Conventional microelectronic components such as transistors and solid-state memories process and store information by the manipulation of electrical currents. It is the flow of electrons in these devices that generates heat and is responsible for their poor energy efficiency. New technologies that harness the quantum mechanical spin property of electrons (so called spintronics) offer the prospect of rapid and low power information processing and storage. In particular spintronic devices based on antiferromagnetic materials have very recently emerged as a disruptive low power technology. However, materials employed in early prototype devices are not yet optimised sufficiently for practical application and the large parameter space of possible material combinations makes this an extremely challenging problem.
This project aims to employ predictive first principles materials modelling to screen promising materials for antiferromagnetic (AF) spintronics (specifically Mn-based alloys) against their bulk crystal properties, together with heterointerfaces present in thin-film devices, intrinsic point and extended defects which may impact on material properties. Predictive theoretical modelling can be invaluable in identifying problematic defects in advance and suggesting routes to their mitigation (e.g., through modification of material composition). The findings will be used to guide experimental materials growth and characterisation in the group of Prof. Atsufumi Hirohata (Electronic Engineering).
Please note that for PhD projects advertised as “awaiting funding”, we anticipate that the majority of decisions will be made in December 2019.