Machine learning to enhance strain-resilience humidity sensing on flexible surface acoustic wave platform

Flexible surface acoustic wave (SAW) humidity sensors have garnered considerable attention in fields such as environmental monitoring and healthcare, mainly attributed to their advantages such as wearability, applicability in non-planar scenarios, quasi-digital output, and wireless passive capabilities. However, improvement in performance of these flexible SAW humidity sensors faces great challenges such as low electromechanical coupling coefficient, poor humidity response or sensitivity, and introduction of detection errors caused by mechanical strain interference. Herein, we developed a flexible SAW humidity sensor utilizing an aluminum scandium nitride (AlScN) piezoelectric film deposited on ultrathin glass substrates, incorporating ternary nanocomposites of graphene quantum dots-polyethyleneimine-silica nanoparticles (GQDs-PEI-SiO 2 NPs) as the sensitive layers, which demonstrated an ultra-high sensitivity of 5.02 kHz (kHz)/%Relative Humidity (RH). To address critical issues of strain interferences under randomly bending or deformation conditions, we applied machine learning (ML) algorithms to establish correlations between sensor's response signal features and humidity labels, thereby effectively mitigating unreliable humidity measurements caused by significant strain interferences, with improved precision and specificity. After comprehensive evaluation and analysis using various artificial intelligence algorithms, multilayer perceptron regression model was identified as the best performer in humidity prediction under strain interferences, with a coefficient of determination as high as 0.997 and a mean square error of ∼0.479. Reliability and generalization capabilities of this model were verified, and such the strategy not only significantly enhances the performance metrics of flexible humidity sensors but also provides an innovative and precision solution under various strain interferences using the flexible SAW sensors. • Flexible SAW device with AlScN thin film on ultra-thin glass enables high-performance sensing. • GQDs-PEI-SiO₂ nanocomposite layer boosts humidity sensitivity to 5.02 kHz/%RH, low hysteresis. • MLPR model minimizes strain interference for precise humidity monitoring via machine learning.