Multiscale Approach to Spintronics - From Nanomagnet to Topological Excitations

The rapid growth of information and data has been pushing the limit of our data storage and processing capabilities. At the hardware level, the physical scaling of the transistor technology has been increasingly difficult and expensive. Magnet, with its long history of being used in information storage, has re-branded itself through the so-called spintronics, which studies how to control and manipulate the magnetic moment (or electron spin) for modern computation. Unlike silicon, researchers have yet to identify an ideal material nor find a perfect way to take advantage of the extra degree of freedom from electron spins. This dissertation describes our study of the prospect and limitation of several spintronic systems as well as our methodology - a multiscale approach that goes from atomistic material properties to device-level performance. At the material level, we use the Non-Equilibrium Green's Function (NEGF) simulations to study the spin transport in nanostructures such as the magnetic tunnel junction, investigating the effect of different electrode materials and interfaces on its read/write performance. At the device level, we implemented a fast, general solver to simulate the thermal noise-induced switching error in nanomagnets, revealing its correlations with various material parameters and magnetic configurations. With an understanding of the limitations of magnetic tunnel junctions, we examined two emerging topological systems - topological insulator (TI) and magnetic skyrmion. In studying the topological insulator, we proposed an alternative way to verify the Klein-Tunneling physics in a PN junction setup on the TI surface. We also discussed its potential as a spin source. For skyrmions, we analyzed the conditions to achieve ultrasmall, fast, and stable skyrmions and offered few material candidates with their corresponding skyrmion phase diagrams.