调整静电耦合微机械谐振器的非线性动力学以提高模域传感器的灵敏度

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Ming Lyu;Xiang Zhi;Bo Yang;Jian Zhao;Najib Kacem
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引用次数: 0

摘要

我们通过实验证明了一种非线性调谐机制,可以提高模式局部化微机械传感器的灵敏度。首先,利用有限元模拟方法对器件的静态拉入、特征频率和线性动力学进行了数值计算,确定了器件的工作动态范围。然后,在真空环境下建立开环实验平台对器件进行测试,实验结果表明,耦合电压对模局域谐振器的非线性行为有显著影响。值得注意的是,耦合电压改变了该器件的非线性动力学特性,使频率响应在非相模式下从硬化行为转变为软化行为。因此,这种装置可用于检测电场强度,与两种模式下的完全硬化振动相比,其灵敏度可提高到116%,即每个偏置电压的振幅比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tuning the Nonlinear Dynamics of Electrostatically Coupled Micromechanical Resonators to Enhance the Sensitivity of Mode-Localized Sensors
We demonstrate experimentally a nonlinear tuning mechanism enabling the sensitivity enhancement of mode-localized micromechanical sensors. First, the static pull in, eigenfrequency, and linear dynamics of the fabricated device are numerically calculated using finite element simulations to identify the operating dynamic range. Then, an open-loop experimental platform is established to test the fabricated device in a vacuum environment, and the experimental results show that the coupling voltage significantly affects the nonlinear behavior of the mode-localized resonators. Remarkably, the coupling voltage modifies the nonlinear dynamics of the proposed device with a transition in the frequency response from hardening to softening behavior for the out-of-phase mode. As a result, such a device can be used to detect the electric field strength, and the sensitivity depicted as the amplitude ratio per bias voltage can be enhanced up to 116% compared to fully hardening vibrations for both modes.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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