acquisition test module is used to obtain the performance parameters of all device regions of the elastic wave device divided on the wafer through testing, wherein the performance parameters are S parameters; An expectation calculation module, used to perform mathematical expectation calculation on the performance parameters according to the port structure of the elastic wave device and the frequency of the performance parameters to obtain a performance parameter matrix; A processing output module, used for combining the performance parameter matrices corresponding to the performance parameters in the order of the frequency points to obtain a parameter matrix set, and outputting the parameter matrix set as a performance sample of the elastic wave device; Wherein, the performance parameter is in complex form, and the expectation calculation module is further used to perform mathematical expectation calculation on the real data and imaginary data of the performance parameter respectively, to obtain real expected data and imaginary expected data respectively, and to combine the real expected data and the imaginary expected data to obtain the performance parameter matrix; The expected calculation module is also used to execute: S1021, determining a range of the frequency points of the performance parameter; S1022, arbitrarily determine a test frequency point within the range of the frequency points; S1023, defining the number of the device regions obtained by dividing all the wafers as n , obtaining a one-dimensional normal distribution probability density of the real data or the imaginary data of the performance parameter at the test frequency point, which satisfies the following conditions: ； in, x∈(x1,x2,…,xn) , represents the real data or the imaginary data of the performance parameter, μ represents the mathematical expectation of the real data or the imaginary data, σ represents a standard deviation of the real data or the imaginary data; S1024, returning to step S1022, until the calculation of the mathematical expectation of the real data or the imaginary data at each of the test frequency points in the range is completed; S1025, according to μ The real part expected data and the imaginary part expected data are obtained by using the numerical value of , and the real part expected data and the imaginary part expected data are combined to obtain the performance parameter matrix.

4. The elastic wave device performance sample preprocessing system according to claim 3, characterized in that: The expected calculation module is also used for: According to the mathematical expectation μ and the standard deviation σ are used as optimization variables and the genetic algorithm is used for optimization, which satisfies the following conditions: ； in, It represents the optimization process, and the optimization goal is to obtain the minimum value of the optimization variable.

5. A computer device, characterized in that: include: A memory, a processor, and an elastic wave device performance sample preprocessing program stored in the memory and executable on the processor, wherein the processor implements the steps in the elastic wave device performance sample preprocessing method as described in any one of claims 1 to 2 when executing the elastic wave device performance sample preprocessing program.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an elastic wave device performance sample preprocessing program, and when the elastic wave device performance sample preprocessing program is executed by the processor, the steps in the elastic wave device performance sample preprocessing method as described in any one of claims 1-2 are implemented.

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