Machine learning empowered thin film acoustic wave sensing

Abstract

Thin film-based surface acoustic wave (SAW) technology has been extensively explored for physical, chemical, and biological sensors. However, these sensors often show inferior performance for a specific sensing in complex environments, as they are affected by multiple influencing parameters and their coupling interferences. To solve these critical issues, we propose a methodology to extract critical information from the scattering parameter and combine the machine learning method to achieve multi-parameter decoupling. We used the AlScN film-based SAW device as an example in which the highly c-axis orientated and low stress AlScN film was deposited on silicon substrate. The AlScN/Si SAW device showed a Bode quality factor value of 228 and an electromechanical coupling coefficient of ∼2.3%. Two sensing parameters (i.e., ultraviolet or UV and temperature) were chosen for demonstration, and the proposed machine learning method was used to distinguish their influences. Highly precision UV sensing and temperature sensing were independently achieved without their mutual interferences. This work provides an effective solution for decoupling of multi-parameter influences and achieving anti-interference effects in thin film-based SAW sensing.