The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. Here we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.

Over the last few years, a new mode for imaging in the scanning transmission electron microscope (STEM) has gained attention as it permits the direct visualization of sample conductivity and electrical connectivity. When the electron beam (e-beam) is focused on the sample in the STEM, secondary electrons (SEs) are generated. If the sample is conductive and electrically connected to an amplifier, the SE current can be measured as a function of the e-beam position. This scenario is similar to the better-known scanning electron microscopy-based technique, electron beam-induced current imaging, except that the signal in the STEM is generated by the emission of SEs, hence the name secondary electron e-beam-induced current (SEEBIC), and in this case, the current flows in the opposite direction. Here, we provide a brief review of recent work in this area, examine the various contrast generation mechanisms associated with SEEBIC, and illustrate its use for the characterization of graphene nanoribbon devices.

Atomically resolved imaging of materials enabled by the advent of aberration-corrected scanning transmission electron microscopy (STEM) has become a mainstay of modern materials science. However, much of the wealth of quantitative information contained in the fine details of atomic structure or spectra remains largely unexplored. In this article, we discuss new opportunities enabled by physics-informed big data and machine learning technologies to extract physical information from static and dynamic STEM images, ranging from statistical thermodynamics of alloys to kinetics of solid-state reactions at a single defect level. The synergy of deep-learning image analytics and real-time feedback further allows harnessing beam-induced atomic and bond dynamics to enable direct atom-by-atom fabrication. Examples of direct atomic motion over mesoscopic distances, engineered doping at selected lattice sites, and assembly of multiatomic structures are reviewed. These advances position the scanning transmission electron microscope to transition from a mere imaging tool toward a complete nanoscale laboratory for exploring electronic, phonon, and quantum phenomena in atomically engineered structures.