The convergence of surface acoustic wave technology and artificial intelligence

Surface acoustic wave (SAW) devices have become key building blocks in communications, sensing, and emerging neuromorphic hardware, yet their growing complexity in materials, architectures, and operating environments exposes limitations in conventional design and signal processing approaches. Concurrently, machine learning (ML) provides powerful tools for Modeling high-dimensional systems and extracting information from noisy multiparameter data. This review surveys the rapidly developing interface between SAW technology and ML, spanning device-level Modeling, multiparameter sensing, and SAW-driven neuromorphic hardware. We first discuss how ML is used as a surrogate for traditional simulation and compact Modeling. Examples include neural network-based inverse design of SAW resonators from target performance indicators, and regression models used to extract coupling of modes (COM) parameters from simulated or measured responses, which improves the speed and reliability of resonator design. We then examine ML-enhanced SAW sensing, with emphasis on thin-film and flexible Aluminium Scandium Nitride (AlScN) -based devices operating under complex environmental and mechanical conditions. Case studies include flexible UV sensors that use ML regression to decouple bending-induced strain from the target signal, and stacking ensemble frameworks that exploit scattering parameter features to suppress cross-interference between temperature, humidity, and UV intensity. Finally, we highlight how SAWs are being explored as low-power actuators in neuromorphic and AI hardware, enabling acoustic control of phase transitions in Iron-Rhodium alloy (FeRh) and the creation of magnetic skyrmions for artificial synapses and neurons. Across these domains, we identify common patterns in data representation, model selection, and evaluation, and we outline open challenges in dataset curation, interpretability, and deployment on edge and flexible platforms. By bridging the RF design, sensing, and neuromorphic communities, this review charts a path toward ML-native SAW systems that draw on both the rich physics of acoustic waves and the adaptability of modern data-driven methods….