Microelectromechanical system (MEMS)-based mass sensors are proposed as potential candidates for highly sensitive chemical and gas detection applications owing to their miniaturized structure, low power consumption, and ease of integration with readout circuits. This paper presents a new approach in developing micromachined mass sensors based on capacitive and piezoelectric transducer configurations for use in low concentration level gas detection in a complex environment. These micromachined sensors operate based on a shift in their center resonant frequencies. This shift is caused by a change in the sensor’s effective mass when exposed to the target gas molecules, which is then correlated to the gas concentration level. In this work, capacitive and piezoelectric-based micromachined sensors are investigated and their principle of operation, device structures and configurations, critical design parameters and their candidate fabrication techniques are discussed in detail.
Advancements in microfabrication technologies and novel materials have led to new innovations in miniaturized gas sensors that can identify miniscule changes in a complex environment. Micromachined resonators with the capability to offer high sensitivity and selectivity in array integration make mass loading a potential mechanism for electronic nose applications. This paper investigates the mass sensing characteristics of progressive capacitive based micromachined resonators as potential candidates for volatile organic compound detection where also there is a need for miniaturized array configuration. In this paper, a detailed investigative review of the major three geometric designs of capacitive based micromachined resonators, namely, the microcantilever, the microbridge and the clamped membrane sensors is performed. Although many reviews are present in literature regarding mass sensors, however there is a gap in the literature regarding the common capacitive based micromachined mass sensors. This research gives a review on the foundation for capacitive based micromachined mass sensors while highlighting the potential capabilities of each geometric design to be developed further. Moreover, this paper also introduces the advancements based on the geometric designs of the capacitive based micromachined mass sensors. An in-depth analysis is done for each geometric design, to identify the critical design parameters, which affect the sensors’ performances. Furthermore, the theoretically achievable mass sensitivity for each capacitive based micromachined mass sensor is modeled and analyzed using finite element analysis with mass variation in the picogram range. Finally, a critical analysis is done on the sensor sensitivities and further discussed in detail wherein each design is compared to each other and its current advances. Additionally, an insight to the advantages and disadvantages associated with each simulated geometry and its different advances are given. The results of the investigative review and analysis indicate that the sensitivities of the capacitive based micromachined sensors are dependent not only on the material composition of the devices but also on the varying degrees of clamping between the sensor geometries. In essence, the paper provides future research the groundwork to choose proper candidate geometry for a capacitive based micromachined mass sensor, with its several advantages over other mass sensors, based on the needed application.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.