2024/07/09 by Alexander López, Deyse G. Costa, Lopez, A. +17
Computer Science · Engineering · Physics and Astronomy · #Advanced Memory and Neural Computing #Applied Physics (physics.app-ph) #FOS: Physical sciences #Magnetic properties of thin films #Materials Science (cond-mat.mtrl-sci) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.2407.06768
openalex publication_date 2024/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A promising branch of neuromorphic computing aims to perform cognitive operations in hardware leveraging the physics of efficient and well-established nano-devices. In this work, we present a reconfigurable classifier based on a network of electrically connected magnetic tunnel junctions that categorizes information encoded in the amplitude of input currents through the spin torque driven magnetization switching output configuration. The network can be trained to classify new data by adjusting additional programming currents applied selectively to the junctions. We experimentally demonstrate that a network composed of three magnetic tunnel junctions can learn to classify spoken vowels with a recognition rate that surpasses the performance of software multilayer neural networks with the same number of trained parameters in this task. These results, obtained with the same nano-devices and working principle employed in industrial spin-transfer torque magnetic random-access memories (STT-MRAM), constitute an important step towards the development of large-scale neuromorphic networks based on well-established technology.