基于NTC/云母薄膜的高灵敏度MEMS量热流量传感器的研制

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Bo Yang;Xinmiao Wang;Yuxian Song;Yingying Dou;Aimin Chang;Wenwen Kong
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引用次数: 0

摘要

量热式流量传感器由于其优越的灵敏度是低流量检测的首选。然而,传统的设计存在一定的局限性,即测量精度低,制造复杂。本研究通过将负温度系数(NTC)薄膜与超薄云母衬底集成,提出了一种创新的传感器结构。高灵敏度NTC薄膜(B =3720 K, TCR =4.3)%) achieved precise temperature differential detection via enhanced on-chip monitoring. It is further supported by the thin mica substrate ( $40~\mu $ m thickness) exhibiting exceptionally low thermal mass (heat capacity $\cdot $ K and thermal conductivity $\cdot $ K). This optimized design and process significantly improved the resolution and sensitivity of the sensor. Based on the segmented fitting results of the velocity– $\Delta T$ relationship, it was observed that the optimized calorimetric flow sensor achieved a high sensitivity of up to 1.93 m/s over a flow range of 0.01–0.1 m/s ( ${R}^{{2}} =0.998$ ), with a resolution accuracy of 0.01 m/s. The response time of the flow sensor was measured to be 0.49 s. The repeatability and stability were also verified with a standard deviation that was lower than 2%. The flow sensor herein exhibited superior performance with a simple fabrication process relative to conventional silicon-based heat flow sensors. Furthermore, it also exhibited good application potential in respiratory monitoring, indicating strong suitability for wearable health devices and medical monitoring systems that require high-precision flow measurements.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a High-Sensitive MEMS Calorimetric Flow Sensor Based on NTC/Mica Films
Calorimetric flow sensors are preferred for low-flow detection owing to their superior sensitivity. However, the conventional designs exhibit certain limitations, i.e., low measurement accuracy and fabrication complexity. This study introduces an innovative sensor architecture by integrating negative temperature coefficient (NTC) films with ultrathin mica substrates. The high-sensitivity NTC film (B =3720 K and TCR =4.3%) achieved precise temperature differential detection via enhanced on-chip monitoring. It is further supported by the thin mica substrate ( $40~\mu $ m thickness) exhibiting exceptionally low thermal mass (heat capacity <0.85> $\cdot $ K and thermal conductivity <0.55> $\cdot $ K). This optimized design and process significantly improved the resolution and sensitivity of the sensor. Based on the segmented fitting results of the velocity– $\Delta T$ relationship, it was observed that the optimized calorimetric flow sensor achieved a high sensitivity of up to 1.93 m/s over a flow range of 0.01–0.1 m/s ( ${R}^{{2}} =0.998$ ), with a resolution accuracy of 0.01 m/s. The response time of the flow sensor was measured to be 0.49 s. The repeatability and stability were also verified with a standard deviation that was lower than 2%. The flow sensor herein exhibited superior performance with a simple fabrication process relative to conventional silicon-based heat flow sensors. Furthermore, it also exhibited good application potential in respiratory monitoring, indicating strong suitability for wearable health devices and medical monitoring systems that require high-precision flow measurements.
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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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