This project will deliver unprecedented atomic scale high speed characterisation capability to solve currently impossible characterisation challenges for quantum materials. Building on state-of-the-art transmission electron microscopy (TEM) instrumentation and expertise, combined with deep/machine learning (DL/ML) and human vision approaches, this PhD will seek to deliver automatic, quantitative, and statistically representative imaging of local structure, composition and bonding with atomic resolution over mm areas for semiconductor systems.

The ability to deterministically dope isotopically selected single impurity ions into systems such as (isotopically enriched) Si, SiGe, diamond and 2D materials is a pressing requirement for the development of spin and photonic-based quantum technologies. Optical methods can typically locate implanted single atom qubits in semiconductors to 1µm spatial resolution but cannot probe their local atomic environment. TEM is the only approach that could characterise these buried defect sites but the technique is manual and laborious. Finding a specific atomic feature within a 1µm square field of view could require 10 million atomic TEM images (4 months continuous data collection). Consequently, the nature of many qubit defects remains unknown, hindering their further exploitation and optimisation.

We will overcome this limitation by developing advanced TEM imaging approaches powered by DL/ML control. Using automation, innovative, high speed, event responsive scanning and DL feature identification, this project will develop methods to automatically locate and image point defects in semiconductor crystals at the atomic scale. Once the defect features are identified electron energy loss spectroscopy will enable full characterisation and even manipulation of the local bonding environment.

This world-first characterisation capability will be applied to unlock the ability to reliably produce large arrays (~1 million) of isotopically selected ions as the basis for a fully error-corrected quantum computer. The successful PhD candidate will develop the skills required for a future career as a leading independent researcher in materials for quantum technologies.

Before you apply: We strongly recommend that you contact the supervisor(s) for this project before you apply.

How to apply: To be considered for this project you’ll need complete a formal application through our online application portal. This link should directly open an application for FSE Bicentenary PhD .

When applying, you’ll need to specify the full name of this project , the name of your proposed supervisor/s , details of your previous study, and names and contact details of two referees . You also need to provide a Personal Statement describing the motivation to apply to the project and your CV. Your application cannot be processed without all of the required documents, and we cannot accept responsibility for late or missed deadlines where applications are incomplete.

Equality, diversity and inclusion: Equality, diversity and inclusion are fundamental to the success of The University of Manchester, and are at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

Eligibility: Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or equivalent) in a relevant science or engineering related discipline.

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