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Mechanisms of defect processes in future electronic devices

Project ID: 2228cd1414 (You will need this ID for your application)

Research Theme: Advanced Materials

UCL Lead department: Physics and Astronomy

Department Website

Lead Supervisor: Alexander Shluger

Project Summary:

The power consumption and reliability of novel electronic devices strongly depends on properties of defects in constituent materials and their interfaces. Defects are also considered as prospective candidates for quantum computation and are responsible for new modes of device operation, such as neuromorphic systems which use defect processes in materials to develop fundamentally new brain inspired approaches to computing.

This PhD project will use computational modelling to predict new metal/semiconductor/insulator heterostructures and their deposition techniques required to create electronic devices with reduced power consumption and new functionalities. This will include using the existing and developing novel methods for modelling the structure and properties of such heterostructures using atomistic modelling, Density Functional Theory (DFT) and Machine Learning. The project will use large-scale DFT and classical simulations to explore the role of intrinsic defects and impurities in performance of Si-based and 2D-materials based electronic devices, such as transistors, memristors and neuromorphic memory cells to develop new modes of their operation, such as neuromorphic computation. You will learn how to use computer modelling to solve fundamental problems of real impact for design and technology of electronic devices in collaboration with experimental colleagues.

The PhD training and research will be carried out in the [group of Prof. Alexander Shluger] ( in the Department of Physics and Astronomy and within the vibrant environment of the London Thomas Young Centre. The group is one of the world leaders in computational modelling of defects in insulators and heterostructures underpinning the performance and reliability of electronic devices.

We are looking for a highly motivated candidate with a top-level MSci degree or equivalent in Chemistry, Physics, or Materials. Undergraduate knowledge of Quantum Physics and Solid State Physics is essential. You should enjoy coding and be keen to push the boundaries of machine learning and artificial intelligence in materials applications.