Using the Physics of Electron Beam Interactions to Determine Optimal Sampling and Image Reconstruction Strategies for High Resolution STEM

Images from scanning transmission electron microscopes (STEM) are used to routinely quantify the atomic scale structure, composition, chemistry, bonding, electron/phonon distribution and optical properties of nanostructures, interfaces and defects in many materials systems. However, quantitative and reproducible observations for many materials of current technological importance is limited by electron beam damage destroying the sample before the highest resolution information is obtained. The aim for broadening STEM applications to a wider range of samples and processes is therefore now to focus on more efficient use of the electron dose (i.e. the number of electrons per unit area) that is supplied to the sample. In practice, this is achieved by modelling the physical interactions between the beam and the sample and using this information to minimize the experimental dose, dose rate and dose overlap for any image. These minimized physical interaction conditions result in two main approaches for dose fractionation and optimizing the data content per unit dose – reducing the number of pixels being sampled in scanning mode (STEM), or increasing the speed of individual images in projection mode (TEM). For the case of the STEM, inpainting /machine learning methods allow data to be automatically recorded in a faster compressed form with less material damage. For the TEM, a similar increase in speed and damage reduction can be achieved by implementing compressive sensing/machine learning – this reduces the acquisition time and therefore the dose per image. In this chapter, the basic approach to understanding physical interactions between the electron beam and the sample and how this leads to the integration of sub-sampling/inpainting/compressive sensing and machine learning into the imaging hardware will be described and the potential for future developments will be discussed.