Efficient preparation method and system for low zero drift pressure chip based on SOI

Abstract

The present invention relates to the field of chip preparation technology, and discloses a method and system for efficiently preparing a low-zero-drift pressure chip based on SOI. The method comprises: analyzing the material performance parameters corresponding to existing preparation materials, screening out synergistic preparation materials corresponding to the existing preparation materials from a preset material library; performing finite element simulation on the pressure chip to obtain a pressure chip simulation model, and calculating the structural deflection corresponding to the pressure chip simulation model; configuring simulated environmental conditions for the pressure chip, calculating the zero-point drift dispersion corresponding to the pressure chip simulation model; calculating the chip temperature drift coefficient corresponding to the pressure chip simulation model; determining the efficient preparation material corresponding to the pressure chip from the synergistic preparation materials and existing preparation materials, performing parameter optimization on the chip preparation path to obtain a low-zero-drift preparation path, executing the preparation process on the pressure chip, and obtaining a finished chip. The present invention can improve the preparation efficiency of SOI low-zero-drift pressure chips.

Description

Technical Field

[0001] The present invention relates to an efficient preparation method and system for a low zero drift pressure chip based on SOI, belonging to the technical field of chip preparation. Background Art

[0002] SOI's low zero-drift pressure chip is a sensor chip manufactured using SOI technology. It has low zero-drift characteristics and can convert pressure signals into electrical signals with high precision and high stability. It is widely used in various fields that require pressure measurement, such as monitoring of aircraft air pressure and hydraulic systems in the aerospace field, and detecting tire pressure and oil pressure in the automotive industry. Therefore, the high-efficiency performance of SOI's low zero-drift pressure chip is extremely high.

[0003] At present, the efficient preparation of low zero-drift pressure chips based on SOI often adopts MEMS technology combined with specific design. First, a suitable SOI wafer is selected, and silicon stress film, varistor and other structures are produced through processes such as thermal oxidation, photolithography etching, and ion implantation. Then, a passivation layer is grown and electrodes are produced. Finally, the SOI sheet is sealed to the glass sheet through anodic bonding to form a sealed structure. However, the overall process operation of this method is complicated during use, resulting in high preparation costs. In addition, this method does not consider the performance drift caused by the internal force and temperature factors of the chip, which in turn causes the low zero-drift pressure chip to be unable to operate efficiently and stably. Therefore, a method that can improve the preparation efficiency of SOI low zero-drift pressure chips is needed. Summary of the Invention

[0004] The present invention provides a method and system for efficiently preparing a low zero drift pressure chip based on SOI, the main purpose of which is to improve the preparation efficiency of the low zero drift pressure chip of SOI.

[0005] To achieve the above objectives, the present invention provides an efficient method for preparing a low zero-drift pressure chip based on SOI, comprising:

[0006] Obtain existing preparation materials and chip preparation paths for a low zero drift pressure chip, analyze material performance parameters corresponding to the existing preparation materials, and based on the material performance parameters, screen out collaborative preparation materials corresponding to the existing preparation materials from a preset material library;

[0007] Combining the existing preparation materials and the collaborative preparation materials, performing finite element simulation processing on the pressure chip to obtain a pressure chip simulation model, and calculating the structural deflection corresponding to the pressure chip simulation model;

[0008] querying a chip working environment of the pressure chip, configuring a simulated environmental condition of the pressure chip based on the chip working environment, performing a zero-point test on the pressure chip simulation model under the simulated environmental condition to obtain a zero-point test value, and calculating a zero-point drift dispersion corresponding to the pressure chip simulation model based on the zero-point test value;

[0009] Performing a temperature characteristic test on the pressure chip simulation model to obtain a temperature response value, and calculating a chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value;

[0010] In combination with the structural deflection, the zero drift discreteness and the chip temperature drift coefficient, the high-efficiency preparation material corresponding to the pressure chip is determined from the collaborative preparation materials and the existing preparation materials, and the parameters of the chip preparation path are optimized to obtain a low zero drift preparation path. In combination with the high-efficiency preparation material and the low zero drift preparation path, the preparation process of the pressure chip is performed to obtain a chip finished product.

[0011] Optionally, analyzing the material performance parameters corresponding to the existing prepared material includes:

[0012] Performing component analysis on the existing preparation material to obtain the preparation material components, and screening out the characterizing material components in the preparation material components;

[0013] Collecting the material microstructure corresponding to the existing prepared material, and analyzing the material structure performance corresponding to the existing prepared material based on the material microstructure;

[0014] Measuring thermal performance parameters and electrical performance parameters corresponding to the existing prepared material, and screening out characterizing performance parameters from the thermal performance parameters and the electrical performance parameters to obtain thermal characterizing parameters and electrical characterizing parameters;

[0015] The material performance parameters corresponding to the existing prepared material are generated by combining the characterized material composition, the material structural performance, the thermal characterization parameters and the electrical characterization parameters.

[0016] Optionally, the selecting, based on the material performance parameters, a collaborative preparation material corresponding to the existing preparation material from a preset material library includes:

[0017] Identifying a performance parameter label corresponding to each parameter in the material performance parameters;

[0018] Based on the performance parameter labels, screening out collaborative candidate materials from a preset material library, and retrieving candidate material performance parameters corresponding to the collaborative candidate materials;

[0019] Calculating the parameter matching degree between the material performance parameters and the candidate material performance parameters, and calculating the material economic expenditure corresponding to the collaborative candidate material;

[0020] In combination with the parameter matching degree and the material economic expenditure, the collaborative preparation material corresponding to the existing preparation material is screened out from the collaborative candidate materials.

[0021] Optionally, calculating the structural deflection corresponding to the pressure chip simulation model includes:

[0022] Applying a uniform load to the pressure chip simulation model to perform a tensile test, and recording the model's transverse strain and the model's longitudinal strain under the uniform load;

[0023] Measuring model size parameters corresponding to the pressure chip simulation model, wherein the model size parameters include: diaphragm geometric radius, diaphragm position radius, and upper plate axial size;

[0024] Calculate the model bending stiffness corresponding to the pressure chip simulation model based on the model transverse strain, the model longitudinal strain, and the axial dimension of the upper plate;

[0025] The structural deflection corresponding to the pressure chip simulation model is calculated in combination with the diaphragm geometric radius, the diaphragm position radius, the model bending stiffness and the uniform load.

[0026] Optionally, the calculating of the model bending stiffness corresponding to the pressure chip simulation model by combining the model transverse strain, the model longitudinal strain, and the axial dimension of the upper plate includes:

[0027] querying the material Poisson's ratio corresponding to each model in the pressure chip simulation model, and calculating the model Poisson's ratio corresponding to the pressure chip simulation model based on the material Poisson's ratio;

[0028] Calculating a model elastic coefficient corresponding to the pressure chip simulation model based on the model transverse strain and the model longitudinal strain;

[0029] Combining the model elastic coefficient, the axial dimension of the upper plate and the model Poisson's ratio, the model bending stiffness corresponding to the pressure chip simulation model is calculated using the following formula:

[0030] ;

[0031] Among them, A represents the model bending stiffness corresponding to the pressure chip simulation model, B represents the model elastic coefficient, d represents the axial size of the upper plate, and b represents the model Poisson's ratio.

[0032] Optionally, the calculating the structural deflection corresponding to the pressure chip simulation model by combining the diaphragm geometric radius, the diaphragm position radius, the model bending stiffness, and the uniform load includes:

[0033] The structural deflection corresponding to the pressure chip simulation model is calculated using the following formula:

[0034] ;

[0035] Where D represents the structural deflection corresponding to the pressure chip simulation model, F represents the uniform load, A represents the model bending stiffness, R represents the diaphragm geometric radius, and r represents the diaphragm position radius.

[0036] Optionally, calculating the zero-point drift discreteness corresponding to the pressure chip simulation model based on the zero-point test value includes:

[0037] drawing a test value curve corresponding to the zero-point test value, and identifying abnormal test values among the zero-point test values based on the test value curve;

[0038] Cleaning the abnormal test value in the zero-point test value to obtain a target zero-point test value;

[0039] Calculate the average value corresponding to the target zero-point test value to obtain the zero-point test mean;

[0040] Combining the zero-point test value and the zero-point test mean, the zero-point drift dispersion corresponding to the pressure chip simulation model is calculated using the following formula:

[0041] ;

[0042] Among them, G represents the zero drift discreteness corresponding to the pressure chip simulation model, Indicates the e-th test value in the target zero test value, represents the mean of the zero-point test, e represents the serial number of the target zero-point test value, and q represents the number of target zero-point test values.

[0043] Optionally, calculating a chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value includes:

[0044] Constructing a temperature response scatter plot corresponding to the temperature response value, and performing linear fitting processing on the temperature response scatter plot to obtain a temperature response fitting curve;

[0045] Smoothing the temperature response fitting curve to obtain a smoothed temperature response curve;

[0046] Based on the smooth temperature response curve, constructing a temperature response fitting equation corresponding to the pressure chip simulation model;

[0047] Dispatching historical temperature response data of the pressure chip, and performing equation correction on the temperature response fitting equation based on the historical temperature response data to obtain a target temperature response equation;

[0048] Derivative processing is performed on the target temperature response equation to obtain a temperature sensitive equation corresponding to the pressure chip simulation model;

[0049] Determining a temperature range corresponding to the pressure chip simulation model based on the temperature response value;

[0050] Based on the temperature range, the chip temperature drift coefficient corresponding to the pressure chip simulation model is calculated using the temperature sensitive equation.

[0051] Optionally, the performing parameter optimization on the chip preparation path to obtain a low zero drift preparation path includes:

[0052] Obtaining the fabrication process parameters in the chip fabrication path, and analyzing the parameter sensitivity corresponding to the fabrication process parameters;

[0053] Based on the parameter sensitivity, screening out key preparation parameters among the preparation process parameters;

[0054] Analyzing drift correlations corresponding to the key preparation parameters, and determining, based on the drift correlations, preparation parameters to be optimized among the key preparation parameters;

[0055] Analyze the feasibility of the parameters corresponding to the preparation parameters to be optimized, and query the preparation equipment corresponding to the preparation parameters to be optimized;

[0056] In combination with the preparation equipment and the feasibility of the parameters, setting the constraint conditions of the preparation parameters to be optimized;

[0057] Under the constraints, performing parameter optimization processing on the preparation parameters to be optimized to obtain an optimal combination of parameters;

[0058] Based on the optimal combination of parameters, the chip preparation path is updated with parameters to obtain a low zero drift preparation path.

[0059] In order to solve the above problems, the present invention also provides an efficient preparation system for a low zero drift pressure chip based on SOI, the system comprising:

[0060] A collaborative preparation material screening module is used to obtain existing preparation materials and chip preparation paths of low zero drift pressure chips, analyze material performance parameters corresponding to the existing preparation materials, and based on the material performance parameters, screen out collaborative preparation materials corresponding to the existing preparation materials from a preset material library;

[0061] a structural deflection calculation module, configured to perform finite element simulation processing on the pressure chip in combination with the existing preparation material and the collaborative preparation material to obtain a pressure chip simulation model, and calculate the structural deflection corresponding to the pressure chip simulation model;

[0062] a zero-point drift dispersion calculation module, configured to query the chip working environment of the pressure chip, configure the simulated environmental conditions of the pressure chip based on the chip working environment, perform a zero-point test on the pressure chip simulation model under the simulated environmental conditions to obtain a zero-point test value, and calculate the zero-point drift dispersion corresponding to the pressure chip simulation model based on the zero-point test value;

[0063] a chip temperature drift coefficient calculation module, configured to perform temperature characteristic test processing on the pressure chip simulation model to obtain a temperature response value, and calculate a chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value;

[0064] A chip preparation module is used to determine the high-efficiency preparation material corresponding to the pressure chip from the collaborative preparation materials and the existing preparation materials in combination with the structural deflection, the zero drift discreteness and the chip temperature drift coefficient, perform parameter optimization on the chip preparation path, obtain a low zero drift preparation path, combine the high-efficiency preparation material and the low zero drift preparation path, perform preparation processing on the pressure chip, and obtain a chip finished product.

[0065] Compared with the problems described in the background technology, the present invention can understand the characteristics of the existing preparation materials by analyzing the material performance parameters corresponding to the existing preparation materials, and provide a reliable basis for screening collaborative preparation materials. Furthermore, the present invention combines the existing preparation materials and the collaborative preparation materials to perform finite element simulation on the pressure chip, and can obtain a chip model constructed by different material combinations, thereby providing an important basis for the subsequent calculation of the structural deflection corresponding to the pressure chip simulation model. The present invention can reproduce the actual working scene of the pressure chip by configuring the simulated environmental conditions of the pressure chip, laying a foundation for subsequent testing. Under the simulated environmental conditions, the pressure chip simulation model is subjected to a zero-point test to obtain a zero-point test value, thereby providing data support for the subsequent calculation of the zero-point drift discreteness corresponding to the pressure chip simulation model, and further In the first step, the present invention calculates the chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value. The chip temperature drift coefficient can be used to understand the performance stability of the pressure chip simulation model under different temperature environments and the sensitivity to temperature, thereby providing a basis for the subsequent screening of efficient preparation materials corresponding to the pressure chip. Furthermore, the present invention determines the efficient preparation material corresponding to the pressure chip from the collaborative preparation materials and the existing preparation materials by combining the structural deflection, the zero drift discreteness and the chip temperature drift coefficient, and can screen out materials that meet the chip performance requirements, providing an important basis for high-performance chip manufacturing; the chip preparation path is parameter optimized to obtain a low zero drift preparation path, which can accurately control the process parameters, effectively suppress the chip zero drift phenomenon during the production process, and greatly improve the chip performance stability. Therefore, the SOI-based low zero drift pressure chip efficient preparation method and system provided in the embodiment of the present invention can improve the preparation efficiency of SOI low zero drift pressure chips. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic flow chart of an efficient method for preparing a low zero-drift pressure chip based on SOI according to an embodiment of the present invention;

[0067] Figure 2 A schematic diagram of a module for realizing an efficient method for preparing a low zero-drift pressure chip based on SOI provided in one embodiment of the present invention.

[0068] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0069] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0070] The embodiment of the present application provides an efficient method for preparing a low-zero-drift pressure chip based on SOI. The execution subject of the efficient method for preparing a low-zero-drift pressure chip based on SOI includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the efficient method for preparing a low-zero-drift pressure chip based on SOI can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0071] Example 1:

[0072] Reference Figure 1 FIG. 1 is a flow chart of an efficient method for preparing a low zero drift pressure chip based on SOI according to an embodiment of the present invention. In this embodiment, the efficient method for preparing a low zero drift pressure chip based on SOI includes:

[0073] S1. Obtain existing preparation materials and chip preparation paths for a low zero drift pressure chip, analyze material performance parameters corresponding to the existing preparation materials, and based on the material performance parameters, screen out collaborative preparation materials corresponding to the existing preparation materials from a preset material library.

[0074] By analyzing the material performance parameters corresponding to the existing preparation materials, the present invention can understand the characteristics of the existing preparation materials, and provide a reliable basis for screening collaborative preparation materials. Among them, the low zero drift pressure chip is a chip with low zero drift characteristics and can accurately measure pressure. The existing preparation materials are various raw materials used in the current preparation process of the pressure chip. The chip preparation path is the entire manufacturing process and process method of the pressure chip from raw materials to finished products. The material performance parameters are quantitative indicators corresponding to the existing preparation materials for characterizing their physical, chemical, mechanical and other aspects.

[0075] As an embodiment of the present invention, the analyzing of material performance parameters corresponding to the existing prepared material includes:

[0076] Performing component analysis on the existing preparation material to obtain the preparation material components, and screening out the characterizing material components in the preparation material components;

[0077] Collecting the material microstructure corresponding to the existing prepared material, and analyzing the material structure performance corresponding to the existing prepared material based on the material microstructure;

[0078] Measuring thermal performance parameters and electrical performance parameters corresponding to the existing prepared material, and screening out characterizing performance parameters from the thermal performance parameters and the electrical performance parameters to obtain thermal characterizing parameters and electrical characterizing parameters;

[0079] The material performance parameters corresponding to the existing prepared material are generated by combining the characterized material composition, the material structural performance, the thermal characterization parameters and the electrical characterization parameters.

[0080] Among them, the preparation material component is the specific embodiment of the composition of the existing preparation material, the characterization material component is the representative part of the preparation material component or the part that can be used for specific analysis, the material microstructure is the structural morphology of the existing preparation material at the microscopic level, the material structural performance is the performance of the existing preparation material based on its structure, the thermal performance parameters and the electrical performance parameters are respectively the thermal and electrical performance indicators of the existing preparation material, and the thermal characterization parameters and the electrical characterization parameters are respectively specific parameters of the thermal performance parameters and the electrical performance parameters that can be used to characterize the corresponding properties of the material.

[0081] Furthermore, the composition of the existing prepared materials can be analyzed by chemical analysis techniques such as energy dispersive X-ray spectroscopy (EDX) and inductively coupled plasma mass spectrometry (ICP-MS) to obtain the composition of the prepared materials; the characterization material components in the composition of the prepared materials can be screened out by principal component analysis (PCA); the material microstructure corresponding to the existing prepared materials can be collected by microscopic imaging equipment such as scanning electron microscope (SEM), transmission electron microscope (TEM), atomic force microscope (AFM); based on the material microstructure, calculation methods such as molecular dynamics simulation and experiments such as mechanical property test and electrical property test can be used. The method analyzes the material structural properties corresponding to the existing prepared material; thermal performance parameters and electrical performance parameters corresponding to the existing prepared material can be measured by thermal testing instruments such as a thermomechanical analyzer (TMA) and a laser flash meter, and electrical testing equipment such as a four-probe method testing equipment and a Hall effect testing system; characterization performance parameters among the thermal performance parameters and the electrical performance parameters can be screened out by a sensitivity analysis method to obtain thermal characterization parameters and electrical characterization parameters; the characterization material composition, the material structural properties, the thermal characterization parameters and the electrical characterization parameters are summarized together to obtain the material performance parameters corresponding to the existing prepared material.

[0082] The present invention screens out collaborative preparation materials corresponding to the existing preparation materials from a preset material library based on the material performance parameters, thereby optimizing the material combination, improving the structural performance and electrical and thermal properties of the pressure chip, and effectively reducing the zero drift of the chip, thereby improving the stability of the pressure chip in complex environments. The preset material library is a pre-established database containing information related to a variety of materials (such as composition, performance parameters, etc.), and the collaborative preparation materials are materials that cooperate with the existing preparation materials in performance and can be used together to achieve specific preparation purposes or improve the comprehensive performance of the materials.

[0083] As an embodiment of the present invention, the step of selecting a collaborative preparation material corresponding to the existing preparation material from a preset material library based on the material performance parameters includes:

[0084] Identifying a performance parameter label corresponding to each parameter in the material performance parameters;

[0085] Based on the performance parameter labels, screening out collaborative candidate materials from a preset material library, and retrieving candidate material performance parameters corresponding to the collaborative candidate materials;

[0086] Calculating the parameter matching degree between the material performance parameters and the candidate material performance parameters, and calculating the material economic expenditure corresponding to the collaborative candidate material;

[0087] In combination with the parameter matching degree and the material economic expenditure, the collaborative preparation material corresponding to the existing preparation material is screened out from the collaborative candidate materials.

[0088] Among them, the performance parameter label is the characteristic identifier corresponding to each parameter in the material performance parameters; the synergistic candidate material is a material with potential synergy screened from a preset material library based on the performance parameter label; the candidate material performance parameter is the specific performance value corresponding to the synergistic candidate material; the parameter matching degree indicates the similarity or fit between the material performance parameter and the candidate material performance parameter; the material economic expenditure is the cost expenditure corresponding to the synergistic candidate material.

[0089] Furthermore, the performance parameter label corresponding to each parameter in the material performance parameters can be identified through a classification algorithm; based on the performance parameter label, the collaborative candidate materials can be screened out from the preset material library through database retrieval and screening functions; the candidate material performance parameters corresponding to the collaborative candidate materials can be retrieved through database query statements or interface calls; the parameter matching degree between the material performance parameters and the candidate material performance parameters can be calculated through a cosine similarity algorithm; the material economic expenditure corresponding to the collaborative candidate material can be calculated through material cost database query and market price research; the parameter matching degree and the material economic expenditure are standardized, and the normalized numerical value sum is calculated, and the collaborative candidate material with the highest numerical value sum is selected as the collaborative preparation material corresponding to the existing preparation material.

[0090] S2. Combining the existing preparation materials and the collaborative preparation materials, perform finite element simulation on the pressure chip to obtain a pressure chip simulation model, and calculate the structural deflection corresponding to the pressure chip simulation model.

[0091] The present invention combines the existing preparation materials and the collaborative preparation materials to perform finite element simulation on the pressure chip, and can obtain a chip model constructed by different material combinations, thereby providing an important basis for the subsequent calculation of the structural deflection corresponding to the pressure chip simulation model. Among them, the pressure chip simulation model is a chip model obtained by combining the existing preparation materials and the collaborative preparation materials and performing finite element simulation on the pressure chip through simulation software, and the simulation software includes SolidWorks, CATIA and other software.

[0092] The present invention calculates the structural deflection corresponding to the pressure chip simulation model, and the deformation degree of the pressure chip under stress state can be understood through the structural deflection, which provides reliability for the subsequent reduction of chip zero drift, wherein the structural deflection represents the quantitative value of the degree of bending deformation of the chip structure of the pressure chip simulation model under stress state.

[0093] As an embodiment of the present invention, the calculating the structural deflection corresponding to the pressure chip simulation model includes:

[0094] Applying a uniform load to the pressure chip simulation model to perform a tensile test, and recording the model's transverse strain and the model's longitudinal strain under the uniform load;

[0095] Measuring model size parameters corresponding to the pressure chip simulation model, wherein the model size parameters include: diaphragm geometric radius, diaphragm position radius, and upper plate axial size;

[0096] Calculate the model bending stiffness corresponding to the pressure chip simulation model based on the model transverse strain, the model longitudinal strain, and the axial dimension of the upper plate;

[0097] The structural deflection corresponding to the pressure chip simulation model is calculated in combination with the diaphragm geometric radius, the diaphragm position radius, the model bending stiffness and the uniform load.

[0098] Among them, the uniform load is the external force applied to the tensile test of the pressure chip simulation model, which is uniformly distributed on the model to simulate the actual stress conditions. The model lateral strain and the model longitudinal strain are the relative deformations of the model in the lateral and longitudinal directions under the uniform load, respectively. The diaphragm geometric radius is a key parameter of the overall size of the diaphragm of the pressure chip simulation model, which is used to describe the size of the diaphragm. The diaphragm position radius is the radius parameter of the position where the uniform load acts on the pressure chip simulation model to reach the center of the chip. The axial dimension of the upper plate is the length dimension of the upper plate of the pressure chip simulation model in the axial direction, which reflects the size and shape characteristics of the upper plate. The model bending stiffness is a physical quantity corresponding to the pressure chip simulation model that measures the model's ability to resist bending deformation, and is related to the material properties and geometric shape of the model.

[0099] Furthermore, a uniform load can be applied to the pressure chip simulation model through the load application module of the finite element analysis software to perform a tensile test, and the model lateral strain and model longitudinal strain under the uniform load can be recorded through the strain monitoring tool in the finite element analysis software; the model size parameters corresponding to the pressure chip simulation model can be measured through the geometric measurement tool or post-processing module of the finite element analysis software.

[0100] Furthermore, as an optional embodiment of the present invention, the calculation of the model bending stiffness corresponding to the pressure chip simulation model by combining the model transverse strain, the model longitudinal strain, and the axial dimension of the upper plate includes:

[0101] querying the material Poisson's ratio corresponding to each model in the pressure chip simulation model, and calculating the model Poisson's ratio corresponding to the pressure chip simulation model based on the material Poisson's ratio;

[0102] Calculating a model elastic coefficient corresponding to the pressure chip simulation model based on the model transverse strain and the model longitudinal strain;

[0103] Combining the model elastic coefficient, the axial dimension of the upper plate and the model Poisson's ratio, the model bending stiffness corresponding to the pressure chip simulation model is calculated using the following formula:

[0104] ;

[0105] Among them, A represents the model bending stiffness corresponding to the pressure chip simulation model, B represents the model elastic coefficient, d represents the axial size of the upper plate, and b represents the model Poisson's ratio.

[0106] Among them, the material Poisson's ratio is the ratio of the lateral strain to the longitudinal strain of the material corresponding to each model in the pressure chip simulation model, reflecting the relationship between the lateral deformation and the longitudinal deformation of the material when subjected to force. The model Poisson's ratio is a property similar to the material Poisson's ratio manifested macroscopically by the entire model corresponding to the pressure chip simulation model, which is used to describe the proportional relationship between the lateral and longitudinal deformations when the model as a whole is subjected to force. The model elastic coefficient is a physical quantity corresponding to the pressure chip simulation model that measures the model's ability to resist elastic deformation, and characterizes the proportional relationship between stress and strain when the model is subjected to force.

[0107] Furthermore, the material Poisson's ratio corresponding to each model in the pressure chip simulation model can be queried from the official website of the material through human-computer interaction; the material volume of each material in the simulation model is determined, the material Poisson's ratio is multiplied by the material volume respectively, and the multiplication results are added to obtain the model Poisson's ratio corresponding to the pressure chip simulation model; the ratio between the lateral strain of the model and the longitudinal strain of the model is calculated to obtain the model elastic coefficient corresponding to the pressure chip simulation model.

[0108] Furthermore, as an optional embodiment of the present invention, the calculation of the structural deflection corresponding to the pressure chip simulation model by combining the diaphragm geometric radius, the diaphragm position radius, the model bending stiffness, and the uniform load includes:

[0109] The structural deflection corresponding to the pressure chip simulation model is calculated using the following formula:

[0110] ;

[0111] Where D represents the structural deflection corresponding to the pressure chip simulation model, F represents the uniform load, A represents the model bending stiffness, R represents the diaphragm geometric radius, and r represents the diaphragm position radius.

[0112] S3. Query the chip working environment of the pressure chip, configure the simulated environmental conditions of the pressure chip based on the chip working environment, perform multiple zero-point tests on the pressure chip simulation model under the simulated environmental conditions to obtain zero-point test values, and calculate the zero-point drift discreteness corresponding to the pressure chip simulation model based on the zero-point test values.

[0113] The present invention can reproduce the actual working scene of the pressure chip by configuring the simulated environmental conditions of the pressure chip, laying a foundation for subsequent tests. Under the simulated environmental conditions, the pressure chip simulation model is zero-point tested to obtain a zero-point test value, thereby providing data support for the subsequent calculation of the zero-point drift discreteness corresponding to the pressure chip simulation model, wherein the chip working environment is the sum of the physical, chemical, electrical and other conditions under which the pressure chip is actually operating; the simulated environmental conditions are artificially set by the pressure chip during the simulation process, and are used to simulate various parameter combinations of actual working scenes; the zero-point test value is the value of the pressure chip simulation model. When performing zero-point testing under each different condition, multiple output data obtained in the absence of external pressure are used to measure the performance indicators of the chip's initial state. Furthermore, the chip working environment of the pressure chip can be queried through corporate product documents, historical monitoring data, or user usage requirements. Based on the chip working environment, the simulated environmental conditions of the pressure chip can be configured through the environmental parameter setting module of the finite element analysis software, which is compiled in a programming language. Under the simulated environmental conditions, the pressure chip simulation model can be zero-point tested through the test function module of the finite element analysis software or a custom-written test script to obtain a zero-point test value.

[0114] The present invention calculates the zero-point drift discreteness corresponding to the pressure chip simulation model based on the zero-point test value, and the stability of the pressure chip simulation model can be understood through the zero-point drift discreteness, wherein the zero-point drift discreteness reflects the ability of the pressure chip simulation model to maintain its initial state under different conditions.

[0115] As an embodiment of the present invention, the calculating of the zero-point drift discreteness corresponding to the pressure chip simulation model based on the zero-point test value includes:

[0116] drawing a test value curve corresponding to the zero-point test value, and identifying abnormal test values among the zero-point test values based on the test value curve;

[0117] Cleaning the abnormal test value in the zero-point test value to obtain a target zero-point test value;

[0118] Calculate the average value corresponding to the target zero-point test value to obtain the zero-point test mean;

[0119] Combining the zero-point test value and the zero-point test mean, the zero-point drift dispersion corresponding to the pressure chip simulation model is calculated using the following formula:

[0120] ;

[0121] Among them, G represents the zero drift discreteness corresponding to the pressure chip simulation model, Indicates the e-th test value in the target zero test value, represents the mean of the zero-point test, e represents the serial number of the target zero-point test value, and q represents the number of target zero-point test values.

[0122] Among them, the test value curve is a curve corresponding to the zero-point test value that changes with time or other variables, the abnormal test value is a value in the zero-point test value that obviously deviates from the overall data distribution or does not conform to the expected rules, and the target zero-point test value is the test value obtained after removing the abnormal test value in the zero-point test value.

[0123] Furthermore, a test value curve corresponding to the zero-point test value can be drawn using data visualization tools (such as Matplotlib, Origin, etc.); based on the test value curve, abnormal test values in the zero-point test value can be identified using the isolation forest algorithm; and the average value corresponding to the target zero-point test value can be calculated using the average function to obtain the zero-point test mean.

[0124] S4. Performing a temperature characteristic test on the pressure chip simulation model to obtain a temperature response value, and calculating a chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value.

[0125] The present invention calculates the chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value. The chip temperature drift coefficient can be used to understand the performance stability and sensitivity of the pressure chip simulation model under different temperature environments, thereby providing a basis for the subsequent screening of high-efficiency preparation materials corresponding to the pressure chip. The temperature response value is the output data related to pressure measurement collected under different temperature conditions when the temperature characteristic test of the pressure chip simulation model is performed; the chip temperature drift coefficient is a key indicator of the degree of change in chip measurement performance per unit degree of quantitative temperature change corresponding to the pressure chip simulation model. Furthermore, the temperature characteristic test of the pressure chip simulation model can be performed through a temperature simulation module to obtain a temperature response value, and the temperature simulation module is compiled by JAVA language.

[0126] As an embodiment of the present invention, calculating the chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value includes:

[0127] Constructing a temperature response scatter plot corresponding to the temperature response value, and performing linear fitting processing on the temperature response scatter plot to obtain a temperature response fitting curve;

[0128] Smoothing the temperature response fitting curve to obtain a smoothed temperature response curve;

[0129] Based on the smooth temperature response curve, constructing a temperature response fitting equation corresponding to the pressure chip simulation model;

[0130] Dispatching historical temperature response data of the pressure chip, and performing equation correction on the temperature response fitting equation based on the historical temperature response data to obtain a target temperature response equation;

[0131] Derivative processing is performed on the target temperature response equation to obtain a temperature sensitive equation corresponding to the pressure chip simulation model;

[0132] Determining a temperature range corresponding to the pressure chip simulation model based on the temperature response value;

[0133] Based on the temperature range, the chip temperature drift coefficient corresponding to the pressure chip simulation model is calculated using the temperature sensitive equation.

[0134] The temperature response scatter plot is a set of discrete points corresponding to the temperature response value, plotted with temperature as the horizontal axis and response value as the vertical axis. The temperature response fitting curve is a straight line or curve obtained by linear fitting the temperature response scatter plot, which can approximately represent the distribution trend of the scatter points. The smoothed temperature response curve is a curve obtained by smoothing the temperature response fitting curve to reduce curve fluctuations and make it smoother. The temperature response fitting equation is a mathematical expression corresponding to the pressure chip simulation model constructed based on the smoothed temperature response curve, which describes the relationship between temperature and response value. The historical temperature response data is data related to the response values of the pressure chip recorded under different temperature conditions in the past. The target temperature response equation is an equation that more accurately describes the relationship between the temperature and response value of the pressure chip after correction based on the historical temperature response data. The temperature sensitivity equation is an equation corresponding to the pressure chip simulation model, obtained by derivation of the target temperature response equation, which describes the rate of change of the pressure chip response with temperature. The temperature range is a specific temperature range corresponding to the pressure chip simulation model, determined according to actual application requirements, for analyzing performance such as the chip temperature drift coefficient.

[0135] Furthermore, the temperature response fitting curve can be smoothed by a Savitzky-Golay filtering algorithm to obtain a smooth temperature response curve; based on the smooth temperature response curve, a temperature response fitting equation corresponding to the pressure chip simulation model can be constructed by a polynomial fitting algorithm; the historical temperature response data of the pressure chip can be scheduled through a database query interface or a data management system; based on the historical temperature response data, the temperature response fitting equation can be corrected by a Bayesian optimization algorithm to obtain a target temperature response equation; the target temperature response equation can be derived by symbolic calculation software (such as Mathematica, SymPy) to obtain a temperature sensitive equation corresponding to the pressure chip simulation model; the temperature sensitive equation can be used to calculate the integral value between the temperature intervals to obtain the chip temperature drift coefficient corresponding to the pressure chip simulation model.

[0136] S5. In combination with the structural deflection, the zero drift discreteness and the chip temperature drift coefficient, determine the high-efficiency preparation material corresponding to the pressure chip from the collaborative preparation materials and the existing preparation materials, optimize the parameters of the chip preparation path to obtain a low zero drift preparation path, combine the high-efficiency preparation material and the low zero drift preparation path, perform preparation processing on the pressure chip, and obtain a chip finished product.

[0137] The present invention determines the high-efficiency preparation material corresponding to the pressure chip from the collaborative preparation materials and the existing preparation materials by combining the structural deflection, the zero drift discreteness and the chip temperature drift coefficient, and can screen out materials that meet the chip performance requirements, providing an important basis for high-performance chip manufacturing; the chip preparation path is parameter optimized to obtain a low zero drift preparation path, which can accurately control the process parameters, effectively suppress the chip zero drift phenomenon during the production process, and greatly improve the chip performance stability. Among them, the high-efficiency preparation material is a material with the best performance corresponding to the pressure chip determined from the collaborative preparation materials and the existing preparation materials by combining the structural deflection, the zero drift discreteness and the chip temperature drift coefficient. The low zero drift preparation path is a process parameter combination and process that can effectively reduce the chip zero drift after the chip preparation path is parameter optimized to ensure high-precision chip output. Furthermore, the structural deflection, the zero drift discreteness and the chip temperature drift coefficient corresponding to each material are summed, and the material with the largest sum value and that can be complementary manufactured is selected as the high-efficiency preparation material corresponding to the pressure chip.

[0138] As an embodiment of the present invention, the parameter optimization of the chip preparation path to obtain a low zero drift preparation path includes:

[0139] Obtaining the fabrication process parameters in the chip fabrication path, and analyzing the parameter sensitivity corresponding to the fabrication process parameters;

[0140] Based on the parameter sensitivity, screening out key preparation parameters among the preparation process parameters;

[0141] Analyzing drift correlations corresponding to the key preparation parameters, and determining, based on the drift correlations, preparation parameters to be optimized among the key preparation parameters;

[0142] Analyze the feasibility of the parameters corresponding to the preparation parameters to be optimized, and query the preparation equipment corresponding to the preparation parameters to be optimized;

[0143] In combination with the preparation equipment and the feasibility of the parameters, setting the constraint conditions of the preparation parameters to be optimized;

[0144] Under the constraints, performing parameter optimization processing on the preparation parameters to be optimized to obtain an optimal combination of parameters;

[0145] Based on the optimal combination of parameters, the chip preparation path is updated with parameters to obtain a low zero drift preparation path.

[0146] Among them, the preparation process parameters are specific process operation variables (such as temperature, pressure, time, etc.) in the chip preparation path; the parameter sensitivity indicates the sensitivity of the preparation process parameters to performance indicators such as chip zero drift; the key preparation parameters are parameters among the preparation process parameters that have a significant impact on chip performance and require special attention; the drift correlation is the degree of correlation between the key preparation parameters and the chip zero drift phenomenon; the preparation parameters to be optimized are parameters among the key preparation parameters that need to be further adjusted and optimized to reduce zero drift; the parameter feasibility is the degree to which the preparation parameters to be optimized can be implemented in actual production and meet the process and equipment requirements; the preparation equipment is the production equipment that can realize parameter adjustment and process operation corresponding to the preparation parameters to be optimized; the constraints are the restrictions on the value range, equipment capability, process specifications, etc. of the preparation parameters to be optimized; the optimal parameter combination is the parameter value set that minimizes the chip zero drift after the preparation parameters to be optimized are optimized under the constraints.

[0147] Furthermore, the preparation process parameters in the chip preparation path can be acquired in real time through the equipment sensors; the parameter sensitivity corresponding to the preparation process parameters can be analyzed through the sensitivity analysis algorithm (such as the local derivative method, the Monte Carlo simulation method); the parameter sensitivity and the sensitivity threshold are compared to screen out the key preparation parameters in the preparation process parameters, and the sensitivity threshold is a pre-set reference value, which can be 0.8, or can be set according to the actual application scenario; the drift correlation corresponding to the key preparation parameters can be analyzed through the correlation analysis algorithm (such as the Pearson correlation coefficient, the grey correlation analysis); based on the drift correlation, the correlation threshold is set and the preparation parameters to be optimized among the key preparation parameters are determined in order, such as determining the key preparation parameters whose drift correlation is greater than the set threshold and whose correlation ranking is high as the preparation parameters to be optimized. preparation parameters; the parameter feasibility corresponding to the preparation parameters to be optimized can be analyzed by a process feasibility evaluation model (combined with material properties and process specifications); the preparation equipment corresponding to the preparation parameters to be optimized can be searched through equipment database retrieval and manufacturer technical document query; in combination with the preparation equipment and the parameter feasibility, the constraint conditions of the preparation parameters to be optimized can be set by the upper limit of equipment performance parameters, process specification requirements, and cost constraints; under the constraints, the preparation parameters to be optimized can be optimized by an intelligent optimization algorithm (such as a particle swarm optimization algorithm) to obtain the optimal parameter combination. For example, by using a particle swarm optimization algorithm, each parameter combination to be optimized is regarded as a particle in the search space, and through information sharing and speed update between particles, it is iterated towards the optimal solution direction, and finally the optimal parameter combination that meets the low zero drift target is obtained.

[0148] The present invention combines the high-efficiency preparation material and the low zero drift preparation path to perform preparation processing on the pressure chip to obtain a finished chip, thereby obtaining a high-efficiency performance pressure chip with low zero drift.

[0149] Compared with the problems described in the background technology, the present invention can understand the characteristics of the existing preparation materials by analyzing the material performance parameters corresponding to the existing preparation materials, and provide a reliable basis for screening collaborative preparation materials. Furthermore, the present invention combines the existing preparation materials and the collaborative preparation materials to perform finite element simulation on the pressure chip, and can obtain a chip model constructed by different material combinations, thereby providing an important basis for the subsequent calculation of the structural deflection corresponding to the pressure chip simulation model. The present invention can reproduce the actual working scene of the pressure chip by configuring the simulated environmental conditions of the pressure chip, laying a foundation for subsequent testing. Under the simulated environmental conditions, the pressure chip simulation model is subjected to a zero-point test to obtain a zero-point test value, thereby providing data support for the subsequent calculation of the zero-point drift discreteness corresponding to the pressure chip simulation model, and further In the first step, the present invention calculates the chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value. The chip temperature drift coefficient can be used to understand the performance stability of the pressure chip simulation model under different temperature environments and the sensitivity to temperature, thereby providing a basis for the subsequent screening of efficient preparation materials corresponding to the pressure chip. Furthermore, the present invention determines the efficient preparation material corresponding to the pressure chip from the collaborative preparation materials and the existing preparation materials by combining the structural deflection, the zero drift discreteness and the chip temperature drift coefficient, and can screen out materials that meet the chip performance requirements, providing an important basis for high-performance chip manufacturing; the chip preparation path is parameter optimized to obtain a low zero drift preparation path, which can accurately control the process parameters, effectively suppress the chip zero drift phenomenon during the production process, and greatly improve the chip performance stability. Therefore, the SOI-based low zero drift pressure chip efficient preparation method and system provided in the embodiment of the present invention can improve the preparation efficiency of SOI low zero drift pressure chips.

[0150] Example 2:

[0151] like Figure 2 The figure shows a functional module diagram of an efficient preparation system for a low zero drift pressure chip based on SOI according to the present invention.

[0152] The SOI-based, low-zero-drift pressure chip high-efficiency fabrication system 200 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the SOI-based, low-zero-drift pressure chip high-efficiency fabrication system can include a collaborative fabrication material screening module 201, a structural deflection calculation module 202, a zero-drift dispersion calculation module 203, a chip temperature drift coefficient calculation module 204, and a chip fabrication module 205. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These are stored in the electronic device's memory.

[0153] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0154] The collaborative preparation material screening module 201 is used to obtain existing preparation materials and chip preparation paths of the low zero drift pressure chip, analyze the material performance parameters corresponding to the existing preparation materials, and based on the material performance parameters, screen out collaborative preparation materials corresponding to the existing preparation materials from a preset material library;

[0155] The structural deflection calculation module 202 is used to perform finite element simulation processing on the pressure chip in combination with the existing preparation material and the collaborative preparation material to obtain a pressure chip simulation model, and calculate the structural deflection corresponding to the pressure chip simulation model;

[0156] The zero drift dispersion calculation module 203 is used to query the chip working environment of the pressure chip, configure the simulated environmental conditions of the pressure chip based on the chip working environment, perform a zero point test on the pressure chip simulation model under the simulated environmental conditions to obtain a zero point test value, and calculate the zero drift dispersion corresponding to the pressure chip simulation model based on the zero point test value;

[0157] The chip temperature drift coefficient calculation module 204 is used to perform temperature characteristic test processing on the pressure chip simulation model to obtain a temperature response value, and calculate the chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value;

[0158] The chip preparation module 205 is used to combine the structural deflection, the zero drift discreteness and the chip temperature drift coefficient, determine the high-efficiency preparation material corresponding to the pressure chip from the collaborative preparation materials and the existing preparation materials, optimize the parameters of the chip preparation path, obtain a low zero drift preparation path, combine the high-efficiency preparation material and the low zero drift preparation path, perform preparation processing on the pressure chip, and obtain a chip finished product.

[0159] In detail, the modules in the SOI-based low zero drift pressure chip efficient preparation system 200 according to the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means are used as the efficient preparation method of the low zero drift pressure chip based on SOI described in , and can produce the same technical effects, so they will not be repeated here.

[0160] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An efficient preparation method for a low zero drift pressure chip based on SOI, characterized in that: The method comprises: Obtain existing preparation materials and chip preparation paths for a low zero drift pressure chip, analyze material performance parameters corresponding to the existing preparation materials, and based on the material performance parameters, screen out collaborative preparation materials corresponding to the existing preparation materials from a preset material library; Combining the existing preparation materials and the collaborative preparation materials, performing finite element simulation processing on the pressure chip to obtain a pressure chip simulation model, and calculating the structural deflection corresponding to the pressure chip simulation model; querying a chip working environment of the pressure chip, configuring a simulated environmental condition of the pressure chip based on the chip working environment, performing a zero-point test on the pressure chip simulation model under the simulated environmental condition to obtain a zero-point test value, and calculating a zero-point drift dispersion corresponding to the pressure chip simulation model based on the zero-point test value; Performing a temperature characteristic test on the pressure chip simulation model to obtain a temperature response value, and calculating a chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value, wherein calculating the chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value includes: Constructing a temperature response scatter plot corresponding to the temperature response value, and performing linear fitting processing on the temperature response scatter plot to obtain a temperature response fitting curve; Smoothing the temperature response fitting curve to obtain a smoothed temperature response curve; Based on the smooth temperature response curve, constructing a temperature response fitting equation corresponding to the pressure chip simulation model; Dispatching historical temperature response data of the pressure chip, and performing equation correction on the temperature response fitting equation based on the historical temperature response data to obtain a target temperature response equation; Derivative processing is performed on the target temperature response equation to obtain a temperature sensitive equation corresponding to the pressure chip simulation model; Determining a temperature range corresponding to the pressure chip simulation model based on the temperature response value; Based on the temperature range, the chip temperature drift coefficient corresponding to the pressure chip simulation model is calculated using the temperature sensitive equation; In combination with the structural deflection, the zero drift discreteness and the chip temperature drift coefficient, the high-efficiency preparation material corresponding to the pressure chip is determined from the collaborative preparation materials and the existing preparation materials, and the parameters of the chip preparation path are optimized to obtain a low zero drift preparation path. In combination with the high-efficiency preparation material and the low zero drift preparation path, the preparation process of the pressure chip is performed to obtain a chip finished product.

2. The method for efficiently preparing a low zero drift pressure chip based on SOI according to claim 1, characterized in that: The analyzing of the material performance parameters corresponding to the existing prepared material includes: Performing component analysis on the existing preparation material to obtain the preparation material components, and screening out the characterizing material components in the preparation material components; Collecting the material microstructure corresponding to the existing prepared material, and analyzing the material structure performance corresponding to the existing prepared material based on the material microstructure; Measuring thermal performance parameters and electrical performance parameters corresponding to the existing prepared material, and screening out characterizing performance parameters from the thermal performance parameters and the electrical performance parameters to obtain thermal characterizing parameters and electrical characterizing parameters; The material performance parameters corresponding to the existing prepared material are generated by combining the characterized material composition, the material structural performance, the thermal characterization parameters and the electrical characterization parameters.

3. The method for efficiently preparing a low zero drift pressure chip based on SOI according to claim 1, characterized in that: The step of screening out the collaborative preparation material corresponding to the existing preparation material from a preset material library based on the material performance parameters includes: Identifying a performance parameter label corresponding to each parameter in the material performance parameters; Based on the performance parameter labels, screening out collaborative candidate materials from a preset material library, and retrieving candidate material performance parameters corresponding to the collaborative candidate materials; Calculating the parameter matching degree between the material performance parameters and the candidate material performance parameters, and calculating the material economic expenditure corresponding to the collaborative candidate material; In combination with the parameter matching degree and the material economic expenditure, the collaborative preparation material corresponding to the existing preparation material is screened out from the collaborative candidate materials.

4. The method for efficiently preparing a low zero drift pressure chip based on SOI according to claim 1, characterized in that: The calculating the structural deflection corresponding to the pressure chip simulation model includes: Applying a uniform load to the pressure chip simulation model to perform a tensile test, and recording the model's transverse strain and the model's longitudinal strain under the uniform load; Measuring model size parameters corresponding to the pressure chip simulation model, wherein the model size parameters include: diaphragm geometric radius, diaphragm position radius, and upper plate axial size; Calculate the model bending stiffness corresponding to the pressure chip simulation model based on the model transverse strain, the model longitudinal strain, and the axial dimension of the upper plate; The structural deflection corresponding to the pressure chip simulation model is calculated in combination with the diaphragm geometric radius, the diaphragm position radius, the model bending stiffness and the uniform load.

5. The method for efficiently preparing a low zero drift pressure chip based on SOI according to claim 4, characterized in that: Calculating the model bending stiffness corresponding to the pressure chip simulation model by combining the model transverse strain, the model longitudinal strain, and the axial dimension of the upper plate includes: querying the material Poisson's ratio corresponding to each model in the pressure chip simulation model, and calculating the model Poisson's ratio corresponding to the pressure chip simulation model based on the material Poisson's ratio; Calculating a model elastic coefficient corresponding to the pressure chip simulation model based on the model transverse strain and the model longitudinal strain; Combining the model elastic coefficient, the axial dimension of the upper plate and the model Poisson's ratio, the model bending stiffness corresponding to the pressure chip simulation model is calculated using the following formula: ； Among them, A represents the model bending stiffness corresponding to the pressure chip simulation model, B represents the model elastic coefficient, d represents the axial size of the upper plate, and b represents the model Poisson's ratio.

6. The method for efficiently preparing a low zero drift pressure chip based on SOI according to claim 4, characterized in that: The calculating the structural deflection corresponding to the pressure chip simulation model by combining the diaphragm geometric radius, the diaphragm position radius, the model bending stiffness and the uniform load includes: The structural deflection corresponding to the pressure chip simulation model is calculated using the following formula: ； Where D represents the structural deflection corresponding to the pressure chip simulation model, F represents the uniform load, A represents the model bending stiffness, R represents the diaphragm geometric radius, and r represents the diaphragm position radius.

7. The method for efficiently preparing a low zero drift pressure chip based on SOI according to claim 1, characterized in that: The calculating, based on the zero-point test value, the zero-point drift discreteness corresponding to the pressure chip simulation model includes: drawing a test value curve corresponding to the zero-point test value, and identifying abnormal test values among the zero-point test values based on the test value curve; Cleaning the abnormal test value in the zero-point test value to obtain a target zero-point test value; Calculate the average value corresponding to the target zero-point test value to obtain the zero-point test mean; Combining the zero-point test value and the zero-point test mean, the zero-point drift dispersion corresponding to the pressure chip simulation model is calculated using the following formula: ； Among them, G represents the zero drift discreteness corresponding to the pressure chip simulation model, Indicates the e-th test value in the target zero test value, represents the mean of the zero-point test, e represents the serial number of the target zero-point test value, and q represents the number of target zero-point test values.

8. The method for efficiently preparing a low zero drift pressure chip based on SOI according to claim 1, characterized in that: Optimizing the parameters of the chip preparation path to obtain a low zero drift preparation path includes: Obtaining the fabrication process parameters in the chip fabrication path, and analyzing the parameter sensitivity corresponding to the fabrication process parameters; Based on the parameter sensitivity, screening out key preparation parameters among the preparation process parameters; Analyzing drift correlations corresponding to the key preparation parameters, and determining, based on the drift correlations, preparation parameters to be optimized among the key preparation parameters; Analyze the feasibility of the parameters corresponding to the preparation parameters to be optimized, and query the preparation equipment corresponding to the preparation parameters to be optimized; In combination with the preparation equipment and the feasibility of the parameters, setting the constraint conditions of the preparation parameters to be optimized; Under the constraints, performing parameter optimization processing on the preparation parameters to be optimized to obtain an optimal combination of parameters; Based on the optimal combination of parameters, the chip preparation path is updated with parameters to obtain a low zero drift preparation path.

9. An efficient preparation system for low zero drift pressure chips based on SOI, characterized in that: The system comprises: A collaborative preparation material screening module is used to obtain existing preparation materials and chip preparation paths of low zero drift pressure chips, analyze material performance parameters corresponding to the existing preparation materials, and based on the material performance parameters, screen out collaborative preparation materials corresponding to the existing preparation materials from a preset material library; a structural deflection calculation module, configured to perform finite element simulation processing on the pressure chip in combination with the existing preparation material and the collaborative preparation material to obtain a pressure chip simulation model, and calculate the structural deflection corresponding to the pressure chip simulation model; a zero-point drift dispersion calculation module, configured to query the chip working environment of the pressure chip, configure the simulated environmental conditions of the pressure chip based on the chip working environment, perform a zero-point test on the pressure chip simulation model under the simulated environmental conditions to obtain a zero-point test value, and calculate the zero-point drift dispersion corresponding to the pressure chip simulation model based on the zero-point test value; A chip temperature drift coefficient calculation module is used to perform temperature characteristic test processing on the pressure chip simulation model to obtain a temperature response value, and calculate the chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value, wherein the chip temperature drift coefficient corresponding to the pressure chip simulation model based on the temperature response value is calculated, including: Constructing a temperature response scatter plot corresponding to the temperature response value, and performing linear fitting processing on the temperature response scatter plot to obtain a temperature response fitting curve; Smoothing the temperature response fitting curve to obtain a smoothed temperature response curve; Based on the smooth temperature response curve, constructing a temperature response fitting equation corresponding to the pressure chip simulation model; Dispatching historical temperature response data of the pressure chip, and performing equation correction on the temperature response fitting equation based on the historical temperature response data to obtain a target temperature response equation; Derivative processing is performed on the target temperature response equation to obtain a temperature sensitive equation corresponding to the pressure chip simulation model; Determining a temperature range corresponding to the pressure chip simulation model based on the temperature response value; Based on the temperature range, the chip temperature drift coefficient corresponding to the pressure chip simulation model is calculated using the temperature sensitive equation; A chip preparation module is used to determine the high-efficiency preparation material corresponding to the pressure chip from the collaborative preparation materials and the existing preparation materials in combination with the structural deflection, the zero drift discreteness and the chip temperature drift coefficient, perform parameter optimization on the chip preparation path, obtain a low zero drift preparation path, combine the high-efficiency preparation material and the low zero drift preparation path, perform preparation processing on the pressure chip, and obtain a chip finished product.

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