Piezoelectric surface acoustic wave memristor neural network.

Processing wireless RF signals using analog electromagnetic (EM) wave-based neural networks enables energy efficiency and parallelism by integrating sensing, memory, and computation, avoiding analog-to-digital conversions (ADCs) and the von-Neumann bottleneck. Yet, a notable challenge remains: the absence of compact and programmable building blocks for EM wave-based neural networks. To overcome this limitation, we propose a piezoelectric surface acoustic wave (SAW) memristor that integrates an Ag/SiO2/Au memristor with an acoustoelectric phase shifter. Operating at shorter wavelengths than EM waves, it offers a compact footprint and encodes tunable neural network parameters via nonvolatile programmability and the acoustoelectric effect. A proof-of-concept SAW memristor neural network was experimentally demonstrated on a vector classification task, achieving 91.7% accuracy on par with software while reducing footprint by 105 times versus EM systems and energy consumption by 37 times versus digital systems. This work paves the way for compact, energy-efficient RF signal processing at the edge.