Elastic wave device performance sample preprocessing method, system and equipment and storage medium

Abstract

The invention provides an elastic wave device performance sample preprocessing method, system and device and a storage medium, and the method comprises the steps: S101, obtaining the performance parameters of all device regions, divided on a wafer, of an elastic wave device through testing, and 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; and S103, combining the performance parameter matrixes corresponding to the performance parameters according to the sequence 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. According to the method, the obtained performance sample is closer to real data, compared with a single parameter extraction piece data processing method, errors caused by factory processes and test methods can be reduced, and meanwhile the data utilization rate is increased.

Description

Technical Field

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

[0002] With the widespread application of elastic wave devices, a good elastic wave device simulation platform is crucial for filter design. Simulation design can not only shorten the tape-out cycle, but also greatly save costs. The simulation platform of elastic wave devices, whether based on the COM (Component Object Model) method or the HCT (Hierarchical Clustering Tree) method, depends on the accuracy of the performance parameters of the reference chip.

[0003] A reference shot refers to a collection (Shot) of a batch of designed resonator devices. The resonator is a simple two-port network device. Five identical device collections are arranged in the top, bottom, left, right, and middle positions on a wafer. Multiple wafers of the same shape are produced each time. The current mainstream processing method for reference shots is to randomly select a wafer, select the device collection in the middle position, and then test the performance parameters (S parameters) of the resonator devices in it as standard data for reference shots.

[0004] However, the existing processing method for obtaining parameter-based resonator performance parameters has large errors, mainly because this method does not take into account the errors caused by factory processes, nor does it take into account the errors caused by testing, especially manual testing. In addition, since only one set of device data is used, the data of other sets are not used, resulting in data waste. Summary of the invention

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

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

[0007] Furthermore, the performance parameter is in complex form. In step S102, mathematical expectation calculations are performed on the real data and imaginary data of the performance parameter 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.

[0008] Furthermore, 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 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.

[0009] Furthermore, 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.

[0010] In a second aspect, the present invention further provides an elastic wave device performance sample preprocessing system, comprising: 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; The processing output module is used 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 a performance sample of the elastic wave device.

[0011] Furthermore, the performance parameter is in complex form, and the expectation calculation module is also 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 combine the real expected data and the imaginary expected data to obtain the performance parameter matrix.

[0012] Furthermore, 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.

[0013] Furthermore, the expected calculation module is also used for: According to the mathematical expectation μ and the standard deviation σAs 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.

[0014] In a third aspect, the present invention also provides a computer device, comprising: 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 the above embodiments when executing the elastic wave device performance sample preprocessing program.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which is stored a program for preprocessing performance samples of an elastic wave device. When the program for preprocessing performance samples of an elastic wave device is executed by a processor, the steps of the method for preprocessing performance samples of an elastic wave device as described in any one of the above embodiments are implemented.

[0016] The beneficial effect achieved by the present invention is that a method for preprocessing performance samples of elastic wave devices is proposed. The method assumes that the performance parameters of all devices manufactured on the wafer obey the normal distribution, and according to the characteristics of the normal distribution, all performance parameters are expected to be obtained and combined and summarized, so that the obtained performance samples are closer to the real data. Compared with the method of processing single parameter wafer data, it can reduce the errors caused by the foundry process and testing methods, and also improve the utilization rate of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of the steps of a method for preprocessing performance samples of an elastic wave device provided by an embodiment of the present invention; Figure 2 is a schematic structural diagram of a wafer and a device region provided by an embodiment of the present invention; Figure 3 is a schematic diagram of the structure of a system for preprocessing performance samples of an elastic wave device provided in an embodiment of the present invention; Figure 4 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0019] Please refer to Figure 1 , Figure 1 : is a flowchart of the steps of a method for preprocessing performance samples of an elastic wave device provided by an embodiment of the present invention, wherein the method for preprocessing performance samples of an elastic wave device comprises the following steps: S101 . Obtaining, through testing, respective performance parameters of all device regions of elastic wave devices divided on a wafer, wherein the performance parameters are S parameters.

[0020] For example, please refer to Figure 2 , Figure 2 is a schematic diagram of the structure of a wafer and a device region provided by an embodiment of the present invention. A wafer can be divided into multiple device regions, such as Figure 2 As shown in A, B, and C. It is understandable that due to the objective limitations of semiconductor manufacturing processes, multiple elastic wave devices made from the same wafer will also have differences in performance (physical differences). Therefore, the method of selecting an interval area as a reference piece for performance testing and analysis will be too one-sided and unable to reflect the overall picture.

[0021] S parameters, or radio frequency parameters, are parameters used to reflect the characteristics (amplitude, phase) of the reflected signal and transmission signal of an elastic wave device in the frequency domain. By analyzing and testing the S parameters, the radio frequency performance of the elastic wave device can be intuitively seen. In the embodiment of the present invention, the performance parameters of different device regions can be obtained by the COM method or the HCT method. In the actual production process, the method can be selected according to actual needs, and the present invention does not limit this.

[0022] S102 . 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.

[0023] Specifically, the performance parameter (ie, S parameter) is in a 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 Each of the four S parameters is expressed in complex form. For complex data, the real part and the imaginary part need to be calculated separately during the processing. In step S102, mathematical expectation calculation is performed on the real part data and the imaginary part data of the performance parameter to obtain real part expected data and imaginary part expected data, respectively, and the real part expected data and the imaginary part expected data are combined to obtain the performance parameter matrix.

[0024] Furthermore, 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 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.

[0025] In the embodiment of the present invention, in order to generally analyze the performance of a wafer or all elastic wave devices obtained in a production process, an assumption is made on the obtained performance parameters, assuming that they conform to a one-dimensional normal distribution. In fact, in the implementation process, the physical quality of the device obtained in the middle area of ​​a wafer is better, while the physical quality of the device obtained in the edge area is worse. This is an objective problem caused by the wafer precision and processing technology, and such physical quality distribution also conforms to the mathematical characteristics of the normal distribution. Therefore, in the embodiment of the present invention, by assuming that all the performance parameters conform to a one-dimensional normal distribution, different performance parameters (S 11 , S 12 , S 21 , S 22 ) to perform expected calculations to obtain a value that can reflect the overall performance status.

[0026] At the same time, in the embodiment of the present invention, different frequency points are traversed to obtain the mathematical expectations of the performance parameters at different frequency points during the required frequency period. The data obtained in this way better meets the requirements for evaluating the performance of elastic wave devices.

[0027] Furthermore, 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. The optimization of the genetic algorithm can make the error of the expected data obtained in the end smaller, which is beneficial to many analytical calculations.

[0028] 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.

[0029] The beneficial effect achieved by the present invention is that a method for preprocessing performance samples of elastic wave devices is proposed. The method assumes that the performance parameters of all devices manufactured on the wafer obey the normal distribution, and according to the characteristics of the normal distribution, all performance parameters are expected to be obtained and combined and summarized, so that the obtained performance samples are closer to the real data. Compared with the method of processing single parameter wafer data, it can reduce the errors caused by the foundry process and testing methods, and also improve the utilization rate of the data.

[0030] The embodiment of the present invention also provides an elastic wave device performance sample preprocessing system 200, please refer to Figure 3 , Figure 3 : is a schematic diagram of the structure of an elastic wave device performance sample preprocessing system provided by an embodiment of the present invention, which includes: The parameter acquisition test module 201 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 202 is used to perform mathematical expectation calculation on the performance parameter according to the port structure of the elastic wave device and the frequency of the performance parameter to obtain a performance parameter matrix; The processing output module 203 is used 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 a performance sample of the elastic wave device.

[0031] Furthermore, the performance parameter is in complex form, and the expectation calculation module 202 is also 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 combine the real expected data and the imaginary expected data to obtain the performance parameter matrix.

[0032] Furthermore, the expected calculation module 202 is further configured 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.

[0033] Furthermore, the expectation calculation module 202 is also used for: 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.

[0034] 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. Please refer to the description in the above embodiment and will not be repeated here.

[0035] The embodiment of the present invention also provides a computer device, please refer to Figure 4 , Figure 4 300 is a schematic diagram of the structure of a computer device provided in an 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 in the memory 302 and executable on the processor 301 .

[0036] The processor 301 calls the elastic wave device performance sample preprocessing program stored in the memory 302 to execute the steps of the elastic wave device performance sample preprocessing method provided in the embodiment of the present invention. Figure 1 , specifically including the following steps: S101 . Obtaining, through testing, respective performance parameters of all device regions of elastic wave devices divided on a wafer, wherein the performance parameters are S parameters.

[0037] S102 . 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.

[0038] Furthermore, the performance parameter is in complex form. In step S102, mathematical expectation calculations are performed on the real data and imaginary data of the performance parameter 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.

[0039] Furthermore, 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 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.

[0040] Furthermore, step S1023 also includes: According to the mathematical expectation μ and the standard deviation σAs 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.

[0041] 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.

[0042] The computer device 300 provided in the embodiment of the present invention can implement the steps in the elastic wave device performance sample preprocessing method in the above embodiment, and can achieve the same technical effect. Please refer to the description in the above embodiment and will not be repeated here.

[0043] An embodiment of the present invention also provides a computer-readable storage medium, on which is stored a program for preprocessing performance samples of an elastic wave device. When the program for preprocessing performance samples of an elastic wave device is executed by a processor, the various processes and steps in the method for preprocessing performance samples of an elastic wave device provided in an embodiment of the present invention are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0044] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be realized by instructing the relevant hardware (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) through the elastic wave device performance sample preprocessing program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0045] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0046] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation manner. The above-mentioned specific implementation manner is only illustrative rather than restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, all of which are within the protection 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.

2. The elastic wave device performance sample preprocessing method according to claim 1, characterized in that: The performance parameter is in complex form. In step S102, mathematical expectation calculation is performed on the real data and imaginary data of the performance parameter 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.

3. The elastic wave device performance sample preprocessing method according to claim 2, characterized in that: 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.

4. The elastic wave device performance sample preprocessing method according to claim 3, characterized in that: Step S1023 also includes: According to the mathematical expectation μ and the standard deviation σ As the optimization variable, a 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. 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; The processing output module is used 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 a performance sample of the elastic wave device.

6. The elastic wave device performance sample preprocessing system according to claim 5, characterized in that: The performance parameter is in complex form, and the expectation calculation module is also 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 combine the real expected data and the imaginary expected data to obtain the performance parameter matrix.

7. The elastic wave device performance sample preprocessing system according to claim 6, characterized in that: 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.

8. The elastic wave device performance sample preprocessing system according to claim 7, 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.

9. 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 4 when executing the elastic wave device performance sample preprocessing program.

10. 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 according to any one of claims 1 to 4 are implemented.

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