Measuring and calibrating of the parasitic mismatch in MEMS accelerometer based on harmonic distortion self-test

Chen, Dongliang; Yin, Liang; Fu, Qiang; Zhang, Yufeng; Liu, Xiaowei

doi:10.1016/j.sna.2020.112159

Cited by 17 publications

(10 citation statements)

References 19 publications

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“…However, they ignored the parasitic capacitance between the electrodes in the readout circuit. Chen D. et al proposed a method to identify and calibrate parasitic mismatches based on a digital, closed-loop EM-Δ (electromechanical sigma delta) accelerometer system [ 17 ]. Consequently, their method is only applicable to closed-loop configurations.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Degradation Evaluation of the Mismatch of Sensitive Capacitance in MEMS Accelerometers

Huang

Dong

et al. 2023

Micromachines

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During long-term use, MEMS accelerometers will experience degradation, such as bias and scale factor changes. Bias of MEMS capacitive accelerometers usually comes from the mismatch of parasitic capacitance and sensitive capacitance. This paper focuses on the mismatch of sensitive capacitance and analyzes the mechanism of long-term degradation of MEMS accelerometers. Firstly, the effect of sensitive capacitance mismatch on the performance of a MEMS accelerometer was investigated. Secondly, a method of measuring the mismatch of sensitive capacitance was proposed, and the validation experiment shows that the accuracy of this measurement can be less than 1.10×10−5 of the sensitive capacitance. For the samples in this experiment, the measurement error of this method can be less than 0.36 fF. Finally, a high-temperature acceleration experiment was performed. The mismatch of the sensitive capacitance during the experiment was monitored based on the proposed method, and the experimental results are analyzed. The experimental result demonstrates that the mismatch of sensitive capacitance varies linearly with time. The change rates of sensitive capacitance mismatch for the two samples are 2.95×10−7 C0/h and 2.66×10−7 C0/h in the high-temperature acceleration experiment at 145 °C, respectively. The change in sensitive capacitance mismatch seems small, but it is not to be ignored during long-term use. The rate of change is similar for the same batch of samples. This could imply that the adverse effects due to the mismatch of sensitive capacitance changes can be reduced by compensating for this variation.

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Section: Introductionmentioning

confidence: 99%

Long-Term Degradation Evaluation of the Mismatch of Sensitive Capacitance in MEMS Accelerometers

Huang

Dong

et al. 2023

Micromachines

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“…According to the detection principle, micro-accelerometers can be divided into piezoresistive [ 22 ], piezoelectric [ 23 ], resonant [ 24 ], capacitive [ 25 ], and other forms. Among them, capacitive accelerometers have the advantages of high accuracy [ 26 ], low-noise [ 27 ], low temperature sensitivity [ 28 ], low power consumption [ 29 ], and a simple structure [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of a SiC Microstructure-Based Capacitive Micro-Accelerometer

et al. 2021

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In this study, a comb-type capacitive accelerometer based on a silicon carbide (SiC) microstructure is presented and investigated by the finite element method (FEM). It has the advantages of low weight, small volume, and low cross-coupling. Compared with silicon(111) accelerometers with the same structure, it has a higher natural frequency. When the accelerometer vibrates, its resistive force consists of two main components: a viscous damping and an elastic damping force. It was found that viscous damping dominates at low frequency, and elastic damping dominates at high frequency. The second-order linear system of the accelerometer was analyzed in the time-frequency domain, and its dynamic characteristics were best when the gap between the capacitive plates was 1.23 μm. The range of this accelerometer was 0–100 g, which is 1.64 times that of a silicon(111) accelerometer with the same structure. In addition, the accelerometer could work normally at temperatures of up to 1200 °C, which is much higher than the working temperatures of silicon devices. Therefore, the proposed accelerometer showed superior performance compared to conventional silicon-based sensors for inertial measurements.

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“…It's well known that, there is an increasing demand for improving the accuracy of the calibration system (Chen et al, 2020a(Chen et al, , 2020bSoriano et al, 2020). The use of the appropriate calibration algorithm has been proved to be effective for improving the calibration accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Xue et al (2020) show that the combined PSO-sequential quadratic programming algorithm could reach the highest accuracy for the parameter calibration of the storm water management model and could obtain the most optimal parameter. The genetic algorithm (GA)-deep neural network non-linear regression prediction model has been proposed to achieve a robot's high-precision positioning performance under any external payload (Chen et al, 2020a(Chen et al, , 2020b. Feng et al (2020aFeng et al ( , 2020b proposed a novel optimisation framework of grid search moving window-linear predictive coding-GA for near-infrared (NIR) calibration, and experiments have shown that this algorithm provides an effective method for improving the performance of the NIR calibration models.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

Kong

2021

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Purpose Three-axis accelerometers play a vital role in monitoring the vibrations in aircraft machinery, especially in variable flight temperature environments. The sensitivity of a three-axis accelerometer under different temperature conditions needs to be calibrated before the flight test. Hence, the authors investigated the efficiency and sensitivity calibration of three-axis accelerometers under different conditions. This paper aims to propose the novel calibration algorithm for the three-axis accelerometers or the similar accelerometers. Design/methodology/approach The authors propose a hybrid genetic algorithm–particle swarm optimisation–back-propagation neural network (GA–PSO–BPNN) algorithm. This method has high global search ability, fast convergence speed and strong non-linear fitting capability; it follows the rules of natural selection and survival of the fittest. The authors describe the experimental setup for the calibration of the three-axis accelerometer using a three-comprehensive electrodynamic vibration test box, which provides different temperatures. Furthermore, to evaluate the performance of the hybrid GA–PSO–BPNN algorithm for sensitivity calibration, the authors performed a detailed comparative experimental analysis of the BPNN, GA–BPNN, PSO–BPNN and GA–PSO–BPNN algorithms under different temperatures (−55, 0 , 25 and 70 °C). Findings It has been showed that the prediction error of three-axis accelerometer under the hybrid GA–PSO–BPNN algorithm is the least (approximately ±0.1), which proved that the proposed GA–PSO–BPNN algorithm performed well on the sensitivity calibration of the three-axis accelerometer under different temperatures conditions. Originality/value The designed GA–PSO–BPNN algorithm with high global search ability, fast convergence speed and strong non-linear fitting capability has been proposed to decrease the sensitivity calibration error of three-axis accelerometer, and the hybrid algorithm could reach the global optimal solution rapidly and accurately.

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Measuring and calibrating of the parasitic mismatch in MEMS accelerometer based on harmonic distortion self-test

Cited by 17 publications

References 19 publications

Long-Term Degradation Evaluation of the Mismatch of Sensitive Capacitance in MEMS Accelerometers

Long-Term Degradation Evaluation of the Mismatch of Sensitive Capacitance in MEMS Accelerometers

Modeling and Analysis of a SiC Microstructure-Based Capacitive Micro-Accelerometer

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

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