On-chip phonon-magnon reservoir for neuromorphic computing

Abstract

Reservoir computing is a concept in which signals to process are mapped onto a high-dimensional phase space of a fixed dynamical system called “reservoir” for subsequent recognition by an artificial neural network. Implementations of reservoirs are possible using different hardware and, accordingly, exploit different carriers and mechanisms of signal transformation. Despite the growing number of neuromorphic prototypes of reservoirs, demands for miniaturization, efficiency, and robustness all require implementation of the reservoir on a chip, and remain among the key challenges. Here we propose a nanodevice, in which a sandwich of a semiconductor phonon waveguide and a patterned ferromagnetic layer enables efficient reservoir computing. The optical input signal is coded by a pulsed write-laser and converted into a propagating multimode phonon wave packet, which interacts with a bunch of magnon modes. The output signal read by a second laser represents a phase-sensitive superposition of all the phonon and magnon modes, which possesses ultimate sensitivity to the relative positions of the write- and read-laser spots. The reservoir efficiently separates the visual shapes drawn by the write-laser beam on the nanodevice surface in an area with size comparable to a single pixel of a modern digital camera. Thus, our finding suggests the phonon-magnon interaction as a promising hardware basis for realization of rapid on-chip reservoir computing for future neuromorphic architectures.