Vacancies

Modelling diffusion of atoms in metal interconnects in devices

Please note that owing to funding restrictions beyond the supervisors’ control, this PhD studentship is open only to British citizenships or to EU citizens with UK (pre-)settled status eligible for home fees.

As the dimensions of nanodevices continue to shrink and approach 2 nm, the complexity of the manufacturing process increases. Metal interconnects that link different parts of integrated circuits are the key factor in determining device performance, speed, and power efficiency. This PhD project aims to improve efficiency of power-hungry AI devices through better understanding of the mechanisms of their degradation. It will combine density functional theory simulations, development and applications of machine learning potentials and molecular dynamics simulations to provide atomistic-level understanding of the diffusion properties of metal atoms in semiconductor/metal heterostructures. These results will be used to study the movement of metal atoms under the influence of electric field in device settings and to model electromigration and growth of voids leading to interconnect failure (see figure below). 

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/Chemistry and Solid-State Physics/Chemistry is essential. You should enjoy coding and be keen to push the boundaries of materials modelling in close collaboration with industrial partners. The PhD training and research will be carried out in the group of Prof. Alexander Shluger in the Department of Physics and Astronomy, UCL and within the vibrant environment of the London Thomas Young Centre. Good writing and presentation skills and the ability to actively collaborate in a multidisciplinary team of theorists, experimentalists and engineers is essential.

Applications should be made to UCL by the 1st of January 2026 and will be considered as soon as possible. The expected start date can be as soon as possible or in September/October 2026. The award will cover fees and an annual stipend at the EPSRC-DTP rate. For further information on this PhD position please contact Prof. Alexander Shluger a.shluger@ucl.ac.uk .

Figure: Scanning Electron Microscope image of voids created in a Cu interconnect as a result of electromigration.

Modelling dynamics of polaron formation in oxides

Please note that owing to funding restrictions beyond the supervisors’ control, this PhD studentship is open to British citizenships or EU citizens with UK (pre-)settled status only.

Non-radiative dissipation of excess electronic energy is a ubiquitous phenomenon in nature. It contributes to a wide variety of processes in biological and technological systems from vision to classical computing and optoelectronic devices, from photo-electro-catalysis to radiation damage, from antibacterial action and DNA damage to laser-driven nuclear fusion. Hence, developing a better understanding of these processes is of crucial importance. However, ab-initio modelling of such phenomena is a challenging problem due to the dynamics spanning several time- and energy- scales.

This 4-year project is a mix of method development, software development, and practical simulations of realistic systems. It aims: 1) to perform real-time Time-Dependent Density Functional Theory (RT-TDDFT) simulations coupled to Ehrenfest molecular dynamics to develop an understanding of the ultra-fast electron dynamics of polaron formation in oxides, 2) quantify the accuracy, thence viability, of Physics-Informed Neural Networks (PINNs) for Partial Differential Equations (PDEs) as computationally cheaper alternative to RT-TDDFT towards larger-scale, longer-time simulations, and 3) implement more sophisticated molecular dynamics methods, such as Tully surface hopping (TSH), to study polaron formation in more detail.

This studentship is co-funded by the Ada Lovelace Centre (ALC) and University College London (UCL) and brings together expertise in modelling defects, oxides, and polarons at UCL with expertise in real-time dynamics, spectroscopy, and machine learning at SCD-STFC. The ALC provides scientific computing support to the Science and Technology Facilities Council (STFC) central facilities and related departments – Diamond Light Source, ISIS Neutron and Muon Source, the Central Laser Facility, the STFC Scientific Computing Department (SCD), and the Rosalind Franklin Institute.

The balance of training activities will be tailored to the selected student’s skills, inclinations, and long-term career ambitions. At UCL 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.

The student must spend at least one year of the project time at a STFC site (Rutherford Appleton or Daresbury Laboratory), in a schedule to be arranged by mutual agreement between the ALC and UCL supervisors together with the selected candidate. Research activities at the SCD-STFC will be carried out within the Theoretical and Computational Physics Group (TCPG). The TCPG is an international, family friendly, diverse group who have played a significant role in many ground-breaking UK code development projects including CASTEP, CRYSTAL, ONETEP, Wannier90, and QUESTAAL. Upon completion of the PhD and pending a separate open SCD-STFC screening-process, the selected candidate, who will have demonstrated the development of substantial theory and software skills, will be considered for permanent employment at SCD-STFC.

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/Chemistry and Solid State Physics/Chemistry is essential. You should enjoy coding and be keen to push the boundaries of machine learning and artificial intelligence in state of the art first principles materials modelling. Applications should be made to UCL as soon as possible but no later than 1 April 2024 with an expected start date in September/October 2024. The award will cover fees and an annual stipend at the EPSRC-DTP rate.

Informal enquiries should be addressed to Prof. Alexander Shluger (a.shluger@ucl.ac.uk), Dr. Peter Elliott (peter.elliott@stfc.ac.uk), and Dr. Paolo Trevisanutto (paolo.trevisanutto@stfc.ac.uk).