A vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes

Abstract

The application provides a vacuum quality factor prediction method for MEMS gyro package pre-screening, first, a multi-damping analysis model of a MEMS vibrating gyro is established, simulation values of quality factors under different air pressures are obtained through finite element simulation, and an air pressure-quality factor relationship model, namely a P-Q model, is fitted and established; then, a real measured quality factor of a device in an atmospheric environment is tested, a proportional correction factor is constructed in combination with the simulated quality factor of the atmospheric environment obtained from the P-Q model; finally, the simulated quality factor under a target vacuum pressure is corrected by using the proportional factor, a real quality factor of the device in a vacuum environment is predicted, and device screening before packaging is performed accordingly. The application can predict the vacuum quality factor of the device without completing vacuum packaging, takes into account the generality of the theoretical model and the actual difference of the individual device, has high prediction precision, is simple to implement in engineering, can effectively reduce invalid packaging and testing costs, and improves the production yield of the MEMS gyro.

Description

Technical Field

[0001] This invention relates to the field of performance prediction and testing technology for microelectromechanical systems (MEMS) inertial devices, specifically to a method for predicting the vacuum quality factor for pre-packaging screening of MEMS gyroscopes. Background Technology

[0002] MEMS gyroscopes, as a key component of inertial measurement units (IMUs), have been widely used in consumer electronics, industrial control, automotive electronics, unmanned systems, navigation and guidance, and aerospace. The quality factor is a crucial indicator of MEMS gyroscope performance, directly affecting the device's noise level, response characteristics, and operational stability. To achieve a high quality factor, MEMS gyroscopes typically require vacuum packaging to reduce the impact of gas damping on device vibration. However, vacuum packaging is a costly downstream process in manufacturing, and the performance of packaged devices exhibits significant variability, with some devices failing to meet the expected quality factor requirements.

[0003] In existing technologies, the quality factor testing of MEMS gyroscopes is typically performed after vacuum packaging. This testing method has a significant time lag, making it impossible to assess the potential performance of the device before packaging. If a device's quality factor is found to be substandard only after packaging, it not only wastes expensive packaging materials and process costs but also leads to ineffective investment in subsequent testing and circuit adaptation resources, increasing overall production costs.

[0004] To predict device performance in advance, some studies use finite element method (FEM) simulation to calculate the quality factor under different gas pressures. However, simulation models are usually based on ideal geometric dimensions, material parameters, and boundary conditions, resulting in unavoidable systematic deviations from actually manufactured devices. These deviations stem from various factors such as dimensional errors during manufacturing, fluctuations in material parameters, and differences in boundary conditions, and the degree of deviation varies among different devices. Therefore, the results obtained from pure simulation can only reflect the general patterns of similar devices and cannot accurately predict the actual performance of individual devices, making it difficult to directly apply to device selection in the production process.

[0005] Some methods attempt to test devices in a vacuum environment before packaging, but this requires a dedicated vacuum testing system, which is expensive, complex, and time-consuming. Introducing this vacuum testing into the production line would significantly increase production time and labor costs, reduce production efficiency, and fail to meet the needs of large-scale industrial production.

[0006] Furthermore, most existing quality factor prediction methods are designed for specific MEMS gyroscope structures or are only applicable to a narrow pressure range. When the device structure changes or the packaging vacuum requirements differ, the original methods often become inapplicable, requiring the model to be rebuilt or the test plan to be adjusted, resulting in poor versatility.

[0007] Due to the aforementioned problems, the current MEMS gyroscope manufacturing process cannot effectively screen device performance before packaging, resulting in a large number of low-performance devices entering the subsequent packaging process. This not only increases production costs but also reduces the controllability of the production process, making it difficult to effectively guarantee the yield and consistency of the final product, thus hindering the further development of the MEMS gyroscope industry. Therefore, there is an urgent need to develop a method that can accurately predict the vacuum quality factor of MEMS gyroscopes before packaging to solve the many problems existing in the current technology. Summary of the Invention

[0008] This invention addresses the problem that in existing technologies, the vacuum quality factor of MEMS gyroscopes can usually only be obtained after vacuum packaging and is difficult to evaluate before packaging. It proposes a method for predicting the vacuum quality factor of MEMS gyroscopes based on the PQ model and atmospheric pressure testing correction. The purpose of this invention is to: establish a PQ model (quality factor relationship model) for MEMS gyroscopes under different atmospheric pressures through finite element simulation; construct a proportional correction factor for individual devices by utilizing the proportional relationship between the measured quality factor and the simulated quality factor in an atmospheric environment; apply this proportional correction factor to the simulated quality factor in a vacuum environment to predict the true quality factor under vacuum conditions; and estimate the vacuum quality factor after packaging without packaging the device, providing a basis for device selection and packaging decisions.

[0009] The specific technical solution is as follows:

[0010] A method for predicting the vacuum quality factor before MEMS gyroscope packaging includes the following steps:

[0011] Step S1: Based on the structural parameters, material parameters, and operating mode parameters of the MEMS vibrating gyroscope, establish a multi-damping analysis model of the device that comprehensively considers air damping and structural damping;

[0012] Step S2: Under multiple discrete air pressure points, the simulated value of the quality factor corresponding to the MEMS vibrating gyroscope is obtained through finite element simulation, forming a discrete correspondence between air pressure and quality factor;

[0013] Step S3: Based on the simulated quality factor values ​​at different pressure points, fit the discrete data to establish a pressure-quality factor relationship model, i.e., the PQ model.

[0014] Step S4: Before the MEMS gyroscope is vacuum-sealed, the device is tested for quality factor in an atmospheric environment to obtain the measured atmospheric quality factor of the device.

[0015] Step S5: Input the atmospheric environmental pressure into the PQ model to obtain the simulated quality factor value of the device under atmospheric conditions;

[0016] Step S6: Compare the simulated quality factor value under atmospheric conditions with the measured quality factor value under atmospheric conditions to construct the scaling factor of the device.

[0017] Step S7: Substitute the target vacuum environment pressure into the PQ model to obtain the simulated quality factor value of the device under the target vacuum environment;

[0018] Step S8: Use the scaling factor to correct the simulated quality factor value under vacuum environment to obtain the true predicted quality factor value of the device under vacuum environment.

[0019] This scheme establishes a complete vacuum quality factor prediction process, enabling quality factor estimation even before the device is fully vacuum-sealed, thus overcoming the lag issue of traditional testing methods. Furthermore, by combining finite element simulation with actual atmospheric pressure measurement data, it leverages the universality of the theoretical model while considering the actual differences between individual devices, providing a reliable basis for subsequent device selection.

[0020] As a preferred embodiment of the present invention, the multi-damping analysis model in step S1 comprehensively considers four damping mechanisms: sliding film damping, pressure film damping, thermoelastic damping, and anchor loss damping, and the total quality factor satisfies the following relationship:

[0021] ;

[0022] in, For the overall quality factor, This is the quality factor corresponding to the sluice damping. This is the quality factor corresponding to the pressure membrane damping. This is the quality factor corresponding to thermoelastic damping. This is the quality factor corresponding to anchor loss damping.

[0023] This scheme comprehensively covers the main energy dissipation mechanisms in the operation of MEMS gyroscopes, accurately reflects the true damping characteristics of the device, improves the accuracy of finite element simulation, and lays a solid foundation for the subsequent establishment of PQ models.

[0024] As a preferred embodiment of the present invention, the pressure range of the plurality of discrete pressure points in step S2 covers... to There should be no fewer than 5 pressure points, with at least 2 pressure points set in the normal pressure region and the high vacuum region respectively.

[0025] This approach ensures that the simulation data covers the entire operating range from atmospheric pressure to high vacuum, avoiding errors caused by extrapolation calculations. Furthermore, by adding pressure point distributions in key areas that significantly impact the quality factor, the accuracy and reliability of subsequent curve fitting are improved.

[0026] As a preferred embodiment of the present invention, the PQ model in step S3 is fitted using any one of the following forms: polynomial form, exponential form, power function form, or piecewise function form, wherein the expression of the PQ model in polynomial form is: ;in, For simulation quality factor, For ambient air pressure, , , , , These are the fitting parameters.

[0027] This scheme provides a variety of flexible fitting methods, which can select the optimal model form according to the pressure-quality factor variation characteristics of MEMS gyroscopes of different structural types, ensuring the accurate expression of the nonlinear relationship between the two.

[0028] As a preferred embodiment of the present invention, the quality factor test under atmospheric conditions in step S4 adopts any one of the frequency response method, half-power bandwidth method or free decay method.

[0029] This solution adopts mature and commonly used testing methods in the industry, eliminating the need for additional development of dedicated testing equipment, thus reducing the implementation cost of the method and facilitating its direct application on existing production testing lines.

[0030] As a preferred embodiment of the present invention, the scaling factor in step S6 is defined as follows: ;in, As a scaling factor, This represents the simulated quality factor value under atmospheric conditions. This represents the measured value of the quality factor under atmospheric conditions. This refers to atmospheric environmental pressure.

[0031] This scheme quantifies the systematic deviation between individual devices and theoretical models by comparing simulated values ​​with measured values ​​under normal pressure, providing a clear basis for correcting simulation results under vacuum conditions.

[0032] As a preferred embodiment of the present invention, the formula for calculating the predicted value of the true quality factor under vacuum conditions in step S8 is as follows: ;in, This is the predicted value of the true quality factor under vacuum conditions. The simulated quality factor value under the target vacuum environment. The target vacuum environment pressure.

[0033] This scheme uses a scaling factor obtained under normal pressure to correct the simulation values ​​under vacuum conditions, eliminating the systematic deviation between the theoretical model and the actual device, and improving the accuracy of the vacuum quality factor prediction results.

[0034] As a preferred embodiment of the present invention, the method further includes step S9: screening devices based on the predicted true vacuum quality factor and setting a target quality factor threshold. ,like If the device meets the requirements for subsequent vacuum packaging and performance, it proceeds to the next step; otherwise... If the device does not meet the performance requirements for subsequent vacuum packaging, it will not proceed to the vacuum packaging process.

[0035] This solution enables pre-processing device screening based on prediction results, eliminating unqualified devices before the high-cost vacuum packaging process, thus avoiding waste of packaging resources, testing resources, and subsequent circuit adaptation resources.

[0036] As a preferred embodiment of the present invention, the finite element simulation in step S2 is performed in CoventorWare, ANSYS or COMSOL software. During the simulation, the quality factor corresponding to each damping mechanism is calculated, and then the total quality factor is calculated based on the multi-damping comprehensive relationship.

[0037] This scheme employs mainstream finite element simulation software, ensuring the reliability and consistency of the simulation results. Furthermore, by calculating each damping term step-by-step and then synthesizing the total quality factor, the traceability and accuracy of the simulation process are improved.

[0038] As a preferred embodiment of the present invention, the target vacuum environment pressure in step S7 The range is to .

[0039] This solution covers the vacuum packaging pressure range commonly used in MEMS gyroscopes, ensuring that the prediction results can be directly applied to various vacuum packaging processes in actual production, and has wide applicability.

[0040] As a preferred embodiment of the present invention, the target quality factor threshold in step S9 The range is determined based on the performance requirements of the device application scenario. to .

[0041] This solution can flexibly adjust the screening criteria according to the performance requirements of MEMS gyroscopes in different application scenarios, meeting the differentiated needs of different fields such as consumer electronics, industrial control, and aerospace.

[0042] The present invention has the following beneficial effects:

[0043] 1. This invention enables pre-assessment of device performance and optimizes the production process. It allows for obtaining the device's quality factor under vacuum conditions without waiting for vacuum packaging to complete. This moves the performance evaluation process from the later stages of production to the earlier stages, enabling device screening before the high-cost vacuum packaging process. This avoids wasting packaging, testing, and circuit adaptation resources caused by unqualified devices entering subsequent processes, effectively reducing ineffective investment in the production process.

[0044] 2. It balances the universality of the theoretical model with the individual differences of the devices. The PQ model established through finite element simulation can reflect the general law of quality factor variation with air pressure for similar devices, and has good universality. At the same time, by constructing a scaling factor using measured data at normal pressure, it is possible to specifically correct the systematic deviation between each device and the ideal model, solving the problem that pure simulation methods cannot reflect individual manufacturing differences, and making the prediction results more consistent with the actual performance of the devices.

[0045] 3. The prediction process is simple and easy to implement, facilitating engineering application. The finite element simulation method and atmospheric pressure quality factor testing method used in this invention are both mature and widely used technologies in the industry. No additional specialized equipment or processes need to be developed, and they can be directly deployed and applied to existing production and testing lines. The entire prediction process is logically clear and the calculation is simple, without adding excessive production time or labor costs.

[0046] 4. Wide applicability, meeting the needs of different scenarios. The PQ model established in this invention covers the entire working pressure range from atmospheric pressure to high vacuum, and is applicable to various common MEMS gyroscope structure types. Furthermore, the device selection criteria can be flexibly adjusted according to the performance requirements of different application scenarios, adapting to the differentiated needs of MEMS gyroscopes in multiple fields such as consumer electronics, industrial control, and aerospace.

[0047] 5. Improved controllability of the production process and product yield. Pre-screening allows for the early identification of low-performance components generated during manufacturing, reducing uncertainties in subsequent processes and making the production process more controllable. Simultaneously, it prevents defective products from entering the market, effectively improving the overall performance and reliability of the final product. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention.

[0049] Figure 2 This is a graph showing the comparison between simulated and measured values ​​in atmospheric and vacuum environments in this invention.

[0050] Figure 3 A schematic diagram of a test system used to obtain measured quality factors;

[0051] Figure 4 This is a mesh diagram of the finite element model. Detailed Implementation

[0052] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0053] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual images. They should not be construed as limiting the scope of this application. To better illustrate the embodiments of the present invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0054] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present application. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0055] In the description of this invention, unless otherwise explicitly specified and limited, the term "connection" or similar designation indicating a connection between components should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0056] Reference Figures 1-4 The following examples are provided in this specific implementation.

[0057] Example 1: Establishing simulation data of quality factor under different air pressures

[0058] This embodiment takes a four-mass MEMS vibrating gyroscope as an example. First, a three-dimensional finite element model is established based on the actual two-dimensional layout and structural parameters of the device. The finite element model includes the mass blocks, elastic beams, anchor points, structural layer thickness, gas domain, and related boundary conditions, and comprehensively considers the influence of sliding film damping, pressure film damping, thermoelastic damping, and anchor loss damping on the quality factor.

[0059] Finite element simulations were performed at multiple different pressure points. The set of pressure points is as follows: The corresponding simulated value of the quality factor can be denoted as: This results in a discrete dataset.

[0060] Example 2: Establishing a PQ model

[0061] Curve fitting was performed on the discrete simulation data in Example 1 to establish a continuous PQ model. In this example, the fitting result is expressed in polynomial form: The fitting parameters were obtained by fitting using the least squares method. , , , , For common four-mass MEMS vibrating gyroscopes, the specific values ​​typically range as follows: , , , , This model can be used to calculate any given air pressure. The simulated value of the quality factor is given.

[0062] Example 3: Obtaining the scaling factor under atmospheric conditions

[0063] The quality factor of the MEMS vibrating gyroscope under test was tested in an atmospheric environment, under atmospheric pressure. Approximately The measured quality factor under atmospheric conditions was obtained. Simultaneously, the simulated quality factor of the device under atmospheric conditions was calculated using the PQ model. .

[0064] Define the scaling factor as: ; For example, if the measured quality factor of a certain device in an atmospheric environment is The corresponding simulation quality factor under atmospheric conditions is Then the scaling factor This scaling factor represents the ratio between the simulated quality factor and the measured quality factor of the device.

[0065] Example 4: Predicting the True Quality Factor under Vacuum Conditions

[0066] Set the target vacuum environment pressure for The simulated quality factor under this vacuum environment was calculated using the PQ model. .

[0067] According to the method of the present invention, the predicted true quality factor of the device under this vacuum environment is: Substituting the numerical values ​​yields the prediction result. This allows for the prediction of the true quality factor of a vacuum environment using atmospheric environment test results.

[0068] Example 5: Verification of the stability of the proportional relationship

[0069] To verify the rationality of the method of the present invention, the quality factor of several devices was tested in atmospheric and vacuum environments, and compared with the simulated quality factor at the corresponding pressure points.

[0070] For each device, calculate separately: Atmospheric environmental proportion factor: ; Vacuum environment scaling factor: ;

[0071] If the experimental results show and If the relative deviation does not exceed 15%, it indicates that the proportional relationship between the simulated and measured values ​​of the device is approximately stable under different air pressures. Therefore, we can further conclude that: ; This leads to the theoretical basis for predicting the true quality factor of the vacuum environment in this invention.

[0072] Example 6: Device Screening Based on Predicted Vacuum Quality Factor

[0073] Let the minimum quality factor threshold required after device packaging be... If the prediction is obtained through the method of this invention If the device meets the quality factor requirements after vacuum packaging, it can proceed to the subsequent packaging process. If the prediction is accurate... If a device does not meet the subsequent performance requirements, it will not be included in the high-cost packaging process. This screening method can complete device classification and selection before packaging.

[0074] In summary, the vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes provided in this embodiment has the following advantages:

[0075] 1. This invention enables pre-assessment of device performance and optimizes the production process. It allows for obtaining the device's quality factor under vacuum conditions without waiting for vacuum packaging to complete. This moves the performance evaluation process from the later stages of production to the earlier stages, enabling device screening before the high-cost vacuum packaging process. This avoids wasting packaging, testing, and circuit adaptation resources caused by unqualified devices entering subsequent processes, effectively reducing ineffective investment in the production process.

[0076] 2. It balances the universality of the theoretical model with the individual differences of the devices. The PQ model established through finite element simulation can reflect the general law of quality factor variation with air pressure for similar devices, and has good universality. At the same time, by constructing a scaling factor using measured data at normal pressure, it is possible to specifically correct the systematic deviation between each device and the ideal model, solving the problem that pure simulation methods cannot reflect individual manufacturing differences, and making the prediction results more consistent with the actual performance of the devices.

[0077] 3. The prediction process is simple and easy to implement, facilitating engineering application. The finite element simulation method and atmospheric pressure quality factor testing method used in this invention are both mature and widely used technologies in the industry. No additional specialized equipment or processes need to be developed, and they can be directly deployed and applied to existing production and testing lines. The entire prediction process is logically clear and the calculation is simple, without adding excessive production time or labor costs.

[0078] 4. Wide applicability, meeting the needs of different scenarios. The PQ model established in this invention covers the entire working pressure range from atmospheric pressure to high vacuum, and is applicable to various common MEMS gyroscope structure types. Furthermore, the device selection criteria can be flexibly adjusted according to the performance requirements of different application scenarios, adapting to the differentiated needs of MEMS gyroscopes in multiple fields such as consumer electronics, industrial control, and aerospace.

[0079] 5. Improved controllability of the production process and product yield. Pre-screening allows for the early identification of low-performance components generated during manufacturing, reducing uncertainties in subsequent processes and making the production process more controllable. Simultaneously, it prevents defective products from entering the market, effectively improving the overall performance and reliability of the final product.

[0080] Working principle:

[0081] The energy dissipation of MEMS vibrating gyroscopes is determined by multiple damping mechanisms. Among them, air damping is directly related to ambient air pressure; the lower the air pressure, the weaker the air damping, and the higher the device's quality factor. As the air pressure drops to a certain level, the effect of air damping gradually weakens, and the quality factor tends to stabilize. Based on this physical law, the quality factor of the device under different air pressures can be calculated through finite element simulation to obtain the relationship between air pressure and quality factor. However, because the simulation model uses ideal parameters and boundary conditions, there is a systematic deviation between it and the actual manufactured device. Therefore, relying solely on simulation cannot obtain an accurate quality factor for individual devices. Furthermore, traditional testing methods can only be performed after the device has been vacuum-sealed, making it impossible to evaluate device performance before sealing.

[0082] This invention is based on a key discovery: for the same device, the proportional relationship between the simulated quality factor and the actual tested quality factor exhibits approximately stability under both atmospheric pressure and vacuum conditions. In other words, this systematic deviation does not change significantly with pressure variations but rather scales proportionally. Based on this discovery, this invention first establishes an analytical model that comprehensively considers multiple damping mechanisms. Through finite element simulation, simulated quality factor values ​​at multiple pressure points are obtained and fitted to form a continuous PQ model. This model reflects the overall trend of the device's quality factor changing with pressure under ideal conditions. Subsequently, without vacuum packaging, the quality factor is tested only under atmospheric pressure. This measured value is compared with the atmospheric pressure simulated value calculated by the PQ model to obtain a scaling factor reflecting the deviation between the individual device and the ideal model. Finally, this scaling factor is used to correct the simulated value under the target vacuum environment calculated by the PQ model, thus obtaining the predicted true quality factor of the device under vacuum conditions. Based on the prediction results, devices can be screened before the vacuum packaging process, allowing only those meeting performance requirements to proceed to the subsequent packaging process.

[0083] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the vacuum quality factor before packaging MEMS gyroscopes, characterized in that, Includes the following steps: Step S1: Based on the structural parameters, material parameters, and operating mode parameters of the MEMS vibrating gyroscope, establish a multi-damping analysis model of the device that comprehensively considers air damping and structural damping; Step S2: Under multiple discrete air pressure points, the simulated value of the quality factor corresponding to the MEMS vibrating gyroscope is obtained through finite element simulation, forming a discrete correspondence between air pressure and quality factor; Step S3: Based on the simulated quality factor values ​​at different pressure points, fit the discrete data to establish a pressure-quality factor relationship model, i.e., the PQ model. Step S4: Before the MEMS gyroscope is vacuum-sealed, the device is tested for quality factor in an atmospheric environment to obtain the measured atmospheric quality factor of the device. Step S5: Input the atmospheric environmental pressure into the PQ model to obtain the simulated quality factor value of the device under atmospheric conditions; Step S6: Compare the simulated quality factor value under atmospheric conditions with the measured quality factor value under atmospheric conditions to construct the scaling factor of the device; Step S7: Substitute the target vacuum environment pressure into the PQ model to obtain the simulated quality factor value of the device under the target vacuum environment; Step S8: Use the scaling factor to correct the simulated quality factor value under vacuum environment to obtain the true predicted quality factor value of the device under vacuum environment.

2. The vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes according to claim 1, characterized in that, The multi-damping analysis model described in step S1 comprehensively considers four damping mechanisms: sliding film damping, pressure film damping, thermoelastic damping, and anchor loss damping. The overall quality factor satisfies the following relationship: ； in, For the overall quality factor, This is the quality factor corresponding to the sluice damping. This is the quality factor corresponding to the pressure membrane damping. This is the quality factor corresponding to thermoelastic damping. This is the quality factor corresponding to anchor loss damping.

3. The vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes according to claim 1, characterized in that, The pressure range of the multiple discrete pressure points mentioned in step S2 covers to There should be no fewer than 5 pressure points, with at least 2 pressure points set in the normal pressure region and the high vacuum region respectively.

4. The vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes according to claim 1, characterized in that, The PQ model described in step S3 is fitted using any one of the following forms: polynomial, exponential, power function, or piecewise function. The polynomial PQ model expression is as follows: ;in, For the simulation quality factor, For ambient air pressure, , , , , These are the fitting parameters.

5. The vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes according to claim 1, characterized in that, The quality factor test under atmospheric conditions described in step S4 uses any one of the following methods: frequency response method, half-power bandwidth method, or free decay method.

6. The vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes according to claim 1, characterized in that, The scaling factor mentioned in step S6 is defined as follows: ;in, As a scaling factor, This represents the simulated quality factor value under atmospheric conditions. This represents the measured value of the quality factor under atmospheric conditions. This refers to atmospheric environmental pressure.

7. The vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes according to claim 6, characterized in that, The formula for calculating the predicted true quality factor under vacuum conditions in step S8 is as follows: ;in, This is the predicted value of the true quality factor under vacuum conditions. The simulated quality factor value under the target vacuum environment. The target vacuum environment pressure.

8. The vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes according to claim 1, characterized in that, It also includes step S9: screening devices based on the predicted true vacuum quality factor and setting a target quality factor threshold. ,like If the device meets the requirements for subsequent vacuum packaging and performance, it proceeds to the next step; if If the device does not meet the performance requirements for subsequent vacuum packaging, it will not proceed to the vacuum packaging process.

9. The vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes according to claim 1, characterized in that, The finite element simulation described in step S2 is performed in CoventorWare, ANSYS, or COMSOL software. During the simulation, the quality factor corresponding to each damping mechanism is calculated, and then the total quality factor is calculated based on the multi-damping comprehensive relationship.

10. The vacuum quality factor prediction method for pre-packaging screening of MEMS gyroscopes according to claim 1, characterized in that, The target vacuum environment pressure mentioned in step S7 The range is to .

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