Chiral Non-stationary Magnonic Auto-oscillator for neuromorphic computing

Descripción del proyecto

Nuevos nanoosciladores para imitar mejor las funciones cerebrales

La informática neuromórfica, inspirada en la forma en que el cerebro humano procesa la información, es un campo en rápido crecimiento cuyo objetivo es crear sistemas más rápidos y eficientes desde el punto de vista energético. Con el apoyo de las acciones Marie Skłodowska-Curie, el proyecto CNMA dará un paso adelante con el desarrollo de innovadores nanoosciladores de torque de espín denominados autoosciladores magnónicos quirales no estacionarios. Estos dispositivos utilizarán corrientes eléctricas moduladas temporalmente y señales especiales para imitar capacidades similares a las del cerebro, como la memoria a corto plazo y las respuestas no lineales a frecuencias elevadas de gigahercios. La investigación propuesta se centrará en hacer estos dispositivos más eficientes y rápidos para su uso en redes inspiradas en el cerebro. Además, explorará cómo conectar múltiples dispositivos para potenciar su capacidad de cálculo.

Objetivo

This project aims to advance the field of neuromorphic computing by exploiting novel non-stationary dynamics in spin torque nano-oscillator, called here chiral non-stationary magnonic auto-oscillators. The core innovation lies in the numerical and experimental demonstration of time-modulated electric current (TMEC) stimulation combined with secondary stimuli to induce robust nonlinear excitations capable of sustaining neuromorphic computations. Key features such as short-term memory, echo-state properties and strong non-linear responses are achieved in the GHz frequency range, specifically targeting skyrmion and vortex textures.

The project will focus on optimising CNMA for analogue neural networks by exploring time-dependent stimulation, improving energy efficiency and computational speed. A crucial part of the research will be a close collaboration with the renowned group at the University of Kaiserslautern, where I will receive specialised training in advanced experimental techniques for magnetoresistive and spectral analysis. This will be cross validated with micromagnetic simulations, combining the expertise of the fellow and the host group. The success of the project is also guaranteed by international cooperation with renowned research centres providing samples.

Towards the end of the project, we will numerically demonstrate the scalability of the CNMA system by coupling multiple CNMAs via spin waves in a ferromagnetic strip, mimicking additional neural connections. This approach promises to significantly increase the computational power of CNMA-based reservoir computing systems. Through this partnership, fellow aim to establish a strong international research network, which will open opportunities for long-term collaborations and enhance my position within the scientific community. This extensive cooperation will also play a crucial role in securing future employment within academic institutions or R&D sectors in industry.