Thin-film vacuum gauge optimization method, device, electronic device and storage medium

Abstract

The present invention provides a method, device, electronic device and storage medium for optimizing a thin-film vacuum gauge, which relates to the technical field of optimizing the structure of a vacuum gauge. By means of simulation, a plurality of capacitance data and a plurality of pressure data under different structural parameters are obtained, the sensitivity and linearity corresponding to different structural parameters are calculated, the optimal structural parameters are obtained according to each sensitivity and each linearity, and finally a corresponding simulation model is output according to the optimal structural parameters to obtain a final model, so as to ensure that the selected final model achieves the best balance between sensitivity and linearity.

Description

Technical Field

[0001] The present invention relates to the technical field of vacuum gauge structure optimization, and in particular to a thin film vacuum gauge optimization method, device, electronic equipment and storage medium. Background Art

[0002] Currently, improvements in thin-film vacuum gauge performance rely primarily on experimental methods. However, this approach has drawbacks, such as high experimental costs, long experimental cycles, and difficulty covering all possible operating conditions. Therefore, the introduction of simulation-based improvement methods has become a trend. By combining theoretical analysis with simulation, various factors influencing thin-film vacuum gauge performance, such as film material, electrode structure, and dimensions, can be more deeply studied, leading to more effective improvement solutions. Simulation methods can not only reduce experimental costs and shorten R&D cycles, but also improve design accuracy and reliability. However, when evaluating the performance of thin-film vacuum gauges, the linearity and sensitivity of the capacitance-pressure curve are two key indicators. Linearity reflects the linear relationship between the vacuum gauge output signal and the true pressure value. Good linearity means higher accuracy and reliability of the measurement results. Sensitivity, on the other hand, describes the vacuum gauge's ability to respond to small pressure changes. A highly sensitive vacuum gauge can capture more subtle pressure fluctuations, providing more accurate measurement data. However, these two performance characteristics are mutually exclusive; increasing sensitivity often means decreasing linearity. Therefore, a thin-film vacuum gauge optimization method that can strike a balance between linearity and sensitivity is needed.

[0003] There is currently no effective technical solution to the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a thin film vacuum gauge optimization method, device, electronic device and storage medium, with the aim of finding a set of optimal structural parameters so that the vacuum gauge achieves the best balance between sensitivity and linearity.

[0005] In a first aspect, the present invention provides a thin film vacuum gauge optimization method, comprising the steps of:

[0006] S1. Constructing multiple simulation models of a thin film vacuum gauge based on COMSOL Multiphysics, and performing simulation tests on the multiple simulation models, wherein the multiple simulation models correspond to different structural parameters;

[0007] S2 obtains multiple capacitance data and multiple pressure data under various structural parameters obtained by simulation tests;

[0008] S3 obtains the corresponding sensitivity according to the plurality of capacitance data and the plurality of pressure data under each structural parameter;

[0009] S4. Obtain the corresponding linearity according to the multiple capacitance data and the multiple pressure data under each structural parameter;

[0010] S5. Obtain the optimal structural parameters according to each sensitivity and each linearity;

[0011] S6. Output the corresponding simulation model according to the optimal structural parameters to obtain the final model.

[0012] Through the above settings, a set of optimal structural parameters is found, so that the vacuum gauge achieves the best balance between sensitivity and linearity.

[0013] Further, step S3 includes:

[0014] According to the multiple capacitance data and the multiple pressure data under each structural parameter; calculate the sensitivity using the following formula:

[0015] ;

[0016] Where, = ;

[0017] In the formula, is the sensitivity corresponding to the th structural parameter; is the maximum pressure value among all the pressure data under the th structural parameter; is the minimum pressure value among all the pressure data under the th structural parameter; is the normalization constant, is the maximum capacitance value among all the capacitance data under the th structural parameter; is the minimum capacitance value among all the capacitance data under the th structural parameter, is the maximum pressure value among all the pressure data under the preset structural parameter; is the minimum pressure value among all the pressure data under the preset structural parameter; is the maximum capacitance value among all the capacitance data under the preset structural parameter; is the minimum capacitance value among all the capacitance data under the preset structural parameter.

[0018] Through the above settings, the capacitance data and pressure data obtained by simulation can be converted into quantifiable sensitivity indicators, providing a clear numerical basis for the optimization of subsequent steps and ensuring the effectiveness and reliability of the optimization process.

[0019] Further, step S4 includes:

[0020] According to the multiple capacitance data and the multiple pressure data under each structural parameter, the linearity is calculated using the following formula;

[0021] ;

[0022] where, = ;

[0023] In the formula, is the linearity corresponding to the th structural parameter; is the total number of data points corresponding to each structural parameter. The abscissa of each data point corresponds to one of the pressure data, and the ordinate of each data point corresponds to one of the capacitance data; is the maximum pressure value among all the pressure data under the th structural parameter; is the pressure data of the th data point under the th structural parameter; is the minimum pressure value among all the pressure data under the th structural parameter; is the maximum capacitance value among all the capacitance data under the th structural parameter; is the minimum capacitance value among all the capacitance data under the th structural parameter; is the capacitance data of the th data point under the th structural parameter; is the normalization constant; is the maximum pressure value among all the pressure data under the preset structural parameter; is the minimum pressure value among all the pressure data under the preset structural parameter; is the maximum capacitance value among all the capacitance data under the preset structural parameter; is the minimum capacitance value among all the capacitance data under the preset structural parameter, is the pressure data of the th data point under the preset structural parameter, is the capacitance data of the th data point under the preset structural parameter.

[0024] Through the above settings, the linearity values under different structural parameters are unified to a comparable scale, making it more convenient to optimize.

[0025] Further, step S5 includes:

[0026] Perform a weighted calculation based on each of the sensitivities and each of the linearities and obtain the minimum comprehensive value;

[0027] Obtain the corresponding optimal structural parameters according to the minimum comprehensive value.

[0028] Further, the step of performing a weighted calculation based on each of the sensitivities and each of the linearities and obtaining the minimum comprehensive value includes:

[0029] Based on each of the sensitivities and each of the linearities, calculate the comprehensive value using the following weighted formula:

[0030] ;

[0031] In the formula, is the comprehensive value corresponding to the th structural parameter, is the weight coefficient; is the weight coefficient; is the sensitivity corresponding to the th structural parameter; is the linearity corresponding to the th structural parameter;

[0032] Compare each of the comprehensive values to obtain the minimum comprehensive value.

[0033] Further, step S1 includes:

[0034] S11. Based on COMSOL Multiphysics, set the initial model;

[0035] S12. Perform parametric modeling based on the initial model to obtain multiple simulation models for simulation tests.

[0036] Further, the specific steps of step S12 include:

[0037] S121. Set the material properties;

[0038] S122. Determine the physical field configuration;

[0039] S123. Configure the steady-state solver and perform a simulation test;

[0040] S124. Add a structural parameter scan and preset the structural parameters to be scanned and the structural parameter scan range of variation;

[0041] S125. Perform a structural parameter scan and export all the capacitance data and all the pressure data corresponding to each structural parameter.

[0042] In a second aspect, the present invention provides a thin-film vacuum gauge optimization device, comprising:

[0043] A modeling module, configured to build a plurality of simulation models of the thin-film vacuum gauge based on COMSOL Multiphysics and perform simulation tests on the plurality of simulation models, wherein the plurality of simulation models respectively correspond to different structural parameters;

[0044] A first acquisition module, configured to acquire a plurality of capacitance data and a plurality of pressure data under various different structural parameters obtained from the simulation tests;

[0045] A second acquisition module, configured to obtain corresponding sensitivities according to the plurality of capacitance data and the plurality of pressure data under each structural parameter;

[0046] A third acquisition module, configured to obtain corresponding linearities according to the plurality of capacitance data and the plurality of pressure data under each structural parameter;

[0047] A fourth acquisition module, configured to obtain optimal structural parameters according to each of the sensitivities and each of the linearities;

[0048] An output module, configured to output the corresponding simulation model according to the optimal structural parameter to obtain a final model.

[0049] The thin-film vacuum gauge optimization device provided by the present invention effectively solves the problem of low efficiency of manual operation, shortens the optimization cycle, and improves the optimization efficiency and accuracy.

[0050] Through the collaborative work among the various modules, the problem of low efficiency of manual operation is effectively solved, the optimization cycle is shortened, and the optimization efficiency and accuracy are improved.

[0051] In a third aspect, the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the thin-film vacuum gauge optimization method provided in the first aspect as described above are run.

[0052] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the thin-film vacuum gauge optimization method provided in the first aspect as described above are run.

[0053] As can be seen from the above, the thin-film vacuum gauge optimization method, device, electronic device and storage medium provided by the present invention obtain a plurality of capacitance data and a plurality of pressure data under different structural parameters through simulation, calculate the sensitivity and linearity corresponding to different structural parameters, optimize according to each sensitivity and each linearity to obtain the optimal structural parameters, and finally output the corresponding simulation model according to the optimal structural parameters to obtain the final model, so as to ensure that the selected final model achieves the best balance between sensitivity and linearity.

[0054] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will be obvious from the specification, or will be understood by implementing the embodiments of the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written specification and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic flowchart of the thin-film vacuum gauge optimization method in an embodiment of the present invention.

[0056] Figure 2 It is a schematic structural diagram of the thin-film vacuum gauge optimization device in an embodiment of the present invention.

[0057] Figure 3 It is a schematic structural diagram of the electronic device provided by an embodiment of the present invention.

[0058] Figure 4 It is a schematic diagram of obtaining capacitance and pressure data under different structural parameters provided by an embodiment of the present invention.

[0059] Figure 5 It is a schematic structural diagram of the thin-film vacuum gauge of the prior art.

[0060] Reference numeral description: 11, fixed electrode plate; 12, sensing diaphragm; 21, modeling module; 22, first acquisition module; 23, second acquisition module; 24, third acquisition module; 25, fourth acquisition module; 26, output module; 13, electronic device; 1301, processor; 1302, memory; 1303, communication bus. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Components of the embodiments of the present invention described and illustrated herein can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0062] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0063] In a first aspect, the present invention provides an optimization method for a thin-film vacuum gauge, including the steps of:

[0064] S1. Based on COMSOL Multiphysics, construct multiple simulation models of the thin-film vacuum gauge and conduct simulation tests on the multiple simulation models. The multiple simulation models respectively correspond to different structural parameters;

[0065] S2. Obtain multiple capacitance data and multiple pressure data under each structural parameter obtained from the simulation tests;

[0066] S3. Obtain the corresponding sensitivity according to the multiple capacitance data and multiple pressure data under each structural parameter;

[0067] S4. Obtain the corresponding linearity according to the multiple capacitance data and multiple pressure data under each structural parameter;

[0068] S5. Obtain the optimal structural parameters according to each sensitivity and each linearity;

[0069] S6. Output the corresponding simulation model according to the optimal structural parameters to obtain the final model.

[0070] This technical solution provides an optimization method for a thin-film vacuum gauge, aiming to solve the problem that it is difficult to balance the linearity and sensitivity of the thin-film vacuum gauge. This method first establishes multiple simulation models of thin-film vacuum gauges with different structural parameters through COMSOL Multiphysics software and conducts simulation tests to simulate the performance of the vacuum gauge under different structural parameters. Then, capacitance data and pressure data are obtained from the simulation tests, and based on these data, the sensitivity and linearity corresponding to each structural parameter are calculated. Sensitivity reflects the sensitivity of the vacuum gauge to pressure changes, and linearity reflects the linear relationship between the output signal of the vacuum gauge and pressure. Next, optimization is carried out according to the calculated sensitivity and linearity, with the aim of finding a set of optimal structural parameters to achieve the best balance between the sensitivity and linearity of the vacuum gauge. Finally, the simulation model corresponding to the optimal structural parameters is output as the final design solution.

[0071] This technical solution uses simulation means to replace the traditional experimental method, reducing the experimental cost and cycle and being able to achieve a better balance between sensitivity and linearity.

[0072] Among them, the structural parameters can be the thickness of the sensing diaphragm or the plate spacing (i.e., the distance between the sensing diaphragm and the fixed electrode), etc.

[0073] In practical applications, taking the plate spacing (i.e., Figure 5 the straight-line distance between the sensing diaphragm 12 and the fixed plate 11 in the Figure 5 structure) h of the thin-film vacuum gauge of the existing technology (as shown) as an example, the change range of the parameter (plate spacing h) is set according to the actual working range of the designed thin-film vacuum gauge. Through this solution, the capacitance-pressure relationship diagrams under different structural parameters (i.e., different plate spacings, for example, h1 = 3mm, h2 = 3.2mm, h3 = 3.6mm, h4 = 3.8mm, h5 = 4mm) are obtained through simulation, as shown in Figure 4 Then, the capacitance data and pressure data corresponding to each h are subjected to the relevant calculations below to obtain the linearity and sensitivity corresponding to each structural parameter (such as h), and then the optimal structural parameters are obtained based on the linearity and sensitivity. The above is only an example taking the plate spacing. In practical applications, other key structural parameters of the thin-film vacuum gauge can be used for simulation, such as the thickness of the sensing diaphragm 12, but not limited to this.

[0074] In some embodiments, step S3 includes:

[0075] According to multiple capacitance data and multiple pressure data under each structural parameter; the following formula is used to calculate the sensitivity;

[0076] ;

[0077] Among them, = ;

[0078] In the formula, is the sensitivity corresponding to the th structural parameter; is the maximum pressure value among all the said pressure data under the th structural parameter; is the minimum pressure value among all the said pressure data under the th structural parameter; is the normalization constant, is the maximum capacitance value among all the said capacitance data under the th structural parameter; is the minimum capacitance value among all the said capacitance data under the th structural parameter, is the maximum pressure value among all the pressure data under the preset structural parameter; is the minimum pressure value among all the pressure data under the preset structural parameter; is the maximum capacitance value among all the capacitance data under the preset structural parameter; is the minimum capacitance value among all the capacitance data under the preset structural parameter.

[0079] Specifically, by normalizing the current pressure change rate and capacitance change rate with the corresponding values (common benchmark) of the preset structural parameter, the sensitivity values of different structural parameters can be compared under the common benchmark, ensuring the effectiveness and reliability of the optimization process. Therefore, according to the sensitivity calculation formula provided in this application, the simulated capacitance data and pressure data can be converted into quantifiable sensitivity indicators, providing a clear numerical basis for subsequent optimization and ensuring the effectiveness and reliability of the optimization process.

[0080] In some embodiments, step S4 includes:

[0081] According to the multiple capacitance data and multiple pressure data under each structural parameter, calculate the linearity using the following formula;

[0082] ;

[0083] Among them, = ;

[0084] In the formula, is the linearity corresponding to the th structural parameter; is the total number of data points corresponding to each structural parameter. The abscissa of each data point corresponds to a pressure data, and the ordinate of each data point corresponds to a capacitance data; is the maximum pressure value among all the pressure data under the th structural parameter; is the pressure data of the th data point under the th structural parameter; is the minimum pressure value among all the pressure data under the th structural parameter; is the maximum capacitance value among all the capacitance data under the th structural parameter; is the minimum capacitance value among all the capacitance data under the th structural parameter; is the capacitance data of the th data point under the th structural parameter; is the normalization constant; is the maximum pressure value among all the pressure data under the preset structural parameter; is the minimum pressure value among all the pressure data under the preset structural parameter; is the maximum capacitance value among all the capacitance data under the preset structural parameter; is the minimum capacitance value among all the capacitance data under the preset structural parameter, is the pressure data of the th data point under the preset structural parameter, is the capacitance data of the th data point under the preset structural parameter.

[0085] Specifically, by adopting this formula, the linearity of the thin-film vacuum gauge under different structural parameters can be quantitatively evaluated, making the calculation of linearity have a unified standard and avoiding the evaluation deviation caused by different calculation methods. In addition, a normalization constant is introduced into the formula, which is calculated based on the pressure and capacitance data under the preset structural parameter (the preset structural parameter is obtained from the empirical initial model, and the empirical initial model is a basic design model of the thin-film vacuum gauge established based on historical experimental data, industry standards or preliminary simulation verification, and its core purpose is to provide a scientific and reasonable initial value for the optimization of structural parameters). In order to unify the linearity values under different structural parameters to a comparable scale, so as to facilitate optimization more conveniently, and then improve the operability of the thin-film vacuum gauge optimization method and the reliability of the results.

[0086] In some embodiments, step S5 includes:

[0087] Performing weighted calculation according to each sensitivity and each linearity and obtaining the minimum comprehensive value;

[0088] Obtain the corresponding optimal structural parameters according to the minimum comprehensive value.

[0089] Specifically, by weighted calculation, comprehensively considering sensitivity and linearity, and taking the minimum comprehensive value as the optimization goal, it is possible to effectively optimize according to sensitivity and linearity to obtain the optimal structural parameters and achieve the balance between sensitivity and linearity.

[0090] In some embodiments, the steps of performing weighted calculation according to each sensitivity and each linearity and obtaining the minimum comprehensive value include:

[0091] According to each sensitivity and each linearity, calculate the comprehensive value using the following weighted formula:

[0092] ;

[0093] In the formula, is the comprehensive value corresponding to the th structural parameter, is the weight coefficient; is the weight coefficient; is the sensitivity corresponding to the th structural parameter; is the linearity corresponding to the th structural parameter;

[0094] Compare each comprehensive value to obtain the minimum comprehensive value.

[0095] Specifically, first, set weight coefficients for sensitivity and linearity respectively. These two weight coefficients can be adjusted according to the degree of emphasis on sensitivity and linearity in actual applications, reflecting the flexibility of the design. Then, calculate a comprehensive value through the weighted formula. This comprehensive value reflects the overall performance of the structural parameters under the current weight distribution. By comparing the comprehensive values corresponding to different structural parameters and selecting the structural parameter corresponding to the minimum comprehensive value as the optimal structural parameter. This method transforms the multi-objective optimization problem into a single-objective optimization problem, making the optimization process clearer and more operable. By adjusting the weight coefficients, a trade-off can be made between sensitivity and linearity, thereby obtaining the optimal thin-film vacuum gauge structural parameters that meet specific application requirements.

[0096] In some embodiments, step S1 includes:

[0097] S11. Based on COMSOL Multiphysics, set the initial model;

[0098] S12. Perform parametric modeling according to the initial model to obtain multiple simulation models for simulation experiments.

[0099] Specifically, first, a basic initial model is established, which serves as the basis for subsequent parametric modeling. Based on the initial model, parametric modeling technology is used to quickly and batch generate multiple simulation models with different structural parameters by adjusting the model parameters for subsequent simulation tests. This method makes the construction process of the simulation model more systematic and efficient, facilitating parameter scanning and optimization design, and improving the efficiency and feasibility of simulation optimization.

[0100] In some embodiments, the specific steps of step S12 include:

[0101] S121. Set material properties;

[0102] S122. Determine the physical field configuration;

[0103] S123. Configure the steady-state solver and conduct a simulation test;

[0104] S124. Add structural parameter scanning and preset the structural parameters to be scanned and the variation range of the structural parameter scanning;

[0105] S125. Conduct structural parameter scanning and export all capacitance data and all pressure data corresponding to each structural parameter.

[0106] Through the above settings, the parametric modeling steps are made more operable, improving the efficiency of the thin-film vacuum gauge optimization method.

[0107] Among them, COMSOL Multiphysics is a simulation software of the prior art, and the specific simulation process and method will not be elaborated here.

[0108] In a second aspect, the present invention provides a thin-film vacuum gauge optimization device, including:

[0109] A modeling module 21, configured to build multiple simulation models of the thin-film vacuum gauge based on COMSOL Multiphysics and conduct simulation tests on the multiple simulation models, where the multiple simulation models respectively correspond to different structural parameters;

[0110] A first acquisition module 22, configured to acquire multiple capacitance data and multiple pressure data obtained from the simulation test under various different structural parameters;

[0111] A second acquisition module 23, configured to obtain the corresponding sensitivity according to the multiple capacitance data and the multiple pressure data under each structural parameter;

[0112] A third acquisition module 24, configured to obtain the corresponding linearity according to the multiple capacitance data and the multiple pressure data under each structural parameter;

[0113] The fourth acquisition module 25 is configured to obtain optimal structural parameters according to each sensitivity and each linearity;

[0114] The output module 26 is configured to output a corresponding simulation model according to the optimal structural parameters to obtain a final model.

[0115] The thin-film vacuum gauge optimization device provided by the present invention can find a set of optimal structural parameters, so that the vacuum gauge achieves an optimal balance between sensitivity and linearity, effectively solves the problem of low manual operation efficiency, shortens the optimization cycle, and improves the optimization efficiency and accuracy.

[0116] This technical solution provides a method for optimizing a thin-film vacuum gauge, aiming to solve the problem that it is difficult to balance the linearity and sensitivity of the thin-film vacuum gauge. The method first establishes multiple simulation models of thin-film vacuum gauges with different structural parameters through COMSOL Multiphysics software, and conducts simulation experiments to simulate the performance of the vacuum gauge under different structural parameters. Then, capacitance data and pressure data are obtained from the simulation experiments, and the sensitivity and linearity corresponding to each structural parameter are calculated based on these data. Sensitivity reflects the sensitivity of the vacuum gauge to pressure changes, and linearity reflects the linear relationship between the output signal of the vacuum gauge and pressure. Next, optimization is performed according to the calculated sensitivity and linearity, with the aim of finding a set of optimal structural parameters so that the vacuum gauge achieves an optimal balance between sensitivity and linearity. Finally, the simulation model corresponding to the optimal structural parameters is output as the final design scheme.

[0117] This technical solution replaces the traditional experimental method through simulation means, reduces the experimental cost and cycle, and can achieve a good balance between sensitivity and linearity.

[0118] Among them, the structural parameters can be the thickness of the diaphragm or the distance between the diaphragm and the fixed electrode, etc.

[0119] In practical applications, taking the plate spacing (i.e., Figure 5 the straight-line distance between the induction diaphragm 12 and the fixed plate 11 in the Figure 5 structure) h of the thin-film vacuum gauge of the existing technology (as shown in Figure 4As shown, then perform the relevant calculations below on the capacitance data and pressure data corresponding to each h, and the linearity and sensitivity corresponding to each structural parameter (such as h) can be obtained. Furthermore, the optimal structural parameter can be obtained based on the linearity and sensitivity. The above is only an example with the plate spacing. In actual applications, other key structural parameters of the thin-film vacuum gauge can be used for simulation, such as the thickness of the sensing diaphragm 12, but not limited to this.

[0120] In some embodiments, when the second acquisition module 23 executes to obtain the corresponding sensitivity according to multiple capacitance data and multiple pressure data under each structural parameter, it specifically executes:

[0121] According to the multiple capacitance data and multiple pressure data under each structural parameter; calculate the sensitivity using the following formula;

[0122] ;

[0123] Wherein, = ;

[0124] In the formula, is the sensitivity corresponding to the th structural parameter; is the maximum pressure value among all the pressure data under the th structural parameter; is the minimum pressure value among all the pressure data under the th structural parameter; is the normalization constant, is the maximum capacitance value among all the capacitance data under the th structural parameter; is the minimum capacitance value among all the capacitance data under the th structural parameter, is the maximum pressure value among all the pressure data under the preset structural parameter; is the minimum pressure value among all the pressure data under the preset structural parameter; is the maximum capacitance value among all the capacitance data under the preset structural parameter; is the minimum capacitance value among all the capacitance data under the preset structural parameter.

[0125] Specifically, by normalizing the current pressure change rate and capacitance change rate with the corresponding values (common benchmark) of the preset structural parameters, the sensitivity values of different structural parameters can be compared under the common benchmark, ensuring the effectiveness and reliability of the optimization process. Therefore, according to the sensitivity calculation formula provided in this application, the capacitance data and pressure data obtained by simulation can be converted into quantifiable sensitivity indicators, providing a clear numerical basis for subsequent optimization and ensuring the effectiveness and reliability of the optimization process.

[0126] In some embodiments, when the third acquisition module 24 executes to obtain the corresponding linearity according to multiple capacitance data and multiple pressure data under each structural parameter, it specifically executes:

[0127] According to multiple capacitance data and multiple pressure data under each structural parameter, calculate the linearity using the following formula;

[0128] ;

[0129] Wherein, = ;

[0130] In the formula, is the linearity corresponding to the th structural parameter; is the total number of data points corresponding to each structural parameter. The abscissa of each data point corresponds to a pressure data, and the ordinate of each data point corresponds to a capacitance data; is the maximum pressure value among all pressure data under the th structural parameter; is the pressure data of the th data point under the th structural parameter; is the minimum pressure value among all pressure data under the th structural parameter; is the maximum capacitance value among all capacitance data under the th structural parameter; [[ID=*46]] is the minimum capacitance value among all capacitance data under the th structural parameter; is the capacitance data of the th data point under the th structural parameter; is the normalization constant; is the maximum pressure value among all pressure data under the preset structural parameters; is the minimum pressure value among all pressure data under the preset structural parameters; is the maximum capacitance value corresponding to all capacitance data under the preset structural parameters; is the minimum capacitance value among all capacitance data under the preset structural parameters, is the pressure data of the th data point under the preset structural parameters, is the capacitance data of the th data point under the preset structural parameters.

[0131] Specifically, by adopting this formula, the linearity of the thin-film vacuum gauge under different structural parameters can be quantitatively evaluated, making the calculation of linearity have a unified standard and avoiding evaluation deviations caused by different calculation methods. In addition, a normalization constant is introduced into the formula. This normalization constant is calculated based on the pressure and capacitance data under the preset structural parameters (the preset structural parameters are obtained from the empirical initial model, which is a basic design model of the thin-film vacuum gauge established based on historical experimental data, industry standards or preliminary simulation verification. Its core purpose is to provide scientific and reasonable initial values for structural parameter optimization). In order to unify the linearity values under different structural parameters to a comparable scale, thus making optimization more convenient, and further improving the operability of the thin-film vacuum gauge optimization method and the reliability of the results.

[0132] In some embodiments, when the fourth acquisition module 25 executes to obtain the optimal structural parameters according to each sensitivity and each linearity, it specifically executes:

[0133] Perform weighted calculation according to each sensitivity and each linearity and obtain the minimum comprehensive value;

[0134] Obtain the corresponding optimal structural parameters according to the minimum comprehensive value.

[0135] Specifically, by weighted calculation, sensitivity and linearity are comprehensively considered, and with the minimum comprehensive value as the optimization goal, it is possible to effectively optimize according to sensitivity and linearity to obtain the optimal structural parameters and achieve the balance between sensitivity and linearity.

[0136] In some embodiments, when the fourth acquisition module 25 executes to perform weighted calculation according to each sensitivity and each linearity and obtain the minimum comprehensive value, it specifically executes:

[0137] According to each sensitivity and each linearity, calculate the comprehensive value by using the following weighted formula:

[0138] ;

[0139] In the formula, is the comprehensive value corresponding to the th structural parameter, is the weight coefficient; is the weight coefficient; is the The sensitivity corresponding to a structural parameter; For the linearity corresponding to a structural parameter;

[0140] Compare each comprehensive value to obtain the minimum comprehensive value.

[0141] Specifically, first, set weight coefficients for sensitivity and linearity respectively. These two weight coefficients can be adjusted according to the degree of emphasis on sensitivity and linearity in actual applications, reflecting the flexibility of the design. Then, calculate a comprehensive value through a weighted formula. This comprehensive value reflects the overall performance of the structural parameter under the current weight distribution. By comparing the comprehensive values corresponding to different structural parameters and selecting the structural parameter corresponding to the minimum comprehensive value as the optimal structural parameter. This method transforms the multi-objective optimization problem into a single-objective optimization problem, making the optimization process clearer and more operable. By adjusting the weight coefficients, a trade-off can be made between sensitivity and linearity, thereby obtaining the optimal structural parameters of the thin-film vacuum gauge that meet specific application requirements.

[0142] In some embodiments, when the modeling module 21 executes constructing multiple simulation models of the thin-film vacuum gauge based on COMSOL Multiphysics and performing simulation tests on the multiple simulation models, where the multiple simulation models respectively correspond to different structural parameters, it specifically executes:

[0143] S11. Based on COMSOL Multiphysics, set the initial model;

[0144] S12. Perform parametric modeling based on the initial model to obtain multiple simulation models for simulation tests.

[0145] Specifically, first establish a basic initial model, which serves as the basis for subsequent parametric modeling. Based on the initial model, use parametric modeling technology to quickly and batch generate multiple simulation models with different structural parameters for subsequent simulation tests through adjusting model parameters. This method makes the construction process of the simulation model more systematic and efficient, facilitating parameter scanning and optimization design, and improving the efficiency and feasibility of simulation optimization.

[0146] In some embodiments, when the modeling module 21 executes performing parametric modeling based on the initial model to obtain multiple simulation models for simulation tests, it specifically executes:

[0147] S121. Set the material properties;

[0148] S122. Determine the physical field configuration;

[0149] S123. Configure the steady-state solver and perform simulation tests;

[0150] S124. Add structure parameter scanning, preset the structure parameters to be scanned, and the variation range of the structure parameter scanning;

[0151] S125. Conduct structure parameter scanning and export all capacitance data and all pressure data corresponding to each structure parameter.

[0152] Through the above settings, the parametric modeling steps are made more operable, and the efficiency of the thin-film vacuum gauge optimization method is improved.

[0153] Among them, COMSOL Multiphysics is a simulation software of the prior art, and the specific simulation process and method will not be elaborated here.

[0154] Please refer to Figure 3 , Figure 3 FIG.

[0155] It should be noted that in the translation of the content in ID=17, the original text seems to be incomplete. The translated "FIG." is a placeholder for the complete figure reference that should be filled in according to the actual situation.An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it executes the thin-film vacuum gauge optimization method in any optional implementation manner of the above embodiment to achieve the following functions: constructing a plurality of simulation models of the thin-film vacuum gauge based on COMSOL Multiphysics, and performing simulation tests on the plurality of simulation models, where the plurality of simulation models respectively correspond to different structural parameters; obtaining a plurality of capacitance data and a plurality of pressure data under each structural parameter obtained from the simulation tests; obtaining the corresponding sensitivity according to the plurality of capacitance data and the plurality of pressure data under each structural parameter; obtaining the corresponding linearity according to the plurality of capacitance data and the plurality of pressure data under each structural parameter; obtaining the optimal structural parameter according to each sensitivity and each linearity; and outputting the corresponding simulation model according to the optimal structural parameter to obtain the final model.

[0156] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0157] In the embodiments provided by the present invention, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical, or other form.

[0158] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or may be distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] Furthermore, in each embodiment of the present invention, the functional modules may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

[0160] In this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0161] The above are only the embodiments of the present invention and are not used to limit the protection scope of the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An optimization method for a thin-film vacuum gauge, characterized in that Including the steps: S1. Based on COMSOL Multiphysics, construct multiple simulation models of a thin-film vacuum gauge, and conduct simulation tests on the multiple simulation models, where the multiple simulation models respectively correspond to different structural parameters; S2. Obtain multiple capacitance data and multiple pressure data under each structural parameter obtained from the simulation tests; S3. Obtain the corresponding sensitivity according to the multiple capacitance data and the multiple pressure data under each structural parameter; S4. Obtain the corresponding linearity according to the multiple capacitance data and the multiple pressure data under each structural parameter; S5. Obtain the optimal structural parameter according to each of the sensitivities and each of the linearities; Step S5 includes: Perform weighted calculation according to each of the sensitivities and each of the linearities and obtain the minimum comprehensive value; Obtain the corresponding optimal structural parameter according to the minimum comprehensive value; The step of performing weighted calculation according to each of the sensitivities and each of the linearities and obtaining the minimum comprehensive value includes: According to each of the sensitivities and each of the linearities, calculate the comprehensive value using the following weighted formula: ； In the formula, is the comprehensive value corresponding to the th structural parameter, is the weight coefficient; is the weight coefficient; is the sensitivity corresponding to the th structural parameter; is the linearity corresponding to the th structural parameter. Compare each of the comprehensive values to obtain the minimum comprehensive value; S6. Output the corresponding simulation model according to the optimal structural parameter to obtain the final model.

2. The method for optimizing a thin-film vacuum gauge according to claim 1, wherein Step S3 includes: According to the multiple capacitance data and the multiple pressure data under each structural parameter, calculate the sensitivity using the following formula; ； Among them, = ; In the formula, is the sensitivity corresponding to the th structural parameter; is the maximum pressure value among all the said pressure data under the th structural parameter; is the minimum pressure value among all the said pressure data under the th structural parameter; is the normalization constant, is the maximum capacitance value among all the said capacitance data under the th structural parameter; is the minimum capacitance value among all the said capacitance data under the th structural parameter, is the maximum pressure value among all the pressure data under the preset structural parameter; is the minimum pressure value among all the pressure data under the preset structural parameter; is the maximum capacitance value among all the capacitance data under the preset structural parameter; is the minimum capacitance value among all the capacitance data under the preset structural parameter.

3. The thin-film vacuum gauge optimization method according to claim 1, characterized in that, Step S4 includes: According to the multiple capacitance data and the multiple pressure data under each structural parameter, calculate the linearity using the following formula; ； Among them, = ; Where, For the The linearity corresponding to the structural parameters; The total number of data points corresponding to each structural parameter, the abscissa of each data point corresponds to one pressure data, and the ordinate of each data point corresponds to one capacitance data; For the The maximum pressure value among all the pressure data under the structural parameters; For the The first structural parameter The pressure data of data points; For the The minimum pressure value among all the pressure data under the structural parameters; For the The maximum capacitance value among all the capacitance data under the structural parameters; For the The minimum capacitance value among all the capacitance data under the structural parameters; For the The first structural parameter The capacitance data of data points; is the normalization constant; It is the maximum pressure value among all pressure data under the preset structural parameters; It is the minimum pressure value among all pressure data under the preset structural parameters; is the maximum capacitance value among all capacitance data under the preset structural parameters; is the minimum capacitance value among all capacitance data under the preset structural parameters. The first The pressure data of data points, The first The capacitance data of each data point.

4. The method for optimizing a thin-film vacuum gauge according to claim 1, characterized in that, Step S1 includes: S11. Based on COMSOL Multiphysics, set the initial model; S12. Perform parametric modeling according to the initial model to obtain the multiple simulation models for simulation tests.

5. The thin-film vacuum gauge optimization method according to claim 4, characterized in that, The specific steps of step S12 include: S121. Set the material properties; S122. Determine the physical field configuration; S123. Configure the steady-state solver and conduct simulation tests; S124. Add structural parameter scanning and preset the structural parameters to be scanned and the structural parameter scanning range of variation; S125. Conduct structural parameter scanning and export all capacitance data and all pressure data corresponding to each structural parameter.

6. An optimized device for a thin-film vacuum gauge, characterized in that, Including: A modeling module, configured to construct multiple simulation models of a thin-film vacuum gauge based on COMSOL Multiphysics, and conduct simulation tests on the multiple simulation models, where the multiple simulation models respectively correspond to different structural parameters; A first acquisition module, configured to obtain multiple capacitance data and multiple pressure data under each different structural parameter obtained from the simulation tests; A second acquisition module, configured to obtain the corresponding sensitivity according to the multiple capacitance data and the multiple pressure data under each structural parameter; A third acquisition module, configured to obtain the corresponding linearity according to the multiple capacitance data and the multiple pressure data under each structural parameter; A fourth acquisition module, configured to obtain the optimal structural parameter according to each of the sensitivities and each of the linearities; When the fourth acquisition module executes to obtain the optimal structural parameters according to each of the sensitivities and each of the linearities, it specifically executes: Perform weighted calculation according to each of the sensitivities and each of the linearities and obtain the minimum comprehensive value; Obtain the corresponding optimal structural parameters according to the minimum comprehensive value; When the fourth acquisition module executes the weighted calculation according to each of the sensitivities and each of the linearities and obtains the minimum comprehensive value, it specifically executes: According to each of the sensitivities and each of the linearities, calculate the comprehensive value using the following weighted formula: ； In the formula, is the comprehensive value corresponding to the th structural parameter, is the weight coefficient; is the weight coefficient; is the sensitivity corresponding to the th structural parameter; is the linearity corresponding to the th structural parameter. Compare each of the comprehensive values to obtain the minimum comprehensive value; An output module, configured to output the corresponding simulation model according to the optimal structural parameters to obtain a final model.

7. An electronic device, characterized in that, It includes a processor and a memory, and the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the thin-film vacuum gauge optimization method according to any one of claims 1-5 are run.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps in the thin-film vacuum gauge optimization method according to any one of claims 1-5 are run.

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