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



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


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.
Physical Review Applied


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


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],, (Accessed April 24, 2024)
Created August 22, 2019, Updated October 12, 2021