A machine learning framework for scanning electron diffraction microscopy

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Electron microscopy provides atomic resolution images and information from a vast range of materials. The most common electron microscopes scan a focused electron beam over a sample and record indicators of the beam - sample interaction to construct images. Many thousands of these instruments are used globally and are essential for physical and biological sciences, and nano-technology applications. Surprisingly though, these microscopes disregard vast amounts of information on how the beam interacts with the sample on passing through or scattering from the sample. This follows from the limitations of current generation electron detectors. Recently though, these limits have been shattered by the realization of new pixelated detectors that allow formerly discarded diffraction data to be recorded at rates greater than 1000 beam positions per second. This new technique, termed scanning electron diffraction microscopy, gives a step change in the detail and speed of the microscope output from GB to TB of data per day in typical usage. This thousandfold data volume increase leads to remarkable effective resolution improvements and presents exciting possibilities for new characterization methods, which will be developed in this project. However, the increased information output necessitates that new strategies are developed to process, analyse, store and share these results to achieve the maximum impact and potential of this technique. This project will meet this need by applying the power of machine learning to the data and developing an internationally accessible computational framework. This will allow lessons learnt from the research and trained networks to be easily and efficiently shared, reused and refined. The project will apply the framework to yield new insights into materials sciences in Canada, with first applications in defect identification in electronic devices and characterizing polymer and electron beam sensitive devices such as energy storage devices, photovoltaics and nanoparticles for medical diagnostics and treatments. The project will form a Canadian hub for expertise in this rapidly expanding area of electron microscopy, and will strengthen the partner's research, development and product base in Canada.