职位信息

ELECTRONIC AND ELECTRICAL ENGINEERING: FULLY FUNDED SWANSEA UNIVERSITY AND SPTS PHD SCHOLARSHIP: DESIGNING AND DEVELOPING HUMAN IN THE LOOP AI APPROACHES

截止日期：8月28日

入学日期：2023年9月

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Job 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 semiconductor manufacturing firms expect to increase efficacy with digital technologies, with AI (Machine Learning) set to transform the global semiconductor industry over the next decade, through automated inspection, defect recognition and step changes in quality control and yield.

Semiconductor 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 expertise in human-centred AI to develop and deploy state-of-the-art machine learning models that can be applied to the next generation of semiconductor material and devices.

This multidisciplinary project will span lab-based inspection of defects in new semiconductor wafer materials and creating data-driven machine learning models to predict defects and their impact on utility and reliability of the semiconductor devices.

The engineering lab work will comprise of a combination of 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.

The work will pivot through signal processing – analysis of physical devices – to model generation for precision defect prediction.

The PhD will seek to establish state-of-the-art defect prediction in new materials.

This will need to consider creating models from; low volumes of data, methods such as transfer learning (from large volume data on silicon devices); and one-shot learning; or hybrid data-driven combined with analytical modelling techniques.

The predicative models will aim to target material and process-induced defects, mapped using KLA tools, – increasing knowledge of new material device defects, their impacts on utility and reliability and ultimately reducing process costs and improving power devices yields in automotive applications.

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Eligibility

Candidates must hold an undergraduate degree at 2.1 level (or Non-UK equivalent as defined by Swansea University) in Engineering or similar relevant science discipline (Computer Science, Physics).

English Language requirements: If applicable – IELTS 6.5 overall (with at least 5.5 in each individual component) or Swansea recognised equivalent.

Funding

This scholarship covers the full cost of UK tuition fees and an annual stipend of ￡18,622 at UKRI rate.

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职位信息网址

https://www.swansea.ac.uk/postgraduate/scholarships/research/

注：本文引自国外硕博招生

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