Data-based surrogate modeling and performance prediction of MEMS touch mode capacitive sensing using deep multilayer perceptron networks
- 1School of Electronics Engineering, Vellore Institute of Technology, Vellore , ,
Abstract
Introduction: This work presents a structurally optimized rectangular diaphragm that operates in touch mode as the basis for a high-performance micro-electro-mechanical system (MEMS) capacitive pressure sensor (CPS). Rectangular diaphragms offer significant advantages in terms of structural control, constant stress handling and directional extension of the contact region.
Methods: The proposed framework encompasses structural design, analytical modeling, numerical computation, finite element analysis (FEA) and Artificial Intelligence (AI)-based prediction. In this design, greater directional control results in a smoother and more reliable change in capacitance with pressure. The rectangular diaphragm design can be changed to smooth the transition between normal and touch modes and produce more consistent linearity and sensitivity. Numerical computation, FEA simulation and deep neural network (DNN) surrogate model for predicting the performance characteristics of a novel MEMS capacitive pressure sensor. A comprehensive dataset is constructed by combining Numerical computation and FEA-simulated results across a wide range of diaphragm geometries and applied pressure conditions. A DNN surrogate model is subsequently developed and trained on this dataset to predict the performance characteristics of the proposed MEMS capacitive pressure sensor.
Results: The model achieves an overall mean absolute percentage error (MAPE) of 0.22 % and R2 above 0.999 across all four outputs, a substantial speed improvement, with prediction time reduced to a matter of seconds compared to the tens of min required for direct FEA simulation.
Discussion: The results show that the proposed rectangular diaphragm effectively controls the touch region. It also improves capacitance and sensitivity. The DNN surrogate model predicts sensor performance precisely and efficiently. It enables rapid design-space exploration for MEMS capacitive pressure sensors.
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BibTeX
@article{pushparaj2026databased,
title = {{Data-based surrogate modeling and performance prediction of MEMS touch mode capacitive sensing using deep multilayer perceptron networks}},
author = {Pushparaj E and Sumit Kumar Jindal},
journal = {Frontiers in artificial intelligence},
year = {2026},
month = oct,
doi = {10.3389/frai.2026.1960066},
url = {https://europepmc.org/article/PMC/PMC13724490},
note = {CC BY},
}
RIS
TY - JOUR
TI - Data-based surrogate modeling and performance prediction of MEMS touch mode capacitive sensing using deep multilayer perceptron networks
AU - Pushparaj E
AU - Sumit Kumar Jindal
PY - 2026
DA - 2026/10/19
JO - Frontiers in artificial intelligence
DO - 10.3389/frai.2026.1960066
UR - https://europepmc.org/article/PMC/PMC13724490
AB - Introduction: This work presents a structurally optimized rectangular diaphragm that operates in touch mode as the basis for a high-performance micro-electro-mechanical system (MEMS) capacitive pressure sensor (CPS). Rectangular diaphragms offer significant advantages in terms of structural control, constant stress handling and directional extension of the contact region. Methods: The proposed framework encompasses structural design, analytical modeling, numerical computation, finite element analysis (FEA) and Artificial Intelligence (AI)-based prediction. In this design, greater directional control results in a smoother and more reliable change in capacitance with pressure. The rectangular diaphragm design can be changed to smooth the transition between normal and touch modes and produce more consistent linearity and sensitivity. Numerical computation, FEA simulation and deep neural network (DNN) surrogate model for predicting the performance characteristics of a novel MEMS capacitive pressure sensor. A comprehensive dataset is constructed by combining Numerical computation and FEA-simulated results across a wide range of diaphragm geometries and applied pressure conditions. A DNN surrogate model is subsequently developed and trained on this dataset to predict the performance characteristics of the proposed MEMS capacitive pressure sensor. Results: The model achieves an overall mean absolute percentage error (MAPE) of 0.22 % and R2 above 0.999 across all four outputs, a substantial speed improvement, with prediction time reduced to a matter of seconds compared to the tens of min required for direct FEA simulation. Discussion: The results show that the proposed rectangular diaphragm effectively controls the touch region. It also improves capacitance and sensitivity. The DNN surrogate model predicts sensor performance precisely and efficiently. It enables rapid design-space exploration for MEMS capacitive pressure sensors.
SN - 2624-8212
ER -