About the Project
Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.
The PhD will be based in the School of Electrical and Mechanical Engineering and supervised by Dr Mahsa Mehrad and Dr. Abdul Tawfik.
This project will include:
- AI-enabled CMOS Design
- Wide-Bandgap Device Optimization
- Simulation/Modelling-Guided Research
- Developing and optimising wide-bandgap CMOS power devices using GaN and AlGaN materials.
- Analysing simulation and modeling results to uncover relationships between device parameters and circuit performance.
- Applying AI-enabled methods to predict optimal design parameters and guide the co-optimization of pull-up and pull-down devices.
Project description
The transition to energy-efficient technologies, including electric vehicles, renewable energy systems, and smart power grids, requires high-performance power electronic devices capable of operating at high voltages and frequencies with minimal losses. Wide-bandgap semiconductors such as Gallium Nitride (GaN) and Aluminum Gallium Nitride (AlGaN) offer exceptional electrical performance, including high breakdown voltage, fast switching speed, and excellent efficiency. These properties make them ideal candidates for next-generation CMOS power devices. However, designing wide-bandgap CMOS circuits remains a major challenge. One of the key difficulties is that pull-up and pull-down devices must be closely matched to ensure correct logic operation, while other challenges include device reliability, and integration complexity.
This project will develop AI-enabled methods to address these challenges, combining physics-based simulations with machine learning and multi-objective optimization techniques. Machine learning will analyze data to uncover complex relationships electrical parameters and circuit performance.
This project is interdisciplinary, combining semiconductor device physics, electronic engineering, computational modeling, and artificial intelligence. It offers opportunities to publish research in high-impact journals, present at international conferences, and collaborate with leading experimental and industrial partners working on wide-bandgap power electronics.
General admissions criteria
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a master’s degree in electrical engineering, Electronic Engineering, Semiconductor Devices, Computer Science, Materials Science, Physics, or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications.
English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office.
Specific candidate requirements
- A strong academic background in the related field.
- Knowledge of semiconductor device physics, especially wide-bandgap (e.g., GaN, AlGaN).
- Experience with numerical simulation tools is highly desirable.
- Familiarity with electrical characterisation techniques is an advantage.
- Interest or experience in machine learning / AI methods for modelling, optimisation, or data analysis.
- Strong analytical skills and the ability to interpret complex physical phenomena.
- Ability to work independently and as part of a multidisciplinary research team.
- Good written and spoken communication skills for writing reports, publishing research findings, and presenting at conferences.
How to Apply
We’d encourage you to contact Dr Mahsa Mehrad (mahsa.mehrad@port.ac.uk) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, please follow the 'Apply now' link on the Electronic Engineering PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
When applying please quote project code: SEM10430526
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