MEMS Inertial Sensor Self-Calibration Using Frequency Sweep Demodulation
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Solution Overview
Problem
Existing MEMS inertial sensors face challenges in meeting advanced performance specifications due to process variations and drift over time, which traditional calibration techniques often fail to address effectively.
Innovation Solution
A self-test electrode injects a known electromechanical displacement to the mechanical components of MEMS inertial sensors, allowing for efficient calibration by analyzing sensor signals to identify parameters such as resonant frequency, Q factor, and sensitivity, utilizing a digital self-test method that avoids complex analog circuits and can be performed in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration techniques are used for MEMS inertial sensors, then the calibration process can be implemented with basic equipment, but the calibration accuracy is insufficient to meet advanced performance specifications
Solution Approach 1:
The inertial sensor performs self-calibration by injecting test signals through self-test electrodes and processing the resulting sensor signals through demodulators and calibration circuits integrated within the sensor module, eliminating the need for external calibration equipment and achieving high calibration accuracy through self-contained measurement and processing capabilities
Solution Approach 2:
The calibration process utilizes mechanical vibration by injecting test signals that excite the proof mass to vibrate at specific frequencies, allowing the calibration circuit to measure vibration characteristics such as resonant frequency and Q-factor to determine calibration parameters with high precision
2Reliability
If comprehensive calibration procedures are performed to address process variations and drift, then sensor performance can be optimized, but the calibration time increases significantly
Solution Approach 1:
The calibration system performs periodic calibration by injecting test signals at specific intervals and using demodulators to extract calibration parameters, allowing the sensor to be calibrated repeatedly over time to compensate for drift while maintaining acceptable calibration time through efficient periodic measurement cycles
Solution Approach 2:
The calibration circuit identifies calibration parameters such as resonant frequency and Q-factor in advance through automated measurement of test signal responses, storing these parameters for use in compensation algorithms that maintain sensor performance without requiring frequent lengthy recalibration procedures
3Ease of operation
If analog calibration circuits are used to extract calibration parameters, then the calibration process can be performed, but the circuit complexity and susceptibility to temperature and aging effects increase
Solution Approach 1:
The calibration system replaces complex analog calibration circuits with digital signal processing by using demodulators to convert sensor signals to digital form and applying digital filtering and correlation algorithms to extract calibration parameters, reducing analog circuit complexity and improving stability against temperature and aging effects
Solution Approach 2:
The demodulator serves as an intermediary between the analog sensor output and the digital calibration processing, converting the analog sensor signal and test signal into a demodulated digital signal that can be processed by digital filters and calibration circuits, simplifying the overall system architecture
4Measurement precision
If frequency sweep self-test signals are applied to determine resonant frequency, then accurate calibration parameters can be obtained, but the measurement process becomes more complex
Solution Approach 1:
The calibration circuit uses feedback by comparing the demodulated signal with the injected test signal through correlation processing, automatically identifying the resonant frequency as the frequency that maximizes the correlation output, and using this information to adjust calibration parameters without requiring complex manual measurement procedures
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The calibration process is accurate, time-efficient, and immune to external movements and amplitude differences, simplifying digital design and enabling real-time calibration throughout the lifetime of the sensor module.
Implementation Method 1
utilize a dedicated self-test electrode to inject a known electromechanical displacement to the mechanical components of the MEMS inertial sensor
Implementation Method 2
The digital portion of the sensor module can quickly extract parameters from the test signal... generating a baseband signal by passing the first demodulated signal through a low pass filter
Implementation Method 3
identifying a resonant frequency of the MEMS inertial sensor by analyzing the baseband signal
Data Source
AI summary
A 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 configured to receive the frequency sweep self-test signal. The inertial sensor is configured to generate an analog sensor signal based on the self-test signal. The sensor module includes an analog to digital converter configured to generate a digital sensor signal based on the analog sensor signal and 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 sensor module includes a first low pass filter coupled to the output of the demodulator and configured to generate a baseband signal. The sensor module includes a calibration circuit configured to identify different MEMS characteristics, like resonance frequency, Q-factor, or sensitivity based on the baseband signal.