2024/12/15 by Sreeveni Das, Das, Sreeveni, Rhodri Mansell +8 · 2 citations
Computer Science · Engineering · #Advanced Memory and Neural Computing #Applied Physics (physics.app-ph) #FOS: Physical sciences #Neural Networks and Applications #Neural Networks and Reservoir Computing
paper · doi:10.48550/arxiv.2412.11297
openalex publication_date 2024/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
Neuromorphic computing aims to revolutionize large-scale data processing by developing efficient methods and devices inspired by neural networks. Among these, the control of magnetism through ion migration has emerged as a promising approach due to the inherent memory and nonlinearity of ionically conducting and magnetic materials. In this work, we present a lithium-ion-based magneto-ionic device that uses applied voltages to control the magnetic domain state of a perpendicularly magnetized ferromagnetic layer. This behavior emulates the analog and non-volatile properties of biological synapses and enables the creation of a simple reservoir computing system. To illustrate its capabilities, the device is used in a waveform classification task, where the voltage amplitude range and magnetic bias field are tuned to optimize the recognition accuracy.