@inproceedings{32428b9c845a46878d6c3295523ba954,
title = "Parametric Model of Differential SAW Pressure Sensor: Enabling Rapid Performance Evaluation and Optimization Design",
abstract = "Differential Surface Acoustic Wave (SAW) pressure sensors are widely used across various fields. To enable faster and more accurate performance evaluations, we developed a parametric model. This model divides the interdigital transducer (IDT) structure into several microelements and combines it with a small deflection deformation model of the diaphragm, allowing for rapid calculation of sensor performance parameters under different structures. Comparisons with existing experimental results show that the model achieves an accuracy of over 80\%. Owing to its computational speed, we applied the model to uncertainty analysis and structural optimization. The results indicate that the width of the IDT has the greatest impact on sensitivity uncertainty. After optimization, the sensitivity increased by a factor of 2.18, and the uncertainty decreased to 60.5\% of the original.",
keywords = "optimization design, parametric model, saw pressure sensor, uncertainty analysis",
author = "Aobei Chen and Ge Gao and Dapeng Li and Dezhi Zheng",
note = "Publisher Copyright: {\textcopyright} 2025 ACM.; 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp Companion 2025 ; Conference date: 12-10-2025 Through 16-10-2025",
year = "2025",
month = dec,
day = "29",
doi = "10.1145/3714394.3756272",
language = "English",
series = "UbiComp Companion 2025 - Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
publisher = "Association for Computing Machinery, Inc",
pages = "1376--1381",
editor = "Michael Beigl and Giulio Jacucci and Stephan Sigg and Yu Xiao and Bardram, \{Jakob E.\} and Tsiropoulou, \{Eirini Eleni\} and Chenren Xu",
booktitle = "UbiComp Companion 2025 - Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
}