High sensitivity, thermal noise-driven aluminum-based resonant MEMS humidity sensor

Abstract

The integration of connected devices and the Internet of Things in the era of Industry 4.0 has led to the transformation of various sectors. Sensors, particularly for humidity measurement, play a pivotal role in applications such as respiration and wound monitoring, human–machine interfaces, fuel cells, and circuit failure detection. While conventional humidity sensors dominate the market, gravimetric-based sensing offers promise but faces challenges due to complex fabrication, high cost, and nonlinearity. This study explores gravimetric platforms for humidity sensing and proposes linear and nonlinear schemes to detect humidity. The linear sensors exhibit consistent frequency shifts with humidity changes, while the nonlinear sensors introduce a turn-around point where nonlinear effects counteract mass-loading. We address this limitation by splitting the dynamic range into low and high ranges on either side of this point, thereby creating one-to-one relationships. Both sensor types demonstrate high sensitivity (from 39068 to −23568 ppm/%RH for nonlinear and 5809 ppm/%RH for linear) and repeatability, emphasizing their suitability for real-time humidity monitoring. Moreover, the thermal noise-driven resonators underlying the sensors do not require external input, thereby enhancing their suitability for low-power applications. These findings provide a foundation for innovative real-time sensing applications and offer insights into optimizing sensitivity and stability in humidity sensing and other applications.