Doctoral Defense: Advancing Skyrmion-Based Computing and Memory Technologies

Ilaria Di Manici of the SPINTEC laboratory will defend her doctoral thesis, "Electrical manipulation of topological spin textures for memory and neuromorphic applications," on Thursday, December 18th, at 15:00. The defense will be held in person at the IRIG/SPINTEC auditorium (CEA Building 10.05, Grenoble), with prior access authorization required by December 8th via admin.spintec@cea.fr. It will also be accessible via video conference (Zoom Meeting ID: 918 3363 1116; Passcode: 382054).

The research addresses the critical challenge of rising energy consumption in information technology, particularly from artificial intelligence, by exploring spintronics as an alternative to conventional microelectronics. It focuses on magnetic skyrmions—nanoscale topological spin textures—for next-generation memory and neuromorphic computing.

Key Research Findings:

* High-Speed Skyrmion Motion in Synthetic Antiferromagnets: The first part of the thesis demonstrates that skyrmions in synthetic antiferromagnets (stacked, antiferromagnetically coupled ferromagnetic layers) can be driven by electrical currents at speeds up to 900 m/s. This is approximately an order of magnitude faster than in standard ferromagnets, a performance gain attributed to the compensation of gyrotropic forces due to the antiferromagnetic coupling.

* Electrical Control in Magnetic Tunnel Junctions for Memory: The second part tackles the electrical manipulation of skyrmions within magnetic tunnel junctions (MTJs), a core structure for applications. Using operando magnetic imaging combining electrical detection and X-ray microscopy, the work successfully demonstrates the controlled electrical nucleation and annihilation of skyrmions in MTJs. Time-resolved magnetic microscopy further elucidated these dynamics, paving the way for developing multi-state memory devices.

* Neuromorphic Computing via Physical Reservoir Computing: The final part leverages the dynamic properties of spin textures in MTJs for neuromorphic computation. The system uses the voltage-induced dynamics of a spin texture as the computational "reservoir" in a physical reservoir computing paradigm. Experiments confirmed the system possesses the necessary non-linearity and short-term memory. It successfully performed temporal signal classification—such as distinguishing between sine and square waves with high accuracy—and showed strong performance on other benchmark tasks, indicating a viable path toward nanoscale, low-power AI hardware.

Thesis Committee:

* Rapporteurs: Julie Grollier (CNRS) and Edoardo Albisetti (Politecnico di Milano)

* Examiners: Ales Hrabec (ETH Zurich & Paul Scherrer Institute) and Liliana Buda-Prejbeanu (Grenoble INP – UGA)

* Supervisors: Gilles Gaudin (SPINTEC, thesis director) and Olivier Boulle (SPINTEC, co-supervisor)

The work collectively advances the fundamental understanding and practical electrical control of magnetic skyrmions, presenting significant progress toward their application in high-speed, multi-state memory and energy-efficient neuromorphic computing systems.

On Thursday, December 18th, at 15:00, Ilaria Di Manici (SPINTEC) will defend her PhD thesis entitled :

Electrical manipulation of topological spin textures for memory and neuromorphic applications

Place : IRIG/SPINTEC, CEA Building 10.05, auditorium 445 (presential access to the conference room at CEA in Grenoble requires an entry authorization, request it before December 8th to admin.spintec@cea.fr)

video conference : https://univ-grenoble-alpes-fr.zoom.us/j/91833631116?pwd=DJBtFSKUDnzgNjVKN1gFGLWqW5z0mh.1

ID de réunion: 918 3363 1116

Code secret: 382054

Abstract : In recent years, the energy consumption related to information technology has increased exponentially, partly due to the advent of artificial intelligence. Novel hardware approaches are needed to tackle this issue. Spintronics is emerging as one most promising alternatives to the conventional microelectronics. Within the framework of spintronics, nanoscale magnetic textures known as magnetic skyrmions have attracted a great interest due to their potential applications in information storage, logic and also unconventional computing technologies. However, several limitations have been encountered regarding their electrical manipulation and detection in ferromagnets, which need to be overcome before moving toward applications. In this thesis, we address the electrical manipulation and detection of magnetic skyrmions in magnetic tracks and magnetic tunnel junctions for memory and neuromorphic computing applications. In the first part of this work, we study the current-induced dynamics of magnetic skyrmions in synthetic antiferromagnets, which are composed of two thin ferromagnetic layers antiferromagnetically coupled. We demonstrate that skyrmions in such materials can move about one order of magnitude faster than in ferromagnets, reaching speeds up to 900 m/s. This effect is explained by the compensation of the so-called gyrotropic force exerted on the skyrmions as a result of the antiferromagnetic coupling.

In the second part of the thesis, we study the electrical manipulation of skyrmions in magnetic tunnel junctions, which represents another major challenge for applications. In particular, we demonstrate the electrical control of the nucleation and annihilation process of magnetic skyrmions in magnetic tunnel junctions. To this end, we performed operando magnetic imaging by combining the electrical detection with x-ray microscopy. We also investigated the dynamics of the nucleation and annihilation process using time-resolved magnetic microscopy experiments. These results open a promising path toward a multi-state memory based on magnetic skyrmions. In the third and final part of this thesis, we exploit spin textures in magnetic tunnel junctions to achieve complex temporal pattern recognition using the physical reservoir computing paradigm. For this purpose, we use the spin texture in a magnetic tunnel junction and its dynamical response to electrical stimuli as the computational and memory elements of a neural network. We first show that the voltage induced dynamics of the spin texture exhibit the non-linearity and short-term memory properties required from a physical system to be exploited as a physical reservoir computing. We then demonstrate that such a system can perform temporal signal classification, such as distinguishing in between sine and square waves, with high accuracy, and achieve good performances on other benchmark tasks for reservoir computing systems. These results open a pathway toward nanoscale, low power artificial intelligence hardware based on the manipulation of the spin textures.

Jury :

Julie Grollier, Directrice De Recherche, Cnrs Delegation Ile-De-France, Gif-Sur-Yvette,Rapporteure

Edoardo Albisetti, Associate Professor, Politecnico Di Milano, Rapporteur

Ales Hrabec, Senior Scientist, Eth Zurich & Paul Scherrer Institute, Examinateur,

Liliana Buda-Prejbeanu, Professeure Des Universites, Grenoble Inp – Uga

Thesis supervisors :

Gilles Gaudin, SPINTEC , Directeur de thèse

Olivier Boulle, SPINTEC, Co-encadrant

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