SAW filter design error evaluation method and system based on multi-dimensional statistical analysis

Abstract

The invention relates to a multi-dimensional statistical analysis SAW filter design error evaluation method and system, and the method comprises the steps: obtaining the actual measurement data of a SAW filter and simulation data obtained through the simulation of EDA simulation software, carrying out the analysis based on the actual measurement data and the simulation data, and obtaining a key evaluation index of the SAW filter, the key evaluation indexes comprise mean square error, standard deviation, variation coefficient and variance. And according to the key evaluation indexes, performing data analysis and statistics by taking the finger strip spacing and other key evaluation indexes as two dimensions to obtain an error evaluation result. Meanwhile, multi-dimensional statistical analysis of the SAW filter design error is carried out in combination with the mean square error, the standard deviation, the variable coefficient and the variance, and the SAW filter design error can be accurately evaluated.

Description

Technical Field

[0001] This application relates to the field of filter technology, and in particular to a method and system for evaluating the design error of SAW filters using multi-dimensional statistical analysis. Background Technology

[0002] In the field of surface acoustic wave (SAW) filter design, the pitch parameter (the distance between the center-to-center distance of the electrode strips) of the DMS (Dual-Mode SAW) structure has a decisive influence on the frequency response characteristics of the device. Traditional design methods typically employ fixed values ​​or simple linearly varying pitch parameter schemes. However, in actual manufacturing processes, due to limitations in the process design accuracy and manufacturing fluctuations of the PDK (Process Design Kit), the pitch parameters of the actual product often deviate significantly from the design values. This deviation in geometric parameters is nonlinearly amplified through the propagation characteristics of surface acoustic waves, ultimately leading to a non-negligible difference between the simulation results and measured data for key filter performance indicators (such as insertion loss and out-of-band rejection), severely impacting the efficiency of product design iteration and the accuracy of performance optimization. Accurately assessing the design error of SAW filters is a problem that urgently needs to be solved. Summary of the Invention

[0003] Therefore, it is necessary to provide a SAW filter design error assessment method and system that can accurately evaluate design errors through multi-dimensional statistical analysis to address the above problems.

[0004] The first aspect of this application provides a method for evaluating SAW filter design errors using multi-dimensional statistical analysis, including:

[0005] The measured and simulated data of the SAW filter were obtained; the simulated data was obtained by simulation using EDA simulation software.

[0006] Based on the measured data and the simulation data, the key evaluation indicators of the SAW filter are analyzed and obtained; the key evaluation indicators include mean square error, standard deviation, coefficient of variation, and variance.

[0007] Based on the aforementioned key evaluation indicators, data analysis and statistics were performed using mean squared error and other key evaluation indicators as two dimensions to obtain error evaluation results.

[0008] In one embodiment, the measured data and simulation data of the SAW filter are obtained, including: obtaining the measured data of the SAW filter and the simulation data output by the EDA simulation software, and importing them into Python programming software for key evaluation index analysis.

[0009] In one embodiment, both the measured data and the simulated data include S-parameters and corresponding finger-bar spacing data; based on the measured data and the simulated data, key evaluation indicators of the SAW filter are analyzed and obtained, including:

[0010]

[0011] Where MSE is the mean squared error, Y measured,i Let Y be the S-parameter of the i-th actual measurement. simulated,i Let S be the S-parameter of the i-th simulation, and n be the total number of S-parameters.

[0012] In one embodiment, based on the measured data and the simulation data, the key evaluation metrics of the SAW filter are analyzed and obtained, and the analysis further includes:

[0013]

[0014] Where s is the sample standard deviation, n is the total number of finger spacings, and X i For each finger spacing value, μ is the overall average of the finger spacing.

[0015] In one embodiment, based on the measured data and the simulation data, the key evaluation metrics of the SAW filter are analyzed and obtained, and the analysis further includes:

[0016]

[0017] Where CV is the coefficient of variation, σ is the standard deviation of the strip spacing, and μ is the overall average value of the strip spacing.

[0018] In one embodiment, based on the measured data and the simulation data, the key evaluation metrics of the SAW filter are analyzed and obtained, and the analysis further includes:

[0019]

[0020] Where, σ 2 The total variance is N, where N is the total number of intervals, and X is the total variance. i It is the value of the spacing between each finger bar, and μ is the overall average value of the spacing between the bars.

[0021] In one embodiment, based on the key evaluation indicators, data analysis and statistics are performed using mean squared error and other key evaluation indicators as two dimensions to obtain error evaluation results, including:

[0022] The mean squared error was plotted on the ordinate, and the standard deviation, coefficient of variation, and variance were plotted on the abscissa to obtain the variation curve.

[0023] The second aspect of this application provides a multi-dimensional statistical analysis-based SAW filter design error evaluation system, comprising:

[0024] The data acquisition module is used to acquire measured and simulated data of the SAW filter; the simulated data is obtained by simulation using EDA simulation software.

[0025] The data analysis module is used to analyze and obtain the key evaluation indicators of the SAW filter based on the measured data and the simulation data; the key evaluation indicators include mean square error, standard deviation, coefficient of variation and variance;

[0026] The error assessment module is used to perform data analysis and statistics based on the key assessment indicators, using mean squared error and other key assessment indicators as two dimensions, to obtain the error assessment results.

[0027] A third aspect of this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0028] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0029] The aforementioned multi-dimensional statistical analysis of SAW filter design error assessment methods, systems, computer equipment, and storage media acquires measured data and simulation data obtained through EDA simulation software. Based on the measured and simulation data, key evaluation indicators for the SAW filter are analyzed, including mean square error, standard deviation, coefficient of variation, and variance. According to these key evaluation indicators, data analysis and statistics are performed using finger-strip spacing and other key evaluation indicators as two dimensions to obtain error assessment results. Simultaneously, by combining mean square error, standard deviation, coefficient of variation, and variance for multi-dimensional statistical analysis of SAW filter design error, the design error of the SAW filter can be accurately assessed. Attached Figure Description

[0030] Figure 1 A flowchart of a SAW filter design error evaluation method based on multi-dimensional statistical analysis in one embodiment;

[0031] Figures 2 to 23 This is a diagram comparing test results from different embodiments;

[0032] Figure 24 A block diagram of a SAW filter design error evaluation system based on multidimensional statistical analysis in one embodiment;

[0033] Figure 25This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0035] In the field of surface acoustic wave (SAW) filter design, a prominent problem with current technology is the lack of statistical and quantitative evaluation methods for the variation of pitch parameters. Existing solutions can only provide point-to-point performance comparisons at discrete pitch values, failing to systematically evaluate the fluctuation characteristics of pitch parameters during manufacturing and making it difficult to establish a quantitative correlation between these fluctuations and the final frequency response deviation. This technological gap makes it difficult for engineers to accurately predict the performance of actual products during the simulation phase, especially in demanding FDD (Frequency Division Duplex) applications, where the design and optimization of key indicators such as transition band roll-off characteristics face significant challenges.

[0036] In actual manufacturing, the model accuracy and process fluctuations of the PDK (Process Design Kit) can introduce significant deviations, causing the pitch parameters to deviate from the design target. This, in turn, affects the frequency response characteristics of the filter, resulting in a significant difference between simulation and measured performance. PDK process models are typically based on statistically averaged data, making it difficult to fully encompass the nonlinear effects of key processes such as lithography and etching. For example, in submicron-level pitch control, the proximity effect in lithography and lateral drilling in etching can lead to discrepancies between the actual electrode spacing and the design value. Furthermore, systematic fluctuations such as temperature gradients and etching rate non-uniformity during wafer fabrication, as well as random factors such as lithography alignment errors and thin film thickness variations, can cause the pitch parameters to exhibit a non-uniform distribution across different regions of the wafer. For DMS structures using gradient pitch, process deviations accumulate with the increase in the number of fingers, causing the propagation characteristics of surface acoustic waves to deviate from expectations.

[0037] These manufacturing deviations ultimately manifest as deterioration in filter performance, such as center frequency shift, worsened out-of-band rejection, and increased passband ripple. For example, measured data from a 2.4GHz SAW filter showed that, due to systematic deviations in the pitch parameters, its transition band slope was reduced by 25% compared to simulation results, severely impacting adjacent channel interference suppression capabilities. This discrepancy between simulation and actual measurements not only increases debugging costs but also poses a significant challenge to the mass production consistency of filters in high-frequency applications (such as 5G communication). Therefore, it is necessary to more accurately assess the impact of process variations during the design phase or employ compensation techniques to reduce the impact of pitch deviations on filter performance.

[0038] Based on this, this application proposes a SAW filter performance evaluation method based on multi-dimensional statistical analysis. By introducing four statistical measures—MSE (mean square error), standard deviation, coefficient of variation, and variance—a complete quantitative evaluation system is constructed, encompassing pitch parameter fluctuations and frequency response deviations. This method fully leverages the synergistic advantages of EDA software's code execution capabilities and Python programming to achieve automated comparison and analysis of simulation data and measured S21 parameters (SNP file). This not only accurately quantifies the performance deviation of the current design scheme but also provides data support for subsequent pitch parameter optimization. Compared to traditional methods, this technical solution improves the simulation and measurement error of SAW filter insertion loss from ±0.5dB to ±0.1dB and achieves steep transition band characteristics, providing a novel technical approach for the design of high-performance SAW filters.

[0039] In one embodiment, such as Figure 1 As shown, a multi-dimensional statistical analysis method for evaluating SAW filter design errors is provided, including:

[0040] Step S110: Obtain the measured and simulated data of the SAW filter.

[0041] The simulation data was obtained through EDA simulation software. Both the measured and simulated data include S-parameters (e.g., S21 parameters) and the corresponding pitch size data. The actual design data of the SAW filter can be imported into the EDA simulation software for simulation to obtain simulation data. Then, the measured data of the SAW filter and the simulation data output by the EDA simulation software are imported into Python programming software in SNP file format for key evaluation index analysis, efficiently completing the comparative analysis of RF circuit simulation and measured data. Using Python for data processing and plotting greatly improves the flexibility of data analysis and enhances the visualization of results.

[0042] Step S120: Based on measured and simulated data, the key evaluation metrics of the SAW filter are analyzed and obtained. These key evaluation metrics include mean squared error, standard deviation, coefficient of variation, and variance. Using Python programming software, combined with measured and simulated data, data analysis is performed to calculate these four key evaluation metrics: mean squared error, standard deviation, coefficient of variation, and variance. In this embodiment, variance can be the population variance, and standard deviation can be the sample standard deviation.

[0043] In the design and manufacturing process of SAW filters, the difference between simulated and measured performance can be systematically characterized using various statistical methods to comprehensively assess the impact of process variations on device performance. Among these methods, the mean square error (MSE) effectively quantifies the overall deviation of the passband insertion loss, reflecting the systematic differences between the simulation model and the actual device. A large MSE value indicates a potential overall shift in the pitch parameters or significant changes in material properties during manufacturing, leading to a marked deviation between the filter's passband response and design expectations. This difference often stems from limitations of the PDK model or systematic deviations in key process parameters.

[0044] Furthermore, analyzing the dispersion of insertion loss error reveals the stability of the measured data through the standard deviation σ. A smaller standard deviation σ typically indicates better process consistency, with less performance fluctuation across different batches or wafer locations. Conversely, a larger standard deviation σ indicates significant inhomogeneity during manufacturing, potentially caused by factors such as lithography alignment errors or etching rate fluctuations. Notably, in DMS structures employing a gradient pitch design, this dispersion tends to accumulate and amplify with increasing finger count, resulting in a nonlinear impact on filter performance.

[0045] The coefficient of variation (CoV), as the ratio of standard deviation to mean, is particularly suitable for assessing the relative variability between different pitch parameters. In DMS structures containing numerous finger electrodes, CoV can directly reflect the impact of process control on the gradual change in electrode spacing. A high CoV value usually indicates that the actual distribution of the pitch parameters has deviated significantly from the design expectations, potentially leading to increased passband ripple or deterioration of transition band roll-off characteristics. By monitoring the trend of CoV changes, uniformity issues in photolithography or thin film deposition processes can be detected in a timely manner.

[0046] Furthermore, variance reflects the impact of process variations on insertion loss. Larger variance often points to uncontrolled critical process parameters, such as significant changes in etching precision or metal thickness; while smaller variance may indicate that errors are primarily influenced by environmental factors. Combining variance analysis with MSE (Mean Sequencing Equation) can better distinguish between systematic and random errors, providing clear direction for process optimization. For example, when high MSE is observed accompanied by high variance, it may be necessary to focus on checking the stability of the etching process; while a combination of high MSE and low variance suggests the need to recalibrate the PDK model parameters. This multi-dimensional statistical analysis method can not only accurately pinpoint the root cause of problems but also provide data support for improving the design accuracy and manufacturing consistency of SAW filters.

[0047] Specifically, the first step is to write calculation code in Python programming software. The first step is to use the mathematical formula for mean squared error. Y measured,iFor the i-th actually measured S-parameter (specifically, parameter S21), Y simulated,i Let S be the S-parameters of the i-th simulation (specifically, S21 parameters), and n be the total number of S-parameters. The mean square error is obtained by combining the difference at each frequency point in the data with the formula.

[0048] The second step is to apply the coefficient of variation formula. Where σ is the standard deviation of the finger spacing obtained from EDA, and μ is the overall average value of the finger spacing.

[0049] The third step is to apply the population variance formula. N refers to the total number of spacing lines, X i It is the value of the spacing between each finger bar, and μ is the overall average value of the spacing between the bars.

[0050] The fourth step is to apply the sample standard deviation formula. n is the total number of finger spacings, X i For each finger spacing value, μ is the overall average of the finger spacing.

[0051] Run the program in Python programming software to store the measured data and simulation data in an Excel file. Finally, combine multiple sets of measured data and simulation data to calculate the values ​​of various key evaluation indicators through the above steps.

[0052] Step S130: Based on the key evaluation indicators, perform data analysis and statistics using mean squared error and other key evaluation indicators as two dimensions to obtain the error evaluation results.

[0053] After calculating the values ​​of various key evaluation indicators in Python programming software, data analysis can be performed using the mean squared error as the ordinate and the standard deviation, coefficient of variation, and variance as the abscissas, respectively, to plot the resulting curves as the error evaluation results. The Python programming software can also send the generated error evaluation results back to the EDA (Electronic Data Acquisition) system for visualization, allowing testers to observe them. The following section compares test results using specific examples.

[0054] After obtaining the measured and simulated SNP data for C1, C2, C3, and C4, along with the corresponding pitch size data, the mean squared error (MSE), population variance, standard deviation, and coefficient of variation were calculated using the statistical algorithm described above. In practice, two versions were developed based on the difference in the matching loads applied to the circuit terminals: the first version applied different matching loads to the measured and simulated SNPs, while the second version kept the matching loads at both ends of the measured SNP circuit consistent with the simulation.

[0055] like Figures 2 to 12 The results shown are all from tests conducted under the same matched load conditions. Figures 2 to 4 The graph shows the changes in mean squared error (MSE) and other statistical results under the same matched load, along with the corresponding fitted curves. Figure 2 This is the fitted curve of mean squared error (MSE) versus standard deviation (SD). The horizontal axis represents standard deviation (SD), and the vertical axis represents mean squared error (MSE). Figure 3 This is the fitted curve of mean squared error (MSE) versus population variance (Population Variance), with the horizontal axis representing population variance (Population Variance) and the vertical axis representing mean squared error (MSE). Figure 4 This is the fitted curve of mean squared error (MSE) versus coefficient of variation (COP), with the horizontal axis representing COP and the vertical axis representing MSE.

[0056] like Figure 5 and Figure 6 As shown, the simulation and measured data of C2 show the overall frequency response of the S21 parameter, as well as the frequency response of the S21 parameter within the band. The red dashed line in the figure represents the measured data, the blue solid line represents the simulation data, the horizontal axis represents the frequency, and the vertical axis represents the S21 parameter.

[0057] like Figure 7 and Figure 8 As shown, the simulation and measured data of C3 show the overall frequency response of the S21 parameter, as well as the frequency response of the S21 parameter within the band. The red dashed line in the figure represents the measured data, the blue solid line represents the simulation data, the horizontal axis represents the frequency, and the vertical axis represents the S21 parameter.

[0058] like Figures 9 to 10As shown, the simulation and measured data of C4 show the overall frequency response of the S21 parameter, as well as the frequency response of the S21 parameter within the band. The red dashed line in the figure represents the measured data, the blue solid line represents the simulation data, the horizontal axis represents the frequency, and the vertical axis represents the S21 parameter.

[0059] like Figures 11 to 12 As shown, the simulation and measured data of C1 show the overall frequency response of the S21 parameter, as well as the frequency response of the S21 parameter within the band. The red dashed line in the figure represents the measured data, the blue solid line represents the simulation data, the horizontal axis represents the frequency, and the vertical axis represents the S21 parameter.

[0060] like Figures 13 to 23 The results shown are for different load conditions. Figures 13 to 15 The figure shows the changes in MSE and other statistical results under different matching load conditions, as well as the corresponding fitted curves. Figure 13 This is the fitted curve of mean squared error (MSE) versus standard deviation (SD). The horizontal axis represents standard deviation (SD), and the vertical axis represents mean squared error (MSE). Figure 14 This is the fitted curve of mean squared error (MSE) versus population variance (Population Variance), with the horizontal axis representing population variance (Population Variance) and the vertical axis representing mean squared error (MSE). Figure 15 This is the fitted curve of mean squared error (MSE) versus coefficient of variation (COP), with the horizontal axis representing COP and the vertical axis representing MSE.

[0061] like Figure 16 and Figure 17 As shown, the simulation and measured data of C2 show the overall frequency response of the S21 parameter, as well as the frequency response of the S21 parameter within the band. The red dashed line in the figure represents the measured data, the blue solid line represents the simulation data, the horizontal axis represents the frequency, and the vertical axis represents the S21 parameter.

[0062] like Figure 18 and Figure 19 As shown, the simulation and measured data of C3 show the overall frequency response of the S21 parameter, as well as the frequency response of the S21 parameter within the band. The red dashed line in the figure represents the measured data, the blue solid line represents the simulation data, the horizontal axis represents the frequency, and the vertical axis represents the S21 parameter.

[0063] like Figure 20 and Figure 21 As shown, the simulation and measured data of C4 show the overall frequency response of the S21 parameter, as well as the frequency response of the S21 parameter within the band. The red dashed line in the figure represents the measured data, the blue solid line represents the simulation data, the horizontal axis represents the frequency, and the vertical axis represents the S21 parameter.

[0064] like Figure 22 and Figure 23 As shown, the simulation and measured data of C4 show the overall frequency response of the S21 parameter, as well as the frequency response of the S21 parameter within the band. The red dashed line in the figure represents the measured data, the blue solid line represents the simulation data, the horizontal axis represents the frequency, and the vertical axis represents the S21 parameter.

[0065] In summary, the value of combining EDA software with Python programming in practical engineering applications is reflected in several aspects: automated processes reduce the time cost of manually exporting and importing data; Python's rich data processing libraries provide more flexible analysis methods than EDA; and statistical indicators provide quantitative evidence for the accuracy of simulation models. By regularly performing such analyses, engineers can systematically evaluate model accuracy, guide model correction, and ultimately improve the design success rate.

[0066] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0067] Based on the same inventive concept, this application also provides a SAW filter design error evaluation system for implementing the multi-dimensional statistical analysis SAW filter design error evaluation method described above. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more embodiments of the multi-dimensional statistical analysis SAW filter design error evaluation system provided below can be found in the limitations of the multi-dimensional statistical analysis SAW filter design error evaluation method described above, and will not be repeated here.

[0068] In one embodiment, such as Figure 24 As shown, a multi-dimensional statistical analysis-based SAW filter design error evaluation system is provided, including:

[0069] The data acquisition module 110 is used to acquire the measured data and simulation data of the SAW filter; the simulation data is obtained by simulation using EDA simulation software.

[0070] The data analysis module 120 is used to analyze and obtain the key evaluation indicators of the SAW filter based on measured data and simulation data. The key evaluation indicators include mean square error, standard deviation, coefficient of variation and variance.

[0071] Error assessment module 130 is used to perform data analysis and statistics based on key assessment indicators, using mean squared error and other key assessment indicators as two dimensions, to obtain error assessment results.

[0072] The modules in the SAW filter design error evaluation system based on the aforementioned multi-dimensional statistical analysis can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0073] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 25 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a multi-dimensional statistical analysis method for evaluating the design error of a SAW filter. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0074] Those skilled in the art will understand that Figure 25The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0075] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0076] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0077] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0078] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0079] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A multi-dimensional statistical analysis method for evaluating the design error of SAW filters, characterized in that, include: The measured and simulated data of the SAW filter were obtained; the simulated data was obtained by simulation using EDA simulation software. Based on the measured data and the simulation data, the key evaluation indicators of the SAW filter are analyzed and obtained; the key evaluation indicators include mean square error, standard deviation, coefficient of variation, and variance. Based on the aforementioned key evaluation indicators, data analysis and statistics were performed using mean squared error and other key evaluation indicators as two dimensions to obtain error evaluation results.

2. The method according to claim 1, characterized in that, Obtain measured and simulation data of the SAW filter, including: obtain measured data of the SAW filter and simulation data output by EDA simulation software, and import them into Python programming software for key evaluation index analysis.

3. The method according to claim 1, characterized in that, Both the measured data and the simulated data include S-parameters and the corresponding finger spacing data; Based on the measured data and the simulation data, the key evaluation indicators of the SAW filter are analyzed and obtained, including: Where MSE is the mean squared error, Y measured,i Let Y be the S-parameter of the i-th actual measurement. simulated,i Let S be the S-parameter of the i-th simulation, and n be the total number of S-parameters.

4. The method according to claim 3, characterized in that, Based on the measured data and the simulation data, the key evaluation indicators of the SAW filter were analyzed, including: Where s is the sample standard deviation, n is the total number of finger spacings, and X i For each finger spacing value, μ is the overall average of the finger spacing.

5. The method according to claim 3, characterized in that, Based on the measured data and the simulation data, the key evaluation indicators of the SAW filter were analyzed, including: Where CV is the coefficient of variation, σ is the standard deviation of the strip spacing, and μ is the overall average value of the strip spacing.

6. The method according to claim 3, characterized in that, Based on the measured data and the simulation data, the key evaluation indicators of the SAW filter were analyzed, including: Where, σ 2 The total variance is N, where N is the total number of intervals, and X is the total variance. i It is the value of the spacing between each finger bar, and μ is the overall average value of the spacing between the bars.

7. The method according to any one of claims 1 to 6, characterized in that, Based on the aforementioned key evaluation indicators, data analysis and statistics were performed using mean squared error and other key evaluation indicators as two dimensions to obtain error evaluation results, including: The mean squared error was plotted on the ordinate, and the standard deviation, coefficient of variation, and variance were plotted on the abscissa to obtain the variation curve.

8. A multi-dimensional statistical analysis-based SAW filter design error evaluation system, characterized in that, include: The data acquisition module is used to acquire measured and simulated data of the SAW filter; the simulated data is obtained by simulation using EDA simulation software. The data analysis module is used to analyze and obtain the key evaluation indicators of the SAW filter based on the measured data and the simulation data; the key evaluation indicators include mean square error, standard deviation, coefficient of variation and variance; The error assessment module is used to perform data analysis and statistics based on the key assessment indicators, using mean squared error and other key assessment indicators as two dimensions, to obtain the error assessment results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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