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

## TL;DR

This paper enhances the memory capacity of spin-torque nano-oscillators using time-delayed feedback, improving their efficiency in pattern recognition tasks for neuromorphic computing.

## Contribution

It introduces a method to extend the memory of spin-torque nano-oscillators via time-delayed feedback, optimizing their performance for pattern recognition.

## Key findings

- Extended oscillator memory improves pattern recognition efficiency.
- Tunable feedback allows optimization under various conditions.
- Enhanced neuromorphic computing capabilities demonstrated.

## 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.

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Source: https://tomesphere.com/paper/1905.02695