Manipulation of topological spin textures for neuromorphic computing

In magnetic materials, non-trivial magnetic spin textures may have topological properties, which gave them extra stability as they cannot be annihilated continuously without causing a singularity. These structures, including skyrmions, are particularly promising for applications in neuromorphic computing, where their stability and small size enable efficient information processing and storage. Other magnetic textures, such as meander domain walls, characterized by their wavy, serpentine paths due to local variations in magnetic anisotropy or material imperfections, also play a significant role in this context. The ability to precisely control the dynamics of these structures is crucial for advancing neuromorphic computing technologies. In fact, the manipulation of domain walls can be exploited to mimic the complex, adaptive processes of the human brain. In this context, the goal of this work is to demonstrate the potential of topological spin textures and domain walls for neuromorphic computing. Specifically, this project aims to explore the use of a magnetic system based on meander domain walls to perform reservoir computing for solving recognition tasks. The first part focuses on optimizing material properties to stabilize meander domain walls and skyrmions in a heavy metal/ferromagnet/metal oxide system. Through careful selection of materials and device parameters, it is possible to create a system that supports these topological spin textures, which are essential for advanced neuromorphic computing applications. The second part evaluates and measures the dynamic properties of the system. The goal is to ensure it meets the necessary requirements for reservoir computing, such as stability, non-linearity, and short-term memory, by observing the response of skyrmions and meander domain walls under various stimuli. The final part of the manuscript is devoted to test the capability of the system to perform basic recognition tasks, with a particular focus on recognizing simple waveforms such as sine and square waves. These tests demonstrate the potential of the optimized material system for practical applications in reservoir computing, paving the way for more complex neuromorphic computing tasks in the future.