High-Temperature Self-Compensating Absolute Micro-Pressure Sensor and Its Testing Method

Sun, Boshan; Xiong, Jijun; Fang, Zhihong; Gao, Lixia; Xu, Aodi; Jia, Pengyu; Feng, Qiyun; Li, Chen

doi:10.1109/jsen.2022.3223981

Cited by 1 publication

(2 citation statements)

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“…Many efforts have been made to address this interference, including structural decoupling, [21,22] calibration, [23][24][25] and multimode sensing. [26][27][28][29][30] Although these decoupling methods can solve environmental problems, there are still inevitable limitations in practical applications.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the structural decoupling method is accompanied by a complicated fabrication process and complex structure, which makes it difficult to utilize the sensor. [21,22] However, calibration, which is the most commonly used method, has chronic limitations, such as the requirement for additional sensors, complexity in designing circuit boards, and complex relationship formulas. [23][24][25] Multimode sensing methods have emerged in recent years owing to their simultaneous pressure and temperature detection ability based on separated sensing mechanisms; however, they still have problems with limited decoupling capability, complex signal collecting systems, and the impossibility of steady temperature detection without a thermal gradient.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network Regression‐Assisted Pressure Sensor for Decoupling Thermal Variations at Different Operating Temperatures

Bang,

Baek,

Lim

et al. 2023

Advanced Intelligent Systems

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Decoupling environment‐dependent response in sensing techniques is essential for the diverse practical applications. This work presents a novel thermal effect decoupling method for sponge pressure sensors based on a deep neural network (DNN) regression model, which is difficult to achieve owing to the material‐ and structure‐related complex effects of the sponge‐based pressure sensor. A poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate)‐based multifunctional device is fabricated with a both pressure and thermally responsive part and an only thermally responsive part; and a DNN model with two input features is adapted to implement the substantial pressure prediction system without thermal interference. Proposed model shows the robust decoupled pressure‐sensing capability with high accuracy of ≈96.23% using two input features. It also enables accurate pressure prediction under both the thermally steady and transition regions, which indicates significant potential for a precise measurement system. These results demonstrate the possibility of reliable pressure monitoring under varying thermal conditions, which is important for accurately measuring pressure in complex power plants, human–machine interfaces, and compact wearable platforms.

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