@inproceedings{3f8830a37c2d4c15bf7bf410ac6c4f69,
title = "Modeling the anchor effect for estimating performance metrics of a MEMS Pirani gauge",
abstract = "Accurate prediction of theoretical limits is critical in MEMS Pirani gauge where the gauge output translates into the primary sensor calibration. In particular, the solid conduction loss (Qs) which determines the detection limit and constitutes a major chunk of total power consumption. Herein, we present a theoretical framework that accurately quantifies the Qs by incorporating an anchor effect. Finite element model (FEM) simulations realize the temperature profile and highlight the effect of anchor in modifying the effective thermal conductivity (keff) and hence Qs. A microbridge type Pirani gauge is fabricated, and the gauge successfully measures the vacuum from 30 Pa to 105 Pa. Theoretical estimations are verified with the measured data and a 37% reduction in Qs error is achieved by incorporating the anchor effect.",
keywords = "Anchor effect, MEMS, Pirani gauge, Polymer MEMS, Thermal conductivity",
author = "Manu Garg and Sushil Kumar and Arya, {Dhairya S.} and Mujeeb Yousuf and Yi Chiu and Pushpapraj Singh",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Sensors Conference, SENSORS 2022 ; Conference date: 30-10-2022 Through 02-11-2022",
year = "2022",
doi = "10.1109/SENSORS52175.2022.9967077",
language = "English",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings",
address = "美國",
}