A Novel Framework for SAW Gas Sensor Optimization via FEM-COM Co-Modeling and Genetic Algorithm

• A hybrid FEM–COM framework is proposed for efficient optimization of SAW gas sensor IDT geometry. • Key physical parameters under gas loading are extracted via FEM and embedded into a COM analytical model. • Genetic algorithm–based optimization maximizes SAW sensor sensitivity toward target gases. • The proposed approach significantly reduces computational cost compared with full FEM optimization. • Simulation results demonstrate improved sensitivity and effective guidance for SAW gas sensor design. This paper presents a computational framework for optimizing the geometry of interdigital transducers (IDTs) in surface acoustic wave (SAW) gas sensors, with the objective of improving sensitivity. The framework is demonstrated using a YZ-cut LiNbO₃ piezoelectric substrate, aluminum IDTs, and a PIB sensing layer for DCM detection. The proposed approach integrates finite element modeling (FEM) in COMSOL Multiphysics with a coupling-of-modes (COM) analytical model implemented in MATLAB. FEM simulations are used to extract critical physical parameters, including acoustic velocity, transduction, reflection, capacitance, and attenuation, based on various IDT configurations under different gas-loading conditions. These parameters are then incorporated into the COM model to compute the electrical response of the sensor, particularly the transmission coefficient S 21 . A genetic algorithm is employed to optimize the IDT geometry, focusing specifically on maximizing the sensor’s sensitivity toward target gases. This hybrid framework reduces computational overhead compared to full-scale FEM optimization, while maintaining physical accuracy. Preliminary results confirm the effectiveness of the proposed method in guiding SAW sensor design toward enhanced gas detection performance.