Science and Engineering: Fully Funded PhD Scholarship: Metrology and Process Integration for defect mapping of next generation semiconductor materials and devices

Funding provider: Swansea University's Faculty of Science and Engineering, Match funded by SPTS (a KLA Company)

Subject areas: Computer Science / Electrical Engineering / Physics

Project start date: 

• 1 July 2022 (Enrolment open from mid-June)

Aligned programme of study: PhD in Electronic and Electrical Engineering

Mode of study: Full or Part-time study is possible

Project description:

The automotive sector is driving uptake of semiconductor devices – the rapidly expanding automotive semiconductor market (worth $35 billion in 2020) is powered by growth of Electric, Hybrid and Autonomous Vehicles sectors.

Defect-free semiconductor device product yields of >90% will be required to enable this revolution. 98% of firms expect to increase efficacy with digital technologies, with AI set to transform the global semiconductor industry over the next decade, through automated inspection, defect recognition and step changes in quality control and yield.

Device manufacturing comprises processes including wafer production, photolithography, insulator growth, deposition, etching and metal deposition). Each process step has variables, which can lead to defects.

KLA is the world’s leading metrology company – KLA’s metrology systems address chip and substrate manufacturing applications, including verification of design-manufacturability, new process characterization and high-volume manufacturing process monitoring. KLA’s precise measurement of pattern dimensions, film thicknesses, layer-to-layer alignment, pattern placement, surface topography and electro-optical properties, allow chip manufacturers to maintain tight process control for improved device performance and yield. SPTS Technology (a KLA company) is a manufacturer of etch and deposition equipment for the semiconductor industry. 

This project will combine wafer production and metrology processes for next generation semiconductor materials and devices – mapping out the materials defects and process-induced defects for Silicon Carbide and Gallium Nitride devices and wafers.

Devices process optimisation and state-of-the-art defect prediction, targeting material and process-induced defects, mapped using KLA tools, will be used to develop models using data-driven sequence-based deep learning with human-in-the-loop Reinforcement learning.

This sequence-based architecture will allow early identification of wafer and device defects – reducing process costs and improving power devices yields in automotive applications.

Eligibility

Candidates must hold an undergraduate degree in Computer Science, Physics or Electrical Engineering at 2.1 level (or Non-UK equivalent as defined by Swansea University). See - Country-specific Information for European Applicants 2019 and Country-specific Information for International Applicants 2019.

English Language requirements: If applicable – IELTS 6.5 overall (with at least 5.5 in each individual component) or Swansea recognised equivalent. Details on the Swansea University English Language entry policy can be found here.

Due to funding restrictions, this scholarship is open to UK and EU based students only. EU Students must be eligible to receive the EU Transitional Bursary Scheme.