A predictive analytical-FEM framework for energy-efficient surface acoustic wave devices via straight-IDT optimization

Surface acoustic wave (SAW) devices are widely used in integrated microsystems, biosensing, microscale actuation, and particle manipulation. Interdigitated transducers (IDTs) are essential components in SAW devices, determining the efficiency of acoustic excitation. IDTs' designs are often optimized through iterative fabrication and experimental testing. We present a modeling-guided methodology that combines analytical IDT modeling, finite-element simulations, and experimental characterization to establish predictive relationships between IDT geometry and device performance. SAW devices with IDT wavelengths of 100 and 300 µm, with finger pairs ranging from 10 to 30, were fabricated on lithium niobate and characterized for resonance, insertion loss, impedance matching, and return loss. The results establish a relationship between the number of finger pairs and electrical/RF performance, which is consistent across analytical predictions, simulations, and measurements. Based on RF characterization, 100 µm-wavelength SAW devices with 10 and 20 finger pairs were integrated with microfluidic channels for particle manipulation. Particle-pushing experiments with 25 µm polystyrene particles showed that the minimum actuation power required for particle displacement varies with the number of finger pairs, demonstrating the influence of transducer design on acoustic actuation. The proposed workflow provides a predictive method for optimizing SAW transducers before fabrication and can be extended beyond acoustofluidic platforms to sensing, microscale actuation, and integrated optoacoustic or photoacoustic microsystems. This approach provides a general framework for connecting RF/electrical transducer characteristics with functional microsystem performance.