Agentic AI/Plasma Developer – KTP Associate

An exciting opportunity has become available to work on a 30-month Knowledge Transfer Partnership (KTP) between the University of Exeter and Oxford Instrument Plasma Technology Limited (OIPT). The KTP will apply advanced agentic-based Artificial Intelligence (AI) techniques to the modelling of semiconductor processing systems, addressing productivity challenges in High Volume Manufacture sector. Specifically, this enables a timely response to customer challenges and market trends in an environment that is increasingly brittle, anxious, non-linear, and incomprehensible (BANI).

The successful candidate will be employed by the University of Exeter and will spend the majority of their time working at OIPT in Severn Beach, Bristol with occasional visits to the University of Exeter.

The project applies agentic-AI to the modelling of plasma etching and deposition and, in doing so, will embed skills and understanding within OIPT to further enhance their products. OIPT is a leading provider of plasma etch and deposition equipment to the global semiconductor manufacturing industry. Process modelling, particularly optimising gas flow through Computational Fluid Dynamics (CFD) is a critical component of both hardware design and the development of customer specific processes. Supporting the High-Volume Manufacturing (HVM) segment of the semiconductor market requires constant evolution in hardware and processes as new applications are launched and scaled. Conventional CFD workflows cannot keep pace with industry demands and hence new approaches and techniques are necessary. Agentic-AI, the application of AI principles to a goal-based problem, promises a route towards delivering the step increase in productivity demanded by the industry.

The KTP Associate will be embedded within Oxford Instrument Plasma Technology Ltd to deliver the project objectives by:

• Developing and implementing agentic-AI workflows for the modelling of plasma etching and deposition systems, with a particular focus on CFD-based process modelling.
• Integrating existing simulation tools, engineering data and AI methods into reliable workflows that can be used by OIPT engineers.
• Applying and testing these workflows on representative OIPT hardware and process-development problems and assessing their performance against existing modelling approaches.
• Working closely with OIPT engineers and University researchers to improve the accuracy, robustness and practical usability of the developed methods.
• Using the developed workflows to support hardware design, process optimisation and faster response to customer-specific engineering questions.
• Documenting the methods and transferring the resulting knowledge and capability into OIPT through training, guidance and day-to-day collaboration.

This project enables and accelerates OIPT’s current innovation processes and products to accelerate its strategic goals.