Vector High-Resolution Marine Turbulence Sensor Based on a MEMS Bionic Cilium-Shaped Structure

Zhang, Wenjun; Hao, Congcong; Zhang, Zengxing; Yang, Shasha; Peng, Jiali; Wu, Bin; Xue, Xiaobin; Zang, Junbin; Chen, Xu; Yang, Hua; Song, Dalei; Chen, Xue; Zhang, Zhidong; Wang, Renxin

doi:10.1109/jsen.2020.3046836

Cited by 11 publications

(7 citation statements)

References 57 publications

(41 reference statements)

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“…[20,21] Equation (1) is the relationship between strain and resistance change described by the semiconductor piezoresistive effect. Equation ( 2) is the equivalent output voltage expression of the Wheatstone bridge measuring micromechanical deformation [22] (1 2 ) ,…”

Section: Sensor Structure and Principlementioning

confidence: 99%

“…[ 20,21 ] Equation (1) is the relationship between strain and resistance change described by the semiconductor piezoresistive effect. Equation (2) is the equivalent output voltage expression of the Wheatstone bridge measuring micromechanical deformation [ 22 ]

\begin{matrix} left \\ \frac{Δ R}{R} = (1 + 2 μ) ε + \frac{Δ ρ}{ρ}, \frac{Δ ρ}{ρ} = π_{1} E ε \\ \frac{Δ R}{R} \approx π_{1} E ε = π_{1} σ \end{matrix}

\begin{matrix} V_{normalo} = \frac{(R_{1} + Δ R_{1}) (R_{3} + Δ R_{3}) - (R_{2} - Δ R_{2}) (R_{4} - Δ R_{4})}{(R_{1} + Δ R_{1} + R_{2} - Δ R_{2}) (R_{3} + Δ R_{3} + R_{4} - Δ R_{4})} V_{in} \end{matrix}

where π 1 is the piezoresistive coefficient of the Si material, ρ is the resistivity, E is the elastic modulus, and σ is the stress on the material. V in is the driving voltage of the bridge circuit, V o is the output voltage of the signal to be measured, R 1 – R 4 are the corresponding varistor values on the cantilever beam, and △ R is the change in resistance value.…”

Section: Sensor Structure and Principlementioning

confidence: 99%

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Optimum Design and Test of a Novel Bionic Electronic Stethoscope based on the Cruciform Microcantilever with Leaf Microelectromechanical Systems Structure

Zang

Zhou

Xiang

et al. 2022

Adv Materials Technologies

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This paper proposes a heart sound sensing structure and system by combining the traditional auscultation structure and human‐like ear eardrum gas–solid coupling sound conduction process. This system is based on the lever pickup mechanism of eardrum vibration and the traditional stethoscope pickup amplification, microelectromechanical system (MEMS) technology, and the detection principle of piezoresistive and Wheatstone bridge. The optimal size and process parameters of the biomimetic sensor structure are given through theoretical calculation and numerical simulation on Comsol and SRIM. Computer numerical control, 3D printing, MEMS technology, and 3D heterogeneous integration technology are used to complete the precise processing and encapsulation of the proposed structure. After the performance tests, results show that the optimal structure can collect directional signals, with a bandwidth from 10 Hz to 1 kHz, which can effectively cover the range of the heart frequency. The signal‐to‐noise ratio is improved by 2.3 dB compared with the 3M stethoscope. An electrocardiogram and phonocardiogram synchronization detection system is developed with this structure. The structure and system can be effectively applied to the high‐quality collection and intelligent recognition of heart sound data sets. The recognition accuracy can reach 98.5%. It is highly effective for early screening and intelligent diagnosis of cardiovascular diseases.

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Section: Sensor Structure and Principlementioning

confidence: 99%

“…[ 20,21 ] Equation (1) is the relationship between strain and resistance change described by the semiconductor piezoresistive effect. Equation (2) is the equivalent output voltage expression of the Wheatstone bridge measuring micromechanical deformation [ 22 ]

\begin{matrix} left \\ \frac{Δ R}{R} = (1 + 2 μ) ε + \frac{Δ ρ}{ρ}, \frac{Δ ρ}{ρ} = π_{1} E ε \\ \frac{Δ R}{R} \approx π_{1} E ε = π_{1} σ \end{matrix}

\begin{matrix} V_{normalo} = \frac{(R_{1} + Δ R_{1}) (R_{3} + Δ R_{3}) - (R_{2} - Δ R_{2}) (R_{4} - Δ R_{4})}{(R_{1} + Δ R_{1} + R_{2} - Δ R_{2}) (R_{3} + Δ R_{3} + R_{4} - Δ R_{4})} V_{in} \end{matrix}

Section: Sensor Structure and Principlementioning

confidence: 99%

Optimum Design and Test of a Novel Bionic Electronic Stethoscope based on the Cruciform Microcantilever with Leaf Microelectromechanical Systems Structure

Zang

Zhou

Xiang

et al. 2022

Adv Materials Technologies

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“…In 2021, Wang, et al made modifications to the bionic cilia vector hydrophone by packaging it with MEMS turbulence detectors for the purpose of ocean turbulence detection [18][19][20][21][22]. Through indoor water tank experiments and comparison with commercial PNS shear flow detectors, they verified the feasibility of the MEMS turbulence sensor in detecting two-dimensional turbulence signals.…”

Section: Introductionmentioning

confidence: 99%

A Bio-Inspired MEMS Wake Detector for AUV Tracking and Coordinated Formation

et al. 2023

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AUV (Autonomous Underwater Vehicle) coordinated formation can expand the detection range, improve detection efficiency, and complete complex tasks, which requires each AUV to have the ability to track and locate. A wake detector provides a new technical approach for AUV cooperative formation warfare. Now, most of the existing artificial lateral line detectors are for one-dimensional flow field applications, which are difficult to use for wake detection of AUVs. Therefore, based on the pressure gradient sensing mechanism of the canal neuromasts, we apply Micro-Electro-Mechanical System (MEMS) technology to develop a lateral line-inspired MEMS wake detector. The sensing mechanism, design, and fabrication are demonstrated in detail. Experimental results show the detector’s sensitivity is 147 mV·(m/s)−1, and the detection threshold is 0.3 m/s. In addition, the vector test results verify it has vector-detecting capacity. This wake detector can serve AUVs wake detection and tracking technology, which will be promising in AUV positioning and coordinated formation.

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“…Xue et al proposed a novel MEMS bionic vector hydrophone based on the principle that fish's lateral line organs sense water flow pressure and flow velocity. It can be well applied to underwater detection and has a good frequency response in low frequency and sensitivity [12,13]. Afterwards, Zhang et al optimised and improved this model [14,15], and Liu et al proposed a lollipop-shaped hydrophone that improves the sensitivity [16].…”

Section: Introductionmentioning

confidence: 99%

Design of a Novel Medical Acoustic Sensor Based on MEMS Bionic Fish Ear Structure

et al. 2022

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High-performance medical acoustic sensors are essential in medical equipment and diagnosis. Commercially available medical acoustic sensors are capacitive and piezoelectric types. When they are used to detect heart sound signals, there is attenuation and distortion due to the sound transmission between different media. This paper proposes a new bionic acoustic sensor based on the fish ear structure. Through theoretical analysis and finite element simulation, the optimal parameters of the sensitive structure are determined. The sensor is fabricated using microelectromechanical systems (MEMS) technology, and is encapsulated in castor oil, which has an acoustic impedance close to the human body. An electroacoustic test platform is built to test the performance of the sensor. The results showed that the MEMS bionic sensor operated with a bandwidth of 20 Hz–2k Hz. Its linearity and frequency responses were better than the electret microphone. In addition, the sensor was tested for heart sound collection application to verify its effectiveness. The proposed sensor can be effectively used in clinical auscultation and has a high SNR.

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Vector High-Resolution Marine Turbulence Sensor Based on a MEMS Bionic Cilium-Shaped Structure

Cited by 11 publications

References 57 publications

Optimum Design and Test of a Novel Bionic Electronic Stethoscope based on the Cruciform Microcantilever with Leaf Microelectromechanical Systems Structure

Optimum Design and Test of a Novel Bionic Electronic Stethoscope based on the Cruciform Microcantilever with Leaf Microelectromechanical Systems Structure

A Bio-Inspired MEMS Wake Detector for AUV Tracking and Coordinated Formation

Design of a Novel Medical Acoustic Sensor Based on MEMS Bionic Fish Ear Structure

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