AI-assisted Helium Atom Microscopy for Data-driven Characterisation of Quantum Diamond Materials

About the Project

This project is part of cohort 3 of the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute.

Diamond is an emerging platform for quantum technologies, hosting defect centres that can act as ultra-sensitive sensors or single-photon sources for quantum communication. These defects are typically created by ion implantation followed by thermal or laser activation. However, the performance of these devices depends critically on the structure of the material within a few tens of nanometres of the surface, where implantation damage and surface chemistry play a key role.

Understanding and controlling this near-surface region remains a major challenge. Conventional microscopy techniques often struggle with insulating materials or can introduce damage during measurement. This project will address this challenge by developing helium atom microscopy (SHeM) as a non-destructive method for studying the surface structure of implanted diamond.

SHeM uses a neutral, low-energy helium beam, allowing imaging of sensitive materials without charging or damage. It provides information on both surface topography and crystallographic order, making it uniquely suited to studying how implantation and processing affect the structure of diamond at the nanoscale.

The student will prepare diamond samples using advanced fabrication techniques, including deterministic or single-ion implantation, ultrafast laser activation, and surface treatments such as chemical termination and ion-beam-based smoothing. These processes will be systematically varied to understand how fabrication conditions influence material structure and device performance.

The project will involve developing measurement protocols using helium atom microscopy to quantify surface roughness, disorder, and crystallinity across sets of samples. These measurements will be combined with optical characterisation of quantum defects, such as photoluminescence spectroscopy, to link surface structure to device-relevant properties.

A key aspect of the project will be the analysis of large experimental datasets. The student will develop workflows to extract quantitative information from microscopy and diffraction data, and will apply statistical and machine-learning techniques to identify relationships between fabrication parameters and resulting material properties. This will enable the development of predictive models to guide optimisation of fabrication processes.

The project will provide insight into how implantation damage evolves during processing and how it can be controlled or mitigated. This is essential for improving the reproducibility and performance of diamond-based quantum devices.

Beyond diamond, the methods developed will be applicable to a wide range of materials systems where surface structure plays a critical role, including semiconductor devices, catalytic materials, and advanced coatings.

The student will work in a highly interdisciplinary environment at the interface of physics, materials science, and instrumentation, and will collaborate with an industrial partner developing advanced ion beam technologies. The project offers opportunities to develop skills in advanced experimental techniques, data analysis, and emerging digital approaches to materials science. Day to day, the student will design and carry out experiments, analyse data, and develop models to understand how fabrication processes affect material performance.

This project is suitable for candidates with a background in physics, materials science, engineering, or a related discipline, with an interest in experimental research and data analysis.