Modeling the anchor effect for estimating performance metrics of a MEMS Pirani gauge

Manu Garg, Sushil Kumar, Dhairya S. Arya, Mujeeb Yousuf, Yi Chiu, Pushpapraj Singh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publication2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665484640
StatePublished - 2022
Event2022 IEEE Sensors Conference, SENSORS 2022 - Dallas, United States
Duration: 30 Oct 20222 Nov 2022

Publication series

NameProceedings of IEEE Sensors
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229


Conference2022 IEEE Sensors Conference, SENSORS 2022
Country/TerritoryUnited States


  • Anchor effect
  • MEMS
  • Pirani gauge
  • Polymer MEMS
  • Thermal conductivity


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