Multilayer Ferromagnetic Spintronic Devices for Neuromorphic Computing Applications
Aijaz H. Lone, Xuecui Zou, Kishan K. Mishra, Venkatesh Singaravelu,, Hossein Fariborzi, Gianluca Setti

TL;DR
This paper demonstrates a multilayer ferromagnetic spintronic device with multilevel resistance states, suitable for neuromorphic computing, achieving 90% accuracy on MNIST and potential use in cryogenic quantum memory.
Contribution
It presents the first experimental and micromagnetic realization of such a device for neuromorphic applications, with demonstrated neural network performance.
Findings
Device exhibits temperature-dependent multilevel resistance states.
Resistance states evolve with spin-orbit torque in experiments and simulations.
Achieves 90% accuracy on MNIST dataset.
Abstract
Spintronics has gone through substantial progress due to its applications in energy-efficient memory, logic and unconventional computing paradigms. Multilayer ferromagnetic thin films are extensively studied for understanding the domain wall and skyrmion dynamics. However, most of these studies are confined to the materials and domain wall/skyrmion physics. In this paper, we present the experimental and micromagnetic realization of a multilayer ferromagnetic spintronic device for neuromorphic computing applications. The device exhibits multilevel resistance states and the number of resistance states increases with lowering temperature. This is supported by the multilevel magnetization behavior observed in the micromagnetic simulations. Furthermore, the evolution of resistance states with spin-orbit torque is also explored in experiments and simulations. Using the multi-level resistance…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Memory and Neural Computing · Magnetic properties of thin films · Neural Networks and Reservoir Computing
