Characterization of a high-sensitivity micromachined tunneling accelerometer with micro-g resolution

Liu, Cheng-Hsien; Barzilai, A.M.; Reynolds, J.K.; Partridge, A.; Kenny, Thomas W.; Grade, J.D.; Rockstad, Howard K.

doi:10.1109/84.679388

Cited by 73 publications

(10 citation statements)

References 21 publications

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“…In Han and Cho (2003), the performance of an accelerometer and its noise characteristics were recorded with varying voltages and pressures. In Liu et al (1998), a shake table was used to create dynamic accelerations to measure noise and other characteristics of a tunneling accelerometer. In addition to the noise research mentioned above, there are many works that used noise theories to optimize accelerometer designs (Boser and Howe 1996, Yazdi et al 2003, Yeh and Najafi 1997a, Kajita et al 2002, Monajemi and Ayazi 2006, Amini and Ayazi 2005.…”

Section: Accelerometersmentioning

confidence: 99%

Noise in MEMS

Mohd-Yasin

Nagel

Korman

2009

Meas. Sci. Technol.

129

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This review provides a comprehensive recap of noise research in MEMS. Some background on noise and on MEMS is provided. We review noise production mechanisms, and highlight work on the theory and modeling of noise in MEMS. Then noise measurements in the specific types of MEMS are reviewed. Inertial MEMS (accelerometers and angular rate sensors), pressure and acoustic sensors, optical MEMS, RF MEMS, surface acoustic wave devices, flow sensors, and chemical and biological MEMS, as well as data storage devices and magnetic MEMS, are reviewed. We indicate opportunities for additional experimental and computational research on noise in MEMS.

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Section: Accelerometersmentioning

confidence: 99%

Noise in MEMS

Mohd-Yasin

Nagel

Korman

2009

Meas. Sci. Technol.

129

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“…Figure 7 shows the plot of feedback voltage dependent on input acceleration. The linearity is kept until the input is out of the dynamic range, which is about 1.2 mg and it is well matched with the measured value in [12]. The small dynamic range is the price of high sensitivity because of the small K/m value.…”

Section: Tunneling Accelerometer Function Simulationmentioning

confidence: 59%

“…Usually, at this constant distance operation point, the distance between the tip and proof mass electrode is about 10 Å and the tunneling current is about 1.5 nA. More detailed descriptions about the tunneling structures and operation principles can be found in [9][10][11][12]…”

Section: Accelerometer Structurementioning

confidence: 99%

Synthesis of the modeling and control systems of a tunneling accelerometer using the MatLab simulation

Wang

Zhao

Cui

et al. 2002

J. Micromech. Microeng.

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In this paper, we construct the function model of a tunneling accelerometer using MatLab Simulink. With small input signal approximation, the tunneling current and electrostatic actuator nonlinear blocks are analyzed and linearized. From the derived relation between open and close loop transfer functions, we synthesize a simple but effective way to design the control system. By choosing a control system, the accelerometer bandwidth is broadened and the damping is enhanced. The system stability, root locus, step response, gain and phase margins, and pole-zero distribution are all plotted and discussed. With the designed control and feedback systems, a close loop system is stable on both parameter disturbance and frequency response. Compared with the real system at 50 ng Hz−1/2 noise level, we also present tunneling accelerometer characteristics, such as the exponential relationship between tunneling current and displacement change, time history record, dynamics and frequency response.

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“…Thermal microaccelerometers do not need a solid proof mass and are based on thermal convection, although, they have offset variations for long term operation conditions caused by changes in the environment temperature (Kaltsas et al 2006). Tunneling microaccelerometers present a high sensitivity, but they require a complex fabrication process and have stability problems (Liu et al 1998). Novel designs include optical microaccelerometers that have immunity to electromagnetic interference (EMI) and reduce electronic circuitry and total weight; though, they present intrinsic losses due to structural imperfections and require difficult fabrication processes (Llobera et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Performance optimization and mechanical modeling of uniaxial piezoresistive microaccelerometers

et al. 2009

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The acceleration measurements in automotive, navigation, biomedical and consumer applications demand high-performance microaccelerometers. This paper presents an optimization model to maximize the bandwidth of uniaxial piezoresistive microaccelerometers based on cantilever-type beams. The proposed model provides a high sensitivity as well as normal stress levels lower than the material rupture stress of these microaccelerometers. This model uses the Rayleigh method to determine the objective function of the bandwidth and the maximum-normal-stress failure theory to obtain a stress constraint that guarantees safe operation for the microaccelerometer structure. The Box-Complex optimization method is used to solve the optimization model due to its easy programming algorithm. Finite element models (FE) are developed to determine the mechanical behavior of the optimized piezoresistive microaccelerometers. The results of the FE models agree well with those of the optimization model. The optimization model can be easily used by designers to find the optimum geometrical dimensions of piezoresistive microaccelerometers to maximize their performance.

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Characterization of a high-sensitivity micromachined tunneling accelerometer with micro-g resolution

Cited by 73 publications

References 21 publications

Noise in MEMS

Noise in MEMS

Synthesis of the modeling and control systems of a tunneling accelerometer using the MatLab simulation

Performance optimization and mechanical modeling of uniaxial piezoresistive microaccelerometers

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