All-SiC Fiber-Optic Sensor Based on Direct Wafer Bonding for High Temperature Pressure Sensing

Liang, Ting; Li, Wangwang; Cheng, Liang; Li, Yongwei; Li, Zhiqiang; Xiong, Jijun

doi:10.1007/s13320-021-0640-7

Cited by 21 publications

(13 citation statements)

References 27 publications

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“…For example, ber-optic cables placed close to highways and railways can give the wrong signal to a passing vehicle and not see the real problem. In addition, the cost of false alarms is high due to the installation of ber-optic early warning in the remote area [2]. erefore, it is very important to determine the signal vibration of a ber optic cable.…”

Section: Introductionmentioning

confidence: 99%

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Wang

2022

Journal of Control Science and Engineering

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In order to improve the recognition accuracy of SCN for optical fiber data, a method of optical fiber intrusion signal recognition based on SCN (TSVD-SCN) based on truncated singular value decomposition (TSVD) is proposed in this paper. TSVD-SCN performs SVD decomposition on the hidden layer output of the network and sets a threshold to remove the smaller singular values, so as to reduce the number of conditions of the hidden layer output matrix and improve the network recognition rate. This paper uses the method of duty cycle, average amplitude difference function, and FFT to calculate the energy duty cycle for feature extraction and uses TSVD-SCN algorithm to classify and recognize different intrusion vibration feature vectors. The experimental results show that the root mean square errors of TSVD-SCN and SCN networks are significantly less than RVFL. After the hidden layer node L = 20 , the training error decline speed of RVFL tends to be gentle. When LRVFL = L max , the learning effect is the best, and RMSERVFL = 0.3 . With the continuous increase of L, the training error of SCN network and TSVD-SCN network will be reduced to very small, and the training error of TSVD-SCN network is also less than SCN. Conclusion. The accuracy of the algorithm model proposed in this paper is higher than that of the SCN model. It can accurately identify the types of optical fiber intrusion signals, which is of great significance to improve the classification accuracy of the SCN network in practical applications.

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Section: Introductionmentioning

confidence: 99%

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Wang

2022

Journal of Control Science and Engineering

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“…The advantage of this type is that it is less affected by temperature; however, it suffers from the drawback that the sensor chip is easily disturbed by parasitic capacitance signals. The optical fiber type mainly uses the SiC substrate as structural support [22][23][24] and the optical fiber as the pressure sensor. This method can realize pressure measurements at higher temperatures, but the modulation and demodulation of the optical signal is complicated.…”

Section: Microelectromechanicalmentioning

confidence: 99%

Exploring the nonlinear piezoresistive effect of 4H-SiC and developing MEMS pressure sensors for extreme environments

Fang

Qiang

et al. 2023

Microsyst Nanoeng

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Microelectromechanical system (MEMS) pressure sensors based on silicon are widely used and offer the benefits of miniaturization and high precision. However, they cannot easily withstand high temperatures exceeding 150 °C because of intrinsic material limits. Herein, we proposed and executed a systematic and full-process study of SiC-based MEMS pressure sensors that operate stably from −50 to 300 °C. First, to explore the nonlinear piezoresistive effect, the temperature coefficient of resistance (TCR) values of 4H-SiC piezoresistors were obtained from −50 to 500 °C. A conductivity variation model based on scattering theory was established to reveal the nonlinear variation mechanism. Then, a piezoresistive pressure sensor based on 4H-SiC was designed and fabricated. The sensor shows good output sensitivity (3.38 mV/V/MPa), accuracy (0.56% FS) and low temperature coefficient of sensitivity (TCS) (−0.067% FS/°C) in the range of −50 to 300 °C. In addition, the survivability of the sensor chip in extreme environments was demonstrated by its anti-corrosion capability in H2SO4 and NaOH solutions and its radiation tolerance under 5 W X-rays. Accordingly, the sensor developed in this work has high potential to measure pressure in high-temperature and extreme environments such as are faced in geothermal energy extraction, deep well drilling, aeroengines and gas turbines.

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“…Silicon-based piezoresistive pressure sensors are easy to integrate with electronic devices, the response is more linear and is not affected by RF noise in nature, mainly using polysilicon as an elastomer, using advanced miniaturization manufacturing process to integrate silicon diaphragm as a sensitive component, based on the piezoresistive effect of polysilicon or silicon, the piezoresistive material is deformed under stress, and the resistivity changes due to the change of carrier mobility, which affects its resistance value [1][2][3][4][5] . In this paper, the sensitivity of the piezoresistive pressure sensor is calculated by using the COMSOL finite element software to calculate the output voltage of different thickness films at different pressures, and its high sensitivity and linearity are verified [6][7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

The finite element method was used to analyze the sensitivity of piezoresistive pressure sensors in micro-electro-mechanical system

Li,

Liu

2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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With the development of technology, the pressure sensor in MEMS system is required to be small in size and high in accuracy, but the experiment is difficult and the error is large. In this paper, the finite element method is used to analyze a silicon-based pressure sensor model composed of a square film with an edge length of 1 mm, a thickness of 20 m, a p-type doping density of 1.321019 cm-3 and a thickness of 400 nm, and the output voltage sensitivity of the silicon-based pressure sensor model is 0.6mV/kPa at 0-100 Kpa, which meets the requirements of high sensitivity and linearity of MEMS pressure sensor.

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All-SiC Fiber-Optic Sensor Based on Direct Wafer Bonding for High Temperature Pressure Sensing

Cited by 21 publications

References 27 publications

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Exploring the nonlinear piezoresistive effect of 4H-SiC and developing MEMS pressure sensors for extreme environments

The finite element method was used to analyze the sensitivity of piezoresistive pressure sensors in micro-electro-mechanical system

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