Optimal Sensing of Multi-Dimensional Datasets in Scanning Electron Microscopy (SEM)

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Scanning electron microscopy (SEM) is one of the core imaging/characterisation methods used in the development of critical technologies in both the engineering and medical sciences. 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. In both of these cases, the optimal approach to imaging for state-of-the-art samples is to determine the minimum number of pixels and the minimum electron dose per pixel, necessary to achieve the highest resolution and sensitivity in each experiment. 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. The key question in achieving the ultimate level of improvement is determined by the redundancy of information in the sample - is it crystalline or amorphous, single phase or multi-phase, single crystal or polycrystalline, etc. Incorporating known physical information into the inpainting reconstruction, i.e. physics/science based machine learning, in principle permits an SEM to "learn" the best approach to each 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/ML sub-sampling environment to optimise the analysis for a range of different samples. The aim is to establish the learning 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. The PhD student will also become proficient in the application of AI/ML algorithms with the goal of incorporating them routinely into the most advanced characterisation methods.