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
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.