Temporal pattern recognition with delayed feedback spin-torque nano-oscillators

Published

August 23, 2019

Author(s)

Mathieu Riou, Jacob Torrejon, B. Garitaine, Flavio Abreu Araujo, Paolo Bortolotti, Vincent Cros, Sumito Tsunegi, K. Yakushiji, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Damien Querlioz, Mark D. Stiles, Julie Grollier

Abstract

The recent demonstration of neuromorphic computing with spin-torque nano-oscillators has opened a path to energy efficient data processing. The success of this demonstration hinged on the intrinsic short-term memory of the oscillators. In this study, we extend the memory of the spin-torque nano-oscillators through time-delayed feedback. We leverage this extrinsic memory to increase the efficiency of solving pattern recognition tasks that require memory to discriminate different inputs. The large tunability of these non-linear oscillators allows us to control and optimize the delayed feedback memory using different operating conditions of applied current and magnetic field.

Citation

Physical Review Applied

Volume

12

Issue

2

Pub Type

Journals

Download Paper

https://doi.org/10.1103/PhysRevApplied.12.024049

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Keywords

neuromorphic computing, spin-torque oscillator, feedback, magnetic tunnel junction, magnetic vortex, reservoir computing, recurrent neural networks

Citation

Riou, M. , Torrejon, J. , Garitaine, B. , Abreu Araujo, F. , Bortolotti, P. , Cros, V. , Tsunegi, S. , Yakushiji, K. , Fukushima, A. , Kubota, H. , Yuasa, S. , Querlioz, D. , Stiles, M. and Grollier, J. (2019), Temporal pattern recognition with delayed feedback spin-torque nano-oscillators, Physical Review Applied, [online], https://doi.org/10.1103/PhysRevApplied.12.024049, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=926570 (Accessed October 16, 2025)