Method and apparatus for passively identifying system parameters of a microelectromechanical system and microelectromechanical system

Abstract

The invention relates to a method and a device (1) for passive identification of system parameters of a micro-electro-mechanical system (MEMS-SYS). The method comprises the steps of: measuring (S-A) a signal emitted by a micro-electro-mechanical system (MEMS-SYS) excited by noise; determining (S-B) a frequency response of the measured signal; and identifying (S-C) system parameters (SP) of the micro-electro-mechanical system (MEMS-SYS) based on the frequency response of the measured signal. The invention also relates to a corresponding micro-electro-mechanical system.

Description

Technical Field

[0001] This invention relates to a method and apparatus for passively and continuously identifying system parameters of microelectromechanical systems (MEMS), particularly microelectromechanical sensors. The invention also relates to corresponding MEMS. Background Technology

[0002] MEMS stands for Microelectromechanical Systems, in which small, microscopic mechanical and electronic components are combined and integrated onto a single chip. MEMS can incorporate MEMS sensors, particularly speed sensors. A speed sensor vibrates continuously in a driven mode. The rotation applied to the sensor causes a shift in the detection mode due to the Coriolis force. The magnitude of this shift is proportional to the applied rotational speed.

[0003] Open-loop describes a system concept in which no control loops are used to adjust the position of the micromechanical components during the probing process. In an open-loop system, measurements are performed directly by acquiring the output signal, without active feedback to control vibration. Compared to closed-loop systems that employ feedback to improve modal stability, this method is simpler and less complex, but more sensitive to noise interference and temperature effects.

[0004] MEMS sensors manufactured in batch processes must be calibrated after production. During this calibration process, deviations caused by process variations are individually adjusted for each sensor based on a mathematical model.

[0005] However, immediately after calibration, the sensor may be subjected to certain disturbances, such as temperature and humidity changes, mechanical stress (e.g., bending of the circuit board to which the sensor is soldered), aging effects, linear and rotational acceleration, vibration, and mechanical shock. At this point, the sensor's calibration state is no longer optimal.

[0006] Such effects can lead to changes in performance metrics, such as offset and sensitivity, thus requiring recalibration with the highest possible accuracy. Due to the lack of controlled stimulation of sensors in the field, it is often difficult to adjust the mathematical models used in calibration and achieve sufficient accuracy. The diversity of specific customer applications further exacerbates this problem. Therefore, one goal of various compensation methods is to directly resist the possibility of changes in performance metrics. For this purpose, identified system parameters can be used as inputs, for example.

[0007] Therefore, known conventional methods are based on active excitation of the system to perform subsequent system identification (including identification of frequency splits). An example of continuous frequency tracking can be found in US11754397B2 "Micro-electromechanical systems (MEMS) gyroscope sensing frequency tracking".

[0008] System tracking is traditionally based on active excitation of MEMS components, with simultaneous or subsequent measurement of the system response. Through system identification, system parameters such as frequency splitting and quality factor can be determined. These system parameters can then be used in calibration and / or compensation to adjust the system's mathematical model accordingly.

[0009] However, a drawback of existing traditional methods is the need for active stimulation of the system. Active stimulation of the electromechanical system under study must be performed in a manner that ensures no impact on the system's useful signals during operation. To stimulate the system, additional signal sources and / or test signal electrodes are required. Summary of the Invention

[0010] According to a first aspect of the present invention, a method for passively identifying system parameters of a microelectromechanical system is provided, comprising the following steps: Measure signals emitted by a microelectromechanical system excited by noise; Determine the frequency response of the measured signal; And the system parameters of the microelectromechanical system are identified based on the frequency response of the measured signal.

[0011] This method offers the advantage of eliminating the need for additional signal sources and / or test signal electrodes to excite the involved microelectromechanical system (MEMS). This simplifies the manufacturing process and saves area when integrating the MEMS.

[0012] In one possible embodiment of the method according to the invention for passively obtaining system parameters of a microelectromechanical system, the amplitude-frequency response of the measured signal is determined for a specific frequency or frequency range. These frequency ranges may relate to a channel.

[0013] In one possible embodiment of the method for passively obtaining system parameters of a microelectromechanical system according to the present invention, the measured signal is sampled and filtered, and the frequency response of the signal is determined by performing a Fast Fourier Transform (FFT) on the filtered signal. The Fast Fourier Transform is an efficient algorithm for calculating the Discrete Fourier Transform. The FFT transforms the signal from the time domain to the frequency domain. The FFT algorithm obtains a series of discrete values ​​(e.g., measured values ​​of the signal) to determine the frequency response.

[0014] The method according to the invention is based on measurements of unfiltered and uncompensated signals from a noisy microelectromechanical system (MEMS), such as a MEMS speed sensor. By performing a Fourier transform on the noise signal, it can be decomposed into its constituent frequency components, which are then represented as the system's transfer function. From this frequency response, system parameters of the MEMS can be identified, particularly the frequency splitting and quality factor of the MEMS speed sensor.

[0015] In one possible embodiment of the method for passively obtaining system parameters of a microelectromechanical system according to the present invention, the system parameters of the microelectromechanical system are identified by means of curve fitting, three-point fitting, Kalman filtering or neural network based on the frequency response of the measured signal.

[0016] In one possible embodiment of the method for passively obtaining system parameters of a microelectromechanical system according to the present invention, the identified system parameters of the microelectromechanical system are stored. Preferably, this is done by storing them in the integrated storage unit of the microelectromechanical system.

[0017] In one possible embodiment of the method for passively obtaining system parameters of a microelectromechanical system according to the present invention, the microelectromechanical system is continuously monitored based on the identified system parameters. This method can be executed in the background without affecting the continuous operation of the microelectromechanical system.

[0018] In one possible embodiment of the method for passively obtaining system parameters of a microelectromechanical system according to the present invention, a compensation mechanism is performed based on the identified system parameters. This can also be performed after delivery based on system parameters stored in the storage unit of the delivered end product.

[0019] In one possible embodiment of the method for passively obtaining system parameters of a microelectromechanical system according to the present invention, the microelectromechanical system has a microelectromechanical sensor that emits a signal and the system parameters of the microelectromechanical sensor are identified.

[0020] In one possible embodiment of the method for passively obtaining system parameters of a microelectromechanical system according to the present invention, the microelectromechanical sensor includes a speed sensor that emits a signal and whose system parameters are identified. Speed ​​sensors are widely used and can be applied to various auxiliary systems.

[0021] In one possible embodiment of the method for passively obtaining system parameters of a microelectromechanical system according to the present invention, the system parameters of the speed sensor include the frequency split of the speed sensor and / or the quality factor of the speed sensor.

[0022] The method according to the present invention can identify system parameters (frequency splitting, quality factor, etc.) in a modally split MEMS speed sensor.

[0023] Furthermore, according to a second aspect, the present invention also provides an apparatus for passively identifying system parameters of a microelectromechanical system, the apparatus comprising: The measurement unit is designed to measure signals emitted by a noise-excited microelectromechanical system (MEMS). A frequency response determination unit, designed to determine the frequency response of a signal measured by a measurement unit; and The system parameter identification unit is designed to identify the system parameters of the microelectromechanical system based on the frequency response determined by the frequency response determination unit of the measured signal.

[0024] Furthermore, the present invention also provides a microelectromechanical system having means for passively identifying system parameters of the microelectromechanical system according to a second aspect of the present invention.

[0025] In one possible implementation of the microelectromechanical system (MEMS), the MEMS includes a speed sensor.

[0026] In one possible implementation of the microelectromechanical system (MEMS), the MEMS includes an acceleration sensor.

[0027] Other possible configurations, extensions, and implementations of the invention also include combinations of features of the invention not explicitly listed above or below in relation to the embodiments. In particular, those skilled in the art may add individual aspects as improvements or additions to any combination of the basic forms of the invention as reasonable. Attached Figure Description

[0028] The present invention will now be described in detail with reference to the embodiments shown in the accompanying drawings.

[0029] in: Figure 1 This is a schematic flowchart illustrating a possible implementation of the method of the present invention for passively obtaining system parameters of a microelectromechanical system; Figure 2 This is a schematic block diagram illustrating one possible implementation of the device of the present invention for passively identifying system parameters of a microelectromechanical system; Figure 3 Diagrams for explaining the method and apparatus of the present invention; Figure 4 Another diagram for explaining the method and apparatus of the present invention; Figure 5 The block diagram illustrates a possible embodiment of the microelectromechanical system of the present invention, which has an integrated device for passively identifying system parameters of the microelectromechanical system. Figure 6 This is a block diagram illustrating a possible implementation of a microelectromechanical system (MEMS) having a device connected thereto for passively identifying system parameters of the MEMS.

[0030] The accompanying drawings are intended to provide a further understanding of embodiments of the invention. They illustrate implementation methods and, in conjunction with the description, serve to explain the principles and concepts of the invention. Other embodiments and the numerous advantages mentioned will become apparent from the drawings. The elements in the drawings are not necessarily drawn to scale.

[0031] In the accompanying drawings, unless otherwise specified, the same elements, features and parts that are identical in function and action are labeled with the same reference numerals.

[0032] The following explanation will illustrate the identification of frequency splits, which, equivalently, can also be used to identify quality factors, etc. Furthermore, this principle applies not only to the detection of useful modes but also to the acquisition and evaluation of other higher-order modes. Detailed Implementation

[0033] According to a first aspect, the present invention provides a method for passively identifying system parameters of a microelectromechanical system (MEMS-SYS). One embodiment of the method is described in... Figure 1 The diagram is shown schematically.

[0034] In the first step SA, the signal output by the noise-excited MEMS-SYS is measured. The method of the present invention is based on measuring the unfiltered and uncompensated signal of the noise-excited MEMS-SYS (e.g., a MEMS speed sensor).

[0035] In the second step SB, the frequency response of the signal measured in step SA is determined or obtained. In one possible embodiment of the method of the present invention, the amplitude-frequency response of the measured signal for a specific frequency or frequency range is determined in step SB.

[0036] In one possible implementation, the measurement signal is sampled and filtered. In another possible implementation, the frequency response of the signal is determined by performing a Fast Fourier Transform (FFT) on the filtered signal. By calculating the Fourier Transform of the noise signal, the noise signal can be decomposed into its contained frequency components and represented as the system's transfer function. From this frequency response, for example, frequency splitting and quality factor can be identified.

[0037] exist Figure 3 In a further step SC of the method shown, the system parameters SP of the microelectromechanical system (MEMS-SYS) are identified based on the frequency response of the measurement signal obtained in step SB.

[0038] In one possible embodiment of the method of the present invention, in step SB, the system parameters SP of the microelectromechanical system (MEMS-SYS) are identified based on the frequency response of the measured signal through curve fitting, three-point fitting, Kalman filtering, or by a correspondingly trained artificial neural network. Subsequently, the identified system parameters SP of the MEMS-SYS are cached. In data analysis and statistics, curve fitting refers to adapting a mathematical function to a set of data points to model the relationships between variables. Its purpose is to find a function that can describe the underlying structure or relationships in the collected data as well as possible.

[0039] The method of this invention enables passive and continuous system identification. There is no need to interrupt sensor signal detection to stimulate the system. This allows for adjustment of sensor parameters SP during normal sensor operation. Therefore, the adapted MEMS-SYS, particularly MEMS sensors, obtained by implementing the method of this invention, can adapt to dynamic changes in environmental conditions, thereby exhibiting superior performance.

[0040] Furthermore, the method of this invention can also be used to detect slow, time-dependent changes within or in the environment of a microelectromechanical system (MEMS-SYS) (System Monitoring, MON), and trigger subsequent actions if necessary. An example of this is the potting of a sensor. If, for example, a crack exists, it will cause a sudden change in pressure inside the sensor, which can be detected well by known sensor signals. However, if the pressure inside the sensor changes slowly over time, its quality factor also decreases very slowly. Therefore, detection using known sensor signals would be very difficult or even impossible using conventional methods. The method of this invention can also identify such slow aging processes.

[0041] In one possible implementation of the method for passively obtaining system parameters SP of a microelectromechanical system (MEMS-SYS) according to the present invention, the MEMS-SYS is continuously monitored based on the identified and cached system parameters SP.

[0042] In one possible implementation of the method for passively obtaining system parameters SP of a microelectromechanical system (MEMS-SYS) according to the present invention, a compensation mechanism is implemented based on the identified and cached system parameters SP.

[0043] In one possible embodiment of the method for passively obtaining system parameters of a microelectromechanical system according to the present invention, the microelectromechanical system (MEMS-SYS) has a microelectromechanical sensor that emits a signal and whose system parameter SP is identified.

[0044] In one possible embodiment of the method according to the invention, the microelectromechanical sensor has a rotation speed sensor that emits a signal and whose system parameter SP is identified.

[0045] The method according to the present invention can efficiently identify the system parameters SP (such as frequency splitting, quality factor, etc.) of a modally split open-loop MEMS speed sensor. A modally split open-loop MEMS speed sensor is a special type of gyroscope used in microelectromechanical systems (MEMS) to measure angular velocity. It operates based on the principle of vibration sensing technology and utilizes the Coriolis effect to detect rotational speed.

[0046] Modal splitting refers to the driving mode and the probe mode being at separate frequencies, i.e., frequency splitting with a mutual offset, for example, 1 to 6 kHz. Modal splitting gyroscopes are based on two different vibrational modes of a moving microelectromechanical resonator. Frequency splitting refers to the difference between the resonant frequencies of the driving mode and the probe mode. The driving mode is a mechanical vibration excited by external electronic equipment, while the probe mode measures the response to rotation about a specific axis. The rotational rate applied to the system, together with the orthogonal primary vibration (driving mode), generates a Coriolis force, which causes a deflection of the secondary vibration (probe mode). This deflection is detected. In MEMS speed sensors (gyroscopes), the driving mode and the probe mode play a central role. These two terms describe the vibrational state in a mechanical system used to measure angular velocity. The driving mode refers to the actively generated mechanical vibration in the sensor to achieve the measurement of rotational speed. An actuator, typically electrodynamic or piezoelectric, excites a mass block in the sensor to vibrate. This vibration preferably occurs at the system's natural frequency to maximize energy efficiency. The vibration occurs in a specific direction or plane. Vibration generates an output signal, which is influenced by rotational motion. The driving mode represents the primary mechanical motion that can be used to measure the Coriolis force. The probe mode is a mechanical vibration or displacement generated and measured as a response to rotational motion. When the sensor is rotating, the Coriolis force acts perpendicular to the driving direction. This force causes the mass to vibrate along a second orthogonal direction. The motion of the probe mode is typically orthogonal to the motion of the driving mode. The amplitude or frequency of the detected vibration is proportional to the angular velocity. This motion is measured by capacitive or piezoelectric elements.

[0047] In one possible embodiment of the method according to the invention for passively obtaining system parameters SP of a microelectromechanical system (MEMS-SYS), the system parameters SP of the speed sensor have the frequency split of the speed sensor and / or the quality factor of the speed sensor.

[0048] The quality factor of a gyroscope (speed sensor) is an indicator of the energy efficiency of a mechanical system during its vibrations. It is often referred to as the Q factor and has a decisive impact on the sensor's performance. The quality factor is defined as the ratio of stored energy to energy lost per vibration cycle.

[0049] A high Q-factor means that the sensor loses less energy through internal or external losses and is therefore more sensitive. A higher quality factor results in a stronger amplification of the Coriolis force, leading to better detection of small rotational motions. A high Q-factor reduces the effects of noise and improves signal stability, especially in feedbackless systems (open-loop systems).

[0050] In modal split-type speed sensors, the most important system parameters SP, besides frequency splitting and quality factor, include natural frequency, Coriolis sensitivity, modal stiffness, and damping loss. Optimized design aims to achieve both high quality factor and high sensitivity, while reducing noise and temperature dependence.

[0051] like Figure 2 As illustrated, according to a second aspect of the present invention, an apparatus 1 for passively identifying system parameters SP of a microelectromechanical system (MEMS-SYS) is also provided. The apparatus includes a measurement unit 2 designed to measure a signal emitted by the MEMS-SYS excited by noise R. The apparatus 1 further includes a frequency response determination unit 3 designed to determine the frequency response of the signal measured by the measurement unit 2. Furthermore, the apparatus 1 includes a system parameter identification unit 4 designed to identify the system parameters SP of the MEMS-SYS based on the frequency response to the measured signal determined by the frequency response determination unit 3.

[0052] According to another aspect of the present invention, a microelectromechanical system (MEMS-SYS) is also provided, which includes a device 1 integrated therein or connected thereto for passively identifying system parameters of the MEMS-SYS according to a second aspect of the present invention. In one possible embodiment of the MEMS-SYS, the MEMS-SYS has a rotational speed sensor. In another possible embodiment of the MEMS-SYS, the MEMS-SYS has an acceleration sensor.

[0053] In one possible implementation, the microelectromechanical system (MEMS-SYS) has at least one microelectromechanical sensor, particularly a rotation speed sensor, which is monitored by the method according to the invention and the device 1 according to the invention connected thereto.

[0054] The uncompensated and unfiltered probe signal of a microelectromechanical system (MEMS-SYS) (taking a speed sensor as an example) is first sampled and provided at a sufficiently high sampling rate, or sampling speed. Here, the sampling rate is sufficient to satisfy the Nyquist-Shannon sampling theorem.

[0055] In the method of the present invention, the microelectromechanical system (MEMS-SYS) is not excited by a dedicated electrode, but rather by making full use of the existing excitation of the MEMS-SYS by noise R.

[0056] In the first step SA of the method of the present invention, the signal emitted by the microelectromechanical system (MEMS-SYS) excited by noise R is measured. Importantly, the sum of the noise sources must always provide a sufficiently large excitation to the MEMS-SYS. Possible noise sources include, for example, Brownian noise and noise from the applied voltage.

[0057] Figure 3 The diagram schematically illustrates the application of a noise signal R to a MEMS system (e.g., a speed sensor) with a transfer function H. This MEMS system outputs a time signal SIG. After sampling, filtering, and transforming the time signal SIG into a spectrum or frequency response, the system parameters SP of the MEMS system can be identified through the system parameter identification SP-IDENT. These system parameters SP include, for example, frequency, quality factor, phase, gain, mechanical parameters, or manufacturing parameters. Subsequently, an algorithm can be executed based on the obtained system parameters SP to calculate the compensation rule KOMP and / or to adjust the operating state of the MEMS-SYS. The MEMS-SYS can then be fine-tuned TRIM to compensate for environmental influences and / or aging processes. This is preferably performed continuously during the operation of the MEMS-SYS. Alternatively or additionally, a system monitoring SYS-MON can be performed on the MEMS-SYS, which also runs in the background during the operation of the MEMS-SYS, such as... Figure 3 As shown.

[0058] In order to enable system parameter identification SP-IDENT based on the noise signal R, in one possible embodiment of the method of the present invention, the amplitude frequency response is calculated in step SB by Fourier transform (e.g., FFT) at least for a specified frequency or frequency range. The sampled signal may be filtered before Fourier transform.

[0059] Based on the frequency response determined in step SB, system parameter identification (SP-IDENT) is then performed in the third step SC to, for example, determine the frequency split of the probe mode. This frequency split can be identified at the resonant frequency location in the frequency response. One possible implementation may also involve averaging multiple successively calculated frequency responses.

[0060] As in Figure 3 The additional embedded loops shown in the flowchart can also operate at a higher repetition rate. To further identify the system parameters SP, methods such as curve fitting, three-point fitting, or the application of Kalman filters and artificial neural networks can be employed.

[0061] The system parameters SP determined in this way can be used for continuous online system monitoring of MEMS-SYS. Furthermore, these data can be used in subsequent algorithms to adjust the calculation rules of the compensation mechanism or to adjust the operating state. Possible compensation mechanisms can be directly applied to the corresponding measured quantities or directly to the MEMS-SYS, such as sensor systems. An example of a direct application is, for instance, continuous tracking of the stopband of a notch filter in the signal path of a MEMS speed sensor.

[0062] exist Figure 4 The method according to the invention enables efficient measurement of frequency splitting during manufacturing and subsequent continuous measurement in the field. Continuous self-calibration in the field improves performance by compensating for effects such as temperature, humidity, and mechanical stress.

[0063] During the production or manufacturing of the FERT MEMS-SYS, frequency splitting of the MEMS sensor can be identified in step S1 without actively stimulating the MEMS-SYS. This is achieved in the first sub-step S11 by measuring the probe channel of the MEMS sensor, such as a gyroscope. Subsequently, during the manufacturing of the FERT MEMS-SYS, in another sub-step S12, an FFT transformation is performed on the time signal SIG, and system parameters SP, such as frequency splitting or quality factor, are identified. The obtained system parameters SP and corresponding compensation parameters KP can be stored in the storage unit in step S2. Additional parameters can be measured in step S3. These parameters can be stored in the storage unit of the manufactured MEMS-SYS in step S4. Furthermore, in step S4, the parameters derived therefrom can be stored in the storage unit of the manufactured MEMS-SYS.

[0064] Optionally, the computational rules for individual sensors can be created during production in step S5 and stored in the storage unit of the manufactured MEMS-SYS. Further manufacturing steps, such as tape packaging, can be performed in another step S6. The manufactured MEMS-SYS is transported and can be soldered onto the final product EP. Conditioning and adaptation to environmental, mechanical shock, and aging processes may then be performed in step S6.

[0065] If frequency splitting or other system parameters SP-ÄND change, the system parameters SP and the corresponding compensation parameters KP will no longer be precisely aligned. Therefore, in the final product EP containing the MEMS-SYS that has been delivered and is in the field, continuous passive self-calibration of the MEMS-SYS can be performed in step S7. For this purpose, in the first sub-step S71, the probe channels of the MEMS-SYS, especially the MEMS speed sensor, are measured. Furthermore, in sub-step S73, other parameters on all three axes of the MEMS speed sensor can be identified based on the resulting system response. Additional parameters may be measured in sub-step S73 and / or the memory cells of the MEMS-SYS may be read in sub-step S74. Subsequently, in sub-step S75 of the self-calibration step S7, the calculation rules created for the individual sensor in step S5 are executed for the MEMS sensor. Then, the compensation matrix can be adjusted in step S8. Through the method of the present invention, a significant improvement in the performance of the MEMS-SYS can be achieved.

[0066] Figure 5 and Figure 6 This illustrates possible implementations of the present invention, namely, either directly implemented within a MEMS sensor (e.g., ...). Figure 5 (as shown), or implemented in external signal processing connected to it (such as... Figure 6 (As shown).

[0067] Figure 5 and Figure 6 This diagram illustrates an inertial measurement unit (IMU) as an example of a microelectromechanical system (MEMS). The gyroscope unit (GYR) is connected to the digital processor (DP) of the IMU via an analog front-end (AFE). The AFE in a MEMS gyroscope sensor is a circuit component that processes the sensor's analog signals and prepares them for further digital processing. This AFE can act as a measurement unit 2 to measure and amplify electrical signals from the MEMS structure, making them available for analysis and evaluation. The AFE can perform analog filtering on the acquired high-frequency sensor signals to improve the signal quality of the measured sensor signals. Furthermore, the AFE may have an analog-to-digital converter (ADC) that samples the analog signals at a sampling rate and provides the sampled signals to the IMU's digital processor (DP) for data processing. The digital processor (DP) provides a digital time signal (SIG), which is filtered by a filtering unit (FIL). Figure 5 , 6 As shown, the filtered time signal is converted into a digital spectrum FSPE by the conversion unit 3, which represents the frequency response of the MEMS sensor.

[0068] like Figure 5 , 6 As shown, in one possible implementation, a filtering unit FIL is provided for filtering the digital sensor signal SIG. In the illustrated implementation, FFT unit 3 performs a Fast Fourier Transform and provides the spectrum FSPE of the sensor signal SIG. Identification unit 4 performs curve fitting to obtain system parameters SP. These system parameters SP include, for example, the frequency splitting and quality factor Q of the sensor for a specific frequency range or frequency channel CH. The obtained system parameters SP can be fed to the compensation and filtering unit KFE of the digital processor DP to perform passive continuous monitoring of the MEMS system based on the obtained system parameters SP. In this illustrated implementation, the compensation and filtering unit KFE of the digital processor DP provides a rate / quadrature signal R / Q-Sl, such as Figure 5 , 6 As shown. In addition to using the passive continuous method according to the present invention to passively identify the system parameters SP of the MEMS-SYS, it is also possible to combine it with the active method.

[0069] The method of this invention includes a passive, continuous approach for system identification (e.g., frequency splitting measurement) in MEMS systems, particularly in MEMS speed sensors. This approach may include continuous self-detection and / or continuous self-calibration without active excitation. The MEMS sensor can be dynamically adjusted using the method of this invention when measurements are performed under different sensor conditions.

[0070] The method according to the invention is applicable to various MEMS systems, particularly in the automotive, consumer, and industrial sectors, for example, for speed sensors and acceleration sensors. This method can also be applied to other resonant sensor systems.

[0071] Although the invention has been fully described above with reference to preferred embodiments, the invention is not limited thereto and can be modified in various ways.

Claims

1. A method for passively identifying system parameters (SP) of a microelectromechanical system (MEMS-SYS), comprising the following steps: The measurement (SA) is the signal emitted by the microelectromechanical system (MEMS-SYS) excited by noise (R); Determine the frequency response of the signal measured by (SB); and The system parameters (SP) of the microelectromechanical system (MEMS-SYS) are identified (SC) based on the frequency response of the measured signal.

2. The method for passively obtaining system parameters of a microelectromechanical system according to claim 1, characterized in that, Determine the amplitude-frequency response of the measured signal for a specific frequency or frequency range.

3. The method for passively obtaining system parameters of a microelectromechanical system according to claim 1 or 2, characterized in that, The measured signal is sampled and filtered, and the frequency response of the signal is determined by the frequency response determination unit (3) using the Fast Fourier Transform (FFT) of the filtered signal.

4. The method for passively obtaining system parameters of a microelectromechanical system according to any one of claims 1 to 3, characterized in that, The system parameters (SP) of the microelectromechanical system (MEMS-SYS) are identified by the identification unit (4) based on the frequency response of the measured signal, using curve fitting, three-point fitting, Kalman filtering or artificial neural network.

5. The method for passively obtaining system parameters of a microelectromechanical system according to any one of claims 1 to 4, characterized in that, Store the identified system parameters (SP) of the microelectromechanical system (MEMS-SYS).

6. The method for passively obtaining system parameters of a microelectromechanical system according to any one of claims 1 to 5, characterized in that, The microelectromechanical system (MEMS-SYS) is continuously monitored based on the identified system parameters (SP).

7. The method for passively obtaining system parameters of a microelectromechanical system according to any one of claims 1 to 6, characterized in that, The compensation mechanism is executed based on the identified system parameters (SP) of the microelectromechanical system (MEMS-SYS).

8. The method for passively obtaining system parameters of a microelectromechanical system according to any one of claims 1 to 7, characterized in that, The microelectromechanical system (MEMS-SYS) has a microelectromechanical sensor that emits a signal and whose system parameters (SP) are identified.

9. The method for passively obtaining system parameters of a microelectromechanical system according to claim 8, characterized in that, The microelectromechanical sensor has a speed sensor that emits a signal, and the system parameters (SP) of the speed sensor are identified.

10. The method for passively obtaining system parameters of a microelectromechanical system according to claim 9, characterized in that, The system parameters (SP) of the speed sensor include the frequency split of the speed sensor and the quality factor of the speed sensor.

11. A device (1) for passively identifying system parameters of a microelectromechanical system (MEMS-SYS), the device comprising: Measurement unit (2), which is designed to measure signals emitted by a microelectromechanical system (MEMS-SYS) excited by noise (R); A frequency response determination unit (3) is designed to determine the frequency response of the signal measured by the measurement unit (2); as well as The system parameter identification unit (4) is designed to identify the system parameters (SP) of the microelectromechanical system (MEMS-SYS) based on the frequency response determined by the frequency response determination unit (3) of the measured signal.

12. A microelectromechanical system (MEMS-SYS) having a device (1) for passively identifying system parameters (SP) of a microelectromechanical system (MEMS-SYS) according to claim 11.

13. A microelectromechanical system, characterized in that, The microelectromechanical system (MEMS-SYS) has a speed sensor or an acceleration sensor.

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