EPSRC iCASE Project: Optimal Sensing of Multi-dimensional Datasets in Scanning Electron Microscopy (SEM)

Description

This project is a fully funded (£118,483) over 4 years by EPSRC and Oxford Instruments to cover all stipend, fees and incidental research costs. This PhD project is experimental, and involves working with an extensive research group to determine the optimal approach to using artificial intelligence (AI) for the characterisation of advanced engineering, biomedical and medical materials and systems. Familiarity with AI methods is not essential as collaborating partners will provide the necessary expertise and training for the PhD candidate.  The experimental methods determined in this PhD project have wide applicability, but will focus initially on the use of scanning electron microscopes (SEMs).

SEM is one of the core imaging/characterisation methods used in the development of critical new materials technologies. As SEMs have advanced over the last 30 years, in many cases the achievable spatial resolution and chemical sensitivity is no longer limited by the microscope, but by the ability of the sample being studied to withstand the electron beam dose during the experiment. In addition, as the demands of materials science and biology have moved to analysing ever larger areas of sample with higher precision, temporal resolution and the throughput of samples now becomes the limiting factor in performing many experiments. The optimal approach to imaging for state-of-the-art samples is now to determine the minimum number of pixels and the minimum electron dose per pixel, necessary to achieve the highest resolution and sensitivity. 

Recent developments in compressive sensing (CS) have offered a new avenue to achieving this optimum resolution and sensitivity in SEM experiments. The experiment is now performed by acquiring a small sub-set of imaging pixels randomly distributed over the area of the analysis, and then Inpainting algorithms (a form of artificial intelligence (AI)) are used to fill in the missing information. By using this approach, the number of pixels in any image can be reduced by a factor of ~100-1000, vastly improving the temporal resolution and throughput while at the same time significantly reducing the electron dose to the sample.   As an SEM can generate multiple signals simultaneously (Secondary electrons, backscattered electrons, X-rays, channelling patterns, etc) the signal/noise of each being dependent on differences in both the physics of the interaction and the chemistry of the sample, the goal of this PhD project is for the student to perform experiments on a state-of-the-art FIB-SEM at the University of Liverpool to determine how to use these different signals within the AI/CS sub-sampling environment to optimise the analysis for a range of different samples. The aim is to establish the experimental parameters that enable the same SEM to function differently and optimally for both the most beam sensitive biological sample and the most structurally diverse engineering sample. 

This EPSRC ICASE project will incorporate strong interactions between Oxford Instruments, where the student will become one of five current ICASE students and the Albert Crewe Centre for Electron Microscopy, where four other students are currently working on algorithm development and applications of sub-sampling for various imaging and spectroscopic applications in Transmission Electron Microscopy (TEM) and Scanning Transmission Electron Microscopy (STEM). 

For any enquiries, please contact Professor Nigel D. Browning on: nigel.browning@liverpool.ac.uk

To apply for this opportunity, please visit: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/ and click on the 'Ready to apply? Apply online' button.