Automatic calibration method for inertial MEMS sensor

Abstract

The invention relates to an automatic calibration method for an inertial MEMS sensor. The sensor module includes a pattern generator configured to generate a variable frequency self-test signal. The sensor module includes an inertial sensor including a self-test electrode that receives a frequency sweep self-test signal. The inertial sensor generates an analog sensor signal based on the self-test signal. The sensor module includes: an analog-to-digital converter that generates a digital sensor signal based on an analog sensor signal; and a demodulator including a first input to receive the digital sensor signal, a second input to receive the self-test signal, and an output to output a demodulated signal. The sensor module includes a first low pass filter coupled to an output of the demodulator and configured to generate a baseband signal. The sensor module includes a calibration circuit configured to identify different MEMS characteristics, such as resonant frequency, Q factor, or sensitivity, based on the baseband signal.

Description

Technical Field

[0001] The present disclosure relates generally to inertial sensors and, more particularly, to calibration of inertial sensors. Background Art

[0002] Inertial sensors are often implemented as micro-electromechanical systems (MEMS). MEMS inertial sensors are complex systems based on the interaction between mechanical, analog, and digital components. Process variations during manufacturing or drift over time due to temperature or aging can affect the performance of inertial sensing.

[0003] The requirements for accurate performance of MEMS inertial sensors are increasing. The basic performance of MEMS inertial sensors often does not meet the performance requirements without a trimming procedure for parameter calibration. Some calibration techniques can include open-loop step response, closed-loop phase-locked testing, self-test checks, and other methods. However, these traditional techniques often cannot meet the requirements for advanced performance specifications.

[0004] All topics discussed in the background section are not necessarily prior art and should not be considered prior art simply because they are discussed in the background section. Along these lines, any identification of problems in the prior art or associated with such topics discussed in the background section should not be considered prior art unless explicitly stated as prior art. Instead, the discussion of any topic in the background section should be considered part of the inventor's approach to solving a particular problem, which itself may also be inventive. Summary of the invention

[0005] Embodiments of the present disclosure provide efficient and effective calibration for MEMS inertial sensors. The calibration process is accurate and takes very little time. Embodiments of the present disclosure utilize dedicated self-test electrodes to inject known electromechanical displacements into the mechanical components of the MEMS inertial sensor. The MEMS inertial sensor outputs a sensor signal based on the test signal. The overall components of the sensor module analyze the resulting sensor signal. The digital portion of the sensor module can quickly extract parameters from the test signal, such as the resonant frequency of the MEMS inertial sensor, the Q factor of the MEMS inertial sensor, and the sensitivity parameters of the MEMS inertial sensor.

[0006] In one embodiment, a method includes using an inertial sensor, generating an analog sensor signal by applying a self-test signal to a self-test electrode of the inertial sensor, converting the analog sensor signal into a digital sensor signal, and using a first demodulator, generating a demodulated signal by passing the self-test signal and the digital sensor signal to the first demodulator. The method includes generating a baseband signal by passing the first demodulated signal through a low-pass filter, identifying a zero-crossing frequency and a first peak frequency in the baseband signal using a calibration circuit, and calibrating the inertial sensor based on the zero-crossing frequency and the first peak frequency.

[0007] In one embodiment, the method includes applying a frequency swept self-test signal to a test electrode of an inertial sensor and a first demodulator of the inertial sensor, and generating a first digital signal using the inertial sensor based on the frequency swept self-test signal. The method includes generating a demodulated signal by demodulating the digital sensor signal and the frequency swept self-test signal, generating a baseband signal by passing the demodulated signal through a digital low-pass filter, and identifying a resonant frequency of the inertial sensor by analyzing the baseband signal using a calibration circuit.

[0008] In one embodiment, the device includes: a pattern generator configured to generate a frequency swept self-test signal; and an inertial sensor including a self-test electrode configured to receive the frequency swept self-test signal. The inertial sensor is configured to generate an analog sensor signal based on the self-test signal. The device includes an analog-to-digital converter configured to generate a digital sensor signal based on the analog sensor signal. The device includes a demodulator, the demodulator including a first input configured to receive the digital sensor signal, a second input configured to receive the self-test signal, and an output configured to output a demodulated signal. The device includes a first low-pass filter, the first low-pass filter is coupled to the output of the demodulator and is configured to generate a baseband signal. The device includes a calibration circuit, the calibration circuit is configured to identify the resonant frequency of the inertial sensor by analyzing the baseband signal.

[0009] A sensor module according to the principles of the present disclosure can provide various benefits. For example, the sensor module can provide real-time and immediate (on the fly) measurement and calibration. The sensor module can be immune to external inertial motion because the correlation between the injection / sensing signals is independent of the external signal. The sensor module can be immune to the amplitude differences of the analog chain, whether temperature or aging. If a digital square pattern is used, complex analog circuits such as DACs can be avoided. If a digital square pattern is used, the digital demodulation is a simple symbol version. This simplifies digital design. Final testing in the factory can be accelerated using simple and smaller routines. Zero crossing detection of baseband signals is much simpler than peak-based identification points. The calibration process can be performed outside the CPU. This is beneficial in terms of power, area, and programming. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1A is a block diagram of a sensor module according to one embodiment.

[0011] Figure 1B is a block diagram of a sensor module according to one embodiment.

[0012] Figure 2 is a graph illustrating a test signal corresponding to a square wave frequency sweep according to one embodiment.

[0013] Figure 3 is a graph illustrating a baseband signal generated by processing a sensor signal output from an inertial sensor in response to a test signal according to one embodiment.

[0014] Figure 4 is a graph of a sensor signal from a MEMS inertial sensor according to one embodiment.

[0015] Figure 5 is a simplified diagram of an inertial sensor according to one embodiment.

[0016] Figure 6 is a flow chart of a method of operating an inertial sensor according to one embodiment.

[0017] Figure 7 is a flow chart of a method of operating an inertial sensor according to one embodiment. DETAILED DESCRIPTION

[0018] In the following description, certain specific details are set forth in order to provide a thorough understanding of the various disclosed embodiments. However, one skilled in the relevant art will recognize that the embodiments can be practiced without one or more of these specific details or with other methods, components, materials, etc. In other instances, well-known systems, components, and circuit systems associated with integrated circuits have not been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0019] Unless the context requires otherwise, throughout the specification and the claims that follow, the word "comprise" and variations thereof, such as "comprises" and "comprising," should be interpreted in an open, inclusive sense, i.e., to mean "including but not limited to." Furthermore, unless the context clearly dictates otherwise, the terms "first," "second," and similar sequential indicators should be interpreted as interchangeable.

[0020] Throughout the specification, references to "one embodiment" or "an embodiment" refer to a particular feature, structure, or characteristic described in conjunction with the embodiment included in at least one embodiment. Thus, various places where the phrase "in one embodiment" or "in an embodiment" appears throughout the specification do not necessarily all refer to the same embodiment. Furthermore, in one or more embodiments, the particular features, structures, or characteristics may be combined in any suitable manner.

[0021] As used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural references unless the content clearly dictates otherwise. It should also be noted that the term "or" is generally used in the broadest sense, i.e., as meaning "and / or", unless the content clearly dictates otherwise.

[0022] Figure 1A is a block diagram of a sensor module 100 according to one embodiment. The sensor module 100 corresponds to a MEMS sensor module and includes an inertial sensor 102. As will be explained in more detail below, the components of the MEMS sensor module 100 cooperate to enable effective and efficient calibration of the components of the sensor module 100.

[0023] The inertial sensor 102 is a MEMS inertial sensor. The MEMS inertial sensor 102 senses inertial forces. The inertial forces may include linear acceleration, gravity, rotational motion, or other types of inertial forces.

[0024] In one embodiment, Figure 1A The inertial sensor 102 is an accelerometer. Therefore, the inertial sensor 102 senses linear acceleration or force. The inertial sensor 102 can sense gravity and acceleration.

[0025] The inertial sensor 102 may include one or more sensing masses. The sensing masses may be suspended or coupled to one or more fixed masses or one or more other movable masses by mechanical springs. The inertial sensor 102 may include sensing electrodes, such as capacitive electrodes, which will output capacitive sensor signals based on the position of the one or more sensing masses. Although the examples herein primarily deal with capacitive sensing in the inertial sensor 102, other types of sensing may be performed, such as piezoelectric, piezoresistive, and other types of sensing.

[0026] In one embodiment, the inertial sensor 102 is configured as a three-axis accelerometer. Thus, the inertial sensor 102 can sense acceleration along each of three mutually orthogonal axes. The inertial sensor 102 can include a separate sensing mass for each sensing axis. Alternatively, the inertial sensor 102 can sense along only a single axis or along two axes. Other types of accelerometers can be utilized without departing from the scope of the present disclosure.

[0027] The sensor module 100 includes a mechanical portion 103, an analog portion 105, and a digital portion 107. The mechanical portion 103 includes an inertial sensor 102. As used herein, the term "inertial sensor" may refer to a mechanical sensor portion that includes a movable sensing mass, a drive mass, a sensing electrode, a drive electrode, or other mechanical or electromechanical components. As used herein, the term "sensor module" may correspond to a combination of mechanical, analog, and digital components that together generate and process inertial sensor signals for use by components and devices external to the sensor module 100.

[0028] Mechanical part 103, analog part 105 and digital part 107 cooperate to generate usable sensor signals. As used herein, the mechanical part can generate a capacitive sensor signal. The analog part 105 can generate a voltage-based analog sensor signal from the capacitive sensor signal. The analog part can also convert the voltage-based analog sensor signal into a digital sensor signal. The digital part 107 can process or adjust the digital sensor signal for use by circuits, components or devices outside the sensor module 100.

[0029] Sensor module 100 may be implemented in one or more semiconductor dies. For example, inertial sensor 102 may be implemented in a first integrated circuit die. Analog portion 105 and digital portion 107 may be implemented in separate integrated circuit dies and coupled to an integrated circuit die including inertial sensor 102. The integrated circuit die including analog portion 105 and digital portion 107 may correspond to an application specific integrated circuit (ASIC). Alternatively, the entire sensor module may be implemented in a single integrated circuit die.

[0030] One or more integrated circuits of the sensor module 100 are initially processed in a semiconductor wafer. During processing, the semiconductor wafer undergoes a large number of processing steps, such as material deposition, doping, etching, epitaxial growth processes, thermal annealing, and various types of processing steps. Due to process variations, a first sensor module 100 formed by one or more first wafers may have slightly different characteristics than a second sensor module 100 formed by one or more second wafers. For example, the Q factor, resonant frequency, and sensitivity of the inertial sensor 102 are based in part on the processing conditions. Therefore, two substantially identical sensor modules 100 may have slightly different Q factors, resonant frequencies, and sensitivities.

[0031] Due to these changes, it may be beneficial to calibrate the sensor module 100 before using the sensor module 100. In addition, it may be beneficial to calibrate the sensor module 100 at different times throughout the service life of the sensor module 100. The inertial sensor 102 includes a self-test electrode 104 to assist in the calibration process from the sensor module 102. The self-test electrode 104 may include a self-test drive electrode and a self-test sensing electrode. The self-test drive electrode may receive a test signal as part of the calibration process. The test signal causes motion in the inertial sensor 102. The self-test sensing electrode generates a self-test sensor signal based on the self-test drive signal. As will be described in more detail below, the sensor module 100 is calibrated using the test signal and the self-test sensor signal. Specifically, the Q factor, resonant frequency, and sensitivity of the inertial sensor 102 are identified using a test signal of the self-test sensor signal.

[0032] The digital portion 107 of the sensor module 100 includes a pattern generator 106. The pattern generator 106 generates a test signal having a selected pattern. The test signal may include a square wave, a sine wave, a sawtooth wave, or other types of waveforms. In one embodiment, the pattern generator 106 generates the test signal within a selected frequency range. In an example where the test signal is a square wave, the pattern generator 106 continuously increases the frequency of the square wave from a starting frequency to an ending frequency. The starting frequency and the ending frequency are selected so that the resonant frequency of the inertial sensor 102 falls between the starting frequency and the ending frequency.

[0033] In one embodiment, it is particularly beneficial to use a square wave frequency sweep for the test signal. This is because a square wave has only two values ​​(high and low). This makes it possible to have a single bit test signal without a digital-to-analog converter (DAC) between the pattern generator 106 and the inertial sensor 102. If a multi-bit signal is desired, such as for a sine wave, sawtooth wave, or various other waveforms, a DAC can be implemented between the pattern generator 106 and the inertial sensor 102 to convert the digital pattern to an analog waveform.

[0034] The pattern generator 106 applies a test signal to the self-test electrode 104 of the inertial sensor 102. Specifically, the test signal is provided to the self-test drive electrode to induce motion in the mass of the inertial sensor 102. The sensing self-test electrode of the inertial sensor 102 generates a self-test sensor signal based on the induced motion. In an example where the inertial sensor 102 is a capacitive inertial sensor, the self-test sensor signal may correspond to a capacitive signal.

[0035] The analog portion 105 of the sensor module 100 includes a capacitance-to-voltage (C2V) converter 110 coupled to the inertial sensor 102. During calibration, the capacitance-to-voltage converter 110 receives a capacitance self-test sensor signal from the inertial sensor 102 and generates a voltage-based analog sensor signal.

[0036] Analog portion 105 also includes an analog-to-digital converter (ADC) 112. ADC 112 receives the analog sensor signal from capacitance-to-voltage converter 110 and converts the analog sensor signal into a digital sensor signal. Analog portion 105 may include other components without departing from the scope of the present disclosure.

[0037] The digital portion 107 includes a low pass filter 114. The low pass filter 114 is a digital low pass filter. The digital low pass filter 114 allows relatively low frequency signals to pass through and filters out higher frequency signals and high frequency noise. The input of the digital low pass filter 114 is coupled to the output of the ADC 112. Therefore, the digital low pass filter 114 receives the digital sensor signal from the ADC 112. The digital low pass filter 114 outputs a filtered digital signal.

[0038] The digital section 107 includes a demodulator 116. The demodulator 116 receives the filtered digital signal from the low pass filter 114 and receives the test signal from the pattern generator 106. The demodulator 116 generates a demodulated signal. Additional details regarding the demodulator and the demodulated signal are provided below.

[0039] The digital section 107 may include a first delay block 108 coupled between the pattern generator 106 and the inertial sensor 102. The first delay block 108 may delay the test signal by a selected amount. The delay block 108 may include a plurality of buffers, each providing a small amount of delay. The number of connected buffers may be selected to provide the desired delay.

[0040] The digital section 107 may also include a second delay block 118. The second delay block 118 is coupled between the pattern generator 106 and a second input of the demodulator 116. The second delay block 118 may also include a plurality of buffers that may be selectively connected or disconnected to provide a desired amount of delay.

[0041] The digital section 107 includes a second low pass filter 120 coupled to the output of the demodulator 116. The second low pass filter 120 receives the demodulated signal and generates a baseband signal from the demodulated signal by filtering out higher frequencies from the demodulated signal. Figure 3 shown.

[0042] The digital portion 107 includes a calibration circuit 122. The calibration circuit 122 receives the baseband signal, analyzes the baseband signal, and identifies one or more parameters associated with the inertial sensor 102 based on the baseband signal. For example, the calibration circuit can identify the resonant frequency "Fr", the Q factor "Q", and the sensitivity "S" based on the baseband signal. Additional details about these parameters are provided below.

[0043] The digital sensor signal is demodulated by the demodulator 116 to generate a digital beat. The low pass filter 120 shifts the demodulated signal to baseband by low pass filtering the high frequency components of the demodulated signal. The principles of the present disclosure generate a digital beat between the digital sensor signal and the test signal in order to detect a 90° phase shift between them. This corresponds to the resonant frequency Fr of the inertial sensor 102. Before discussing the resonant frequency in more detail, the following will be discussed. Figure 2 and Figure 3 Discuss test signals and baseband signals.

[0044] Reference Figure 2 , Figure 2 A curve 200 of a test signal according to one embodiment is illustrated. The test signal corresponds to a frequency sweep of a square wave. At time t=0, the test signal has a starting frequency Fstart. At completion time Tsweep, the test signal has an ending frequency Fstop. Between time t=0 and time Tsweep, the frequency gradually increases. This corresponds to a frequency sweep. Fstart and Fstop are selected so that the resonant frequency Fr of the inertial sensor 102 (which can be roughly estimated based on the design of the inertial sensor 102) falls between Fstart and Fstop. As previously described, the test signal induces motion in the inertial sensor 102. The digital sensor signal corresponds to a digitized representation of this motion.

[0045] Although Figure 2 A square wave frequency sweep is illustrated, but the test signal may include other types of patterns having a fundamental frequency that spans in any direction around a resonant peak. For example, the test signal may include blocks of different frequency patterns rather than a frequency sweep. Thus, the test signal may correspond to a variable frequency self-test signal. The self-test signal may include a pseudo-random sequence.

[0046] Reference Figure 3 , Figure 33 is a graph 300 illustrating a baseband signal according to one embodiment. The baseband signal is in the frequency domain. The baseband signal has an initial frequency Fstart and a final frequency Fstop. Figure 3 There are three defined points in : ωa, ωb, and ωc. ωa is the first peak, ωb is the zero crossing, and ωc is the second peak.

[0047] In one embodiment, in the example of an accelerometer, ωb corresponds to the resonant frequency Fr of the inertial sensor 102. ωb is the frequency at which a positive peak exists at a frequency below the resonant frequency Fr in the baseband signal. ωc is the frequency at which a negative peak exists at a frequency greater than the resonant frequency Fr in the baseband signal. In the example of a gyroscope, ωb corresponds to the frequency Fmism associated with the gyroscope frequency mismatch.

[0048] As from Figure 3 As can be seen in FIG. 1 , the curve increases until the resonant frequency Fr due to the gain of the MEMS inertial sensor 102 at frequencies close to resonance. However, one pattern frequency corresponds exactly to the resonant frequency Fr, and the result averages to zero after the low-pass filter 120. After exceeding the resonant frequency Fr, the curve switches sign to negative due to the gain of the MEMS inertial sensor 102, and gradually disappears due to MEMS attenuation.

[0049] Back to Figure 1A , the calibration circuit 122 may identify the resonant frequency Fr of the accelerometer by identifying the zero crossing of the baseband signal. The calibration circuit 122 may identify the Fmism of the gyroscope by identifying the zero crossing of the baseband signal. The calibration circuit 122 may identify additional parameters as described below.

[0050] In one embodiment, the calibration circuit 122 calculates the Q factor of the accelerometer based on the characteristics of the baseband signal. In one example, the Q factor Q can be calculated by:

[0051] Q=(ωa*ωb) / (ωb 2 -ωa 2 )

[0052] In one embodiment, the characteristic Q-sense can be calculated by the calibration circuit 122 for the gyroscope based on the characteristics of the baseband signal. In one example, the parameter Q-sense can be calculated in the following manner:

[0053] Q-sense=(ωb+ωdrive) / (ωc-ωa)

[0054] Figure 4is a graph 400 illustrating the motion of a sensing mass of an inertial sensor 102 according to one embodiment, wherein the inertial sensor 102 is a gyroscope. The graph 400 illustrates the drive frequency Fdrive of the gyroscope, wherein ωdrive=Fdrive*2pi. Figure 4 Sensing frequencies Fsense and Fmism are also illustrated.

[0055] Regarding sensitivity, digital patterns operate at lower frequencies where sensitivity training is beneficial. The digital pattern can be a simple square wave with a fixed frequency or a CDMA-like pattern. This type of pattern is defined as null-mean and is uncorrelated with other CDMA patterns or external signals. In general, there is no guarantee that there is no correlation between the injected pattern and the external inertial signal. However, it has been seen in real cases that CDMA-like patterns can be more effective than fixed frequency square waves, as it is difficult to find an interfering signal with the same characteristics as the CDMA key.

[0056] Therefore, the analog path sensitivity drift can be estimated by a secondary measurement. The CDMA pattern can be injected into the trimmed time after the sensor module 100 is manufactured and the memory of the sensor module 100 is annotated with the resulting baseband value. This measurement can be repeated after the device is used. The ratio between the new baseband value and the baseband value stored in the memory can be calculated. This can be used to adjust the original sensitivity calibration done in the factory test.

[0057] The sensor module 100 includes a digital adjustment circuit system 124. The digital adjustment circuit system 124 can receive parameters Fr, Q, Fmism and S from the calibration circuit 122, and can adjust aspects of the digital sensor signal based on these parameters. The digital adjustment circuit system 124 may include a digital sensitivity adjustment gain circuit system. In one example, the digital adjustment circuit system 124 may include a notch filter. A notch filter is a band stop filter that passes most frequencies without change, but attenuates frequencies within a specific range to a very low level. Therefore, the notch filter can have a very narrow stop band, corresponding to a high Q factor. The digital notch filter is used to process sensor signals during standard operation of the inertial sensor 102. The stop band of the notch filter can be calibrated by the calibration circuit 122 based on one or more of Fr, Fmism, Q factor, Qsense and sensitivity S. The calibration circuit 122 can also calibrate other components of the sensor module 100 based on these parameters to facilitate efficient operation of the sensor module 100.

[0058] The calibration process described above may be performed throughout the life of the sensor module 100. Thus, the calibration process may help calibrate the sensor module 100 not only based on process variations, but also based on drifts in temperature, pressure, voltage, and other parameters throughout the life of the sensor module 100.

[0059] Figure 1B is a block diagram of a sensor module 100 according to one embodiment. Figure 1B The sensor module is similar to Figure 1A The sensor module 100 is substantially similar. However, in Figure 1B In the example of FIG. 1 , the inertial sensor 102 is a gyroscope. The second demodulator 130 is implemented in the analog portion 105 between the capacitance-to-voltage converter 100 and the ADC 112. In addition, the DAC 128 is implemented between the delay 108 and the inertial sensor 102. The DAC 128 can be used to convert a multi-bit test signal (such as a CDMA-like signal) from the pattern generator 108 into an analog signal for the inertial sensor 102. Although the DAC 128 is present in Figure 1B in but not in Figure 1A In practice, however, DAC 128 can appear in Figure 1A In the embodiment, and may not appear in Figure 1B In other words, DAC 128 may or may not be present in the accelerometer and gyroscope. Figure 1B In the example of a gyroscope, the calibration circuit 122 may provide a frequency Fmism instead of the resonant frequency Fr.

[0060] according to Figure 1A and Figure 1B The sensor module 100 can provide various benefits. For example, the sensor module 100 can provide real-time and immediate measurements and calibrations. The sensor module 100 can be immune to external inertial motion because the correlation between the injected / sensed signals is independent of the external signal. The sensor module 100 can be immune to amplitude differences in the analog chain, whether due to temperature or aging. If a digital square pattern is used, complex analog circuits such as DACs can be avoided. If a digital square pattern is used, the digital demodulation is a simple symbol version. This simplifies digital design. Final testing in the factory can be accelerated using simple and smaller routines. Zero crossing detection of baseband signals is much simpler than peak-based identification points. The calibration process can be performed outside the CPU. This is beneficial in terms of power, area, and programming.

[0061] Figure 5 is a simplified top view of an inertial sensor 102 according to one embodiment. The inertial sensor 102 is Figure 1A and Figure 1B An example of an inertial sensor 102 of FIG. 102 includes a self-test drive mass 142 and a sense mass 144. During calibration, a test signal is provided to the self-test drive electrode 104, causing the drive mass 142 to oscillate. During calibration, the self-test sense electrode 140 generates a signal about Figure 1A The capacitive sensor signal described above is used to sense the movement of the sensing mass 144. In fact, the inertial sensor 102 may include other shapes, structures, and components without departing from the scope of the present disclosure. In particular, the self-test electrode 104 / 140 may have a configuration similar to that of the sensing mass 144 without departing from the scope of the present disclosure. Figure 5 Different positions or configurations as shown in .

[0062] Figure 6 is a flow chart of a method 600 for calibrating an inertial sensor according to one embodiment. The method 600 may utilize Figures 1A to 5 The components, processes and systems described herein. In 602, method 600 includes generating an analog sensor signal by applying a self-test signal to a self-test electrode of the inertial sensor using an inertial sensor. In 604, method 600 includes converting the analog sensor signal into a digital sensor signal. In 606, method 600 includes generating a demodulated signal by passing the self-test signal and the digital sensor signal to the first demodulator using a first demodulator. In 608, method 600 includes generating a baseband signal by passing the first demodulated signal through a low-pass filter. In 610, method 600 includes identifying a zero-crossing frequency and a first peak frequency in the baseband signal using a calibration circuit. In 612, method 600 includes calibrating the inertial sensor based on the zero-crossing frequency and the first peak frequency.

[0063] Figure 7 is a flow chart of a method 700 for calibrating an inertial sensor according to one embodiment. The method 700 may utilize Figures 1A to 5 The method 700 includes applying a frequency swept self-test signal to a test electrode of an inertial sensor and a first demodulator of the inertial sensor. In 704, the method 700 includes generating a first digital signal based on the frequency swept self-test signal using the inertial sensor. In 706, the method 700 includes generating a demodulated signal by demodulating the digital sensor signal and the frequency swept test signal. In 708, the method 700 includes generating a baseband signal by passing the demodulated signal through a digital low pass filter. In 710, the method 700 includes identifying a resonant frequency of the inertial sensor by analyzing the baseband signal using a calibration circuit.

[0064] In one embodiment, a method includes using an inertial sensor, generating an analog sensor signal by applying a self-test signal to a self-test electrode of the inertial sensor, converting the analog sensor signal into a digital sensor signal, and using a first demodulator, generating a demodulated signal by passing the self-test signal and the digital sensor signal to the first demodulator. The method includes generating a baseband signal by passing the first demodulated signal through a low-pass filter, identifying a zero-crossing frequency and a first peak frequency in the baseband signal using a calibration circuit, and calibrating the inertial sensor based on the zero-crossing frequency and the first peak frequency.

[0065] In one embodiment, a method includes applying a frequency swept self-test signal to a test electrode of an inertial sensor and a first demodulator of the inertial sensor, and generating a first digital signal based on the frequency swept self-test signal using the inertial sensor. The method includes generating a demodulated signal by demodulating the digital sensor signal and the frequency swept self-test signal, generating a baseband signal by passing the demodulated signal through a digital low-pass filter, and identifying a resonant frequency of the inertial sensor by analyzing the baseband signal using a calibration circuit.

[0066] In one embodiment, a device includes: a pattern generator configured to generate a frequency swept self-test signal; and an inertial sensor including a self-test electrode configured to receive the frequency swept self-test signal. The inertial sensor is configured to generate an analog sensor signal based on the self-test signal. The device includes an analog-to-digital converter configured to generate a digital sensor signal based on the analog sensor signal. The device includes a demodulator including a first input configured to receive the digital sensor signal, a second input configured to receive the self-test signal, and an output configured to output a demodulated signal. The device includes a first low-pass filter coupled to the output of the demodulator and configured to generate a baseband signal. The device includes a calibration circuit configured to identify a resonant frequency of the inertial sensor by analyzing the baseband signal.

[0067] The various embodiments described above can be combined to provide additional embodiments. These and other changes can be made to the embodiments according to the above description. In general, in the following claims, the terms used should not be interpreted as limiting the claims to the specific embodiments disclosed in this specification and the claims, but should be interpreted as including all possible embodiments and the full range of equivalents enjoyed by such claims. Therefore, the claims are not limited to the present disclosure.

Claims

1. A method comprising: generating an analog sensor signal by applying a self-test signal to a self-test electrode of the inertial sensor using the inertial sensor; converting the analog sensor signal into a digital sensor signal; generating, using a first demodulator, a demodulated signal by passing the self-test signal and the digital sensor signal to the first demodulator; generating a baseband signal by passing the first demodulated signal through a low pass filter; Using a calibration circuit, identifying a zero-crossing frequency and a first peak frequency in the baseband signal; as well as The inertial sensor is calibrated based on the zero-crossing frequency and the first peak frequency. 2 . The method of claim 1 , wherein calibrating the inertial sensor comprises calibrating a digital notch filter based on the zero-crossing frequency and the first peak frequency. 3 . The method of claim 1 , comprising calculating a Q factor of the inertial sensor based on the first peak frequency and the zero-crossing frequency. The method of claim 1 , wherein the zero-crossing frequency is a resonant frequency of the inertial sensor.

5. The method of claim 1 , comprising identifying, using the calibration circuit, a second peak frequency in the baseband signal, wherein the first peak frequency is a positive peak frequency, the second peak frequency is a negative peak frequency, and the zero-crossing frequency is between the first peak frequency and the second peak frequency. 6 . The method of claim 5 , wherein the inertial sensor is a gyroscope, the method comprising calculating a Q factor of the inertial sensor based on the first peak frequency, the second peak frequency, and the zero-crossing frequency.

7. The method of claim 6, comprising calculating the Q factor based on a driving frequency of the inertial sensor.

8. The method of claim 1, comprising delivering the self-test signal to the self-test electrode via a first variable delay element.

9. The method of claim 8, comprising passing the self-test signal to the demodulator via a second variable delay element. 10 . The method of claim 9 , wherein calibrating the inertial sensor comprises adjusting at least one of the first variable delay element and the second variable delay element. The method of claim 1 , wherein the self-test signal is a frequency sweep signal.

12. The method of claim 1, wherein the self-test signal is a variable frequency self-test signal.

13. A method comprising: applying a frequency swept self-test signal to a test electrode of an inertial sensor and a first demodulator of the inertial sensor; generating, using the inertial sensor, a first digital signal based on the frequency swept self-test signal; generating a demodulated signal by demodulating the digital sensor signal and the frequency swept self-test signal; generating a baseband signal by passing the demodulated signal through a digital low pass filter; as well as The resonant frequency of the inertial sensor is identified by analyzing the baseband signal using a calibration circuit. The method of claim 13 , wherein identifying the resonant frequency comprises identifying a zero-crossing frequency of the baseband signal.

15. The method according to claim 14, comprising: Using the calibration circuit, identifying a first peak frequency of the baseband signal; as well as A Q factor of the inertial sensor is calculated based on the resonant frequency and the first peak frequency.

16. The method according to claim 14, comprising: identifying, using the calibration circuit, a positive frequency of the baseband signal; Using the calibration circuit, identifying negative frequencies of the baseband signal; as well as A Q factor of the inertial sensor is calculated based on the resonant frequency, the positive peak frequency, and the negative peak frequency.

17. A device comprising: a pattern generator configured to generate a variable frequency self-test signal; an inertial sensor comprising a self-test electrode configured to receive the frequency swept self-test signal, the inertial sensor configured to generate an analog sensor signal based on the self-test signal; an analog-to-digital converter configured to generate a digital sensor signal based on the analog sensor signal; A demodulator comprising: a first input configured to receive the digital sensor signal; a second input configured to receive the self-test signal; and an output configured to output a demodulated signal; a first low pass filter coupled to the output of the demodulator and configured to generate a baseband signal; and A calibration circuit is configured to identify a resonant frequency of the inertial sensor by analyzing the baseband signal.

18. The apparatus of claim 17, wherein the calibration circuit is configured to identify the resonant frequency as a zero-crossing frequency of the baseband signal.

19. The apparatus of claim 18, wherein the calibration circuit is configured to identify a first peak frequency of the baseband signal, and to calculate a Q factor of the inertial sensor based on the resonant frequency and the first peak frequency.

20. The apparatus of claim 18, wherein the calibration circuit is configured to identify a positive frequency of the baseband signal, identify a negative frequency of the baseband signal, and calculate a Q factor of the inertial sensor based on the resonant frequency, the positive peak frequency, and the negative peak frequency.

21. The apparatus of claim 17, wherein the frequency varying self-test signal comprises a pseudo-random sequence.

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