A soft touch with electron beams: Digging out structural information of nanomaterials with advanced scanning low energy electron microscopy coupled with deep learning.

Nanostructured materials continue to find applications in various electronic and sensing devices, chromatography, separations, drug delivery, renewable energy, and catalysis. While major advancements on the synthesis and characterization of these materials have already been made, getting information about their structures at sub-nanometer resolution remains challenging. It is also unfortunate to find that many emerging or already available powerful analytical methods take time to be fully adopted for characterization of various nanomaterials. The scanning low energy electron microscopy (SLEEM) is a good example to this. In this report, we show how clearer structural and surface information at nanoscale can be obtained by SLEEM, coupled with deep learning. The method is demonstrated using Au nanoparticles-loaded mesoporous silica as a model system. Moreover, unlike conventional scanning electron microscopy (SEM), SLEEM does not require the samples to be coated with conductive films for analysis; thus, not only it is convenient to use but it also does not give artifacts. The results further reveal that SLEEM and deep learning can serve as great tools to analyze materials at nanoscale well. The biggest advantage of the presented method is its availability, as most modern SEMs are able to operate at low energies and deep learning methods are already being widely used in many fields.

Quantification of STEM Images in High Resolution SEM for Segmented and Pixelated Detectors.

The segmented semiconductor detectors for transmitted electrons in ultrahigh resolution scanning electron microscopes allow observing samples in various imaging modes. Typically, two standard modes of objective lens, with and without a magnetic field, differ by their resolution. If the beam deceleration mode is selected, then an electrostatic field around the sample is added. The trajectories of transmitted electrons are influenced by the fields below the sample. The goal of this paper is a quantification of measured images and theoretical study of the capability of the detector to collect signal electrons by its individual segments. Comparison of measured and ray-traced simulated data were difficult in the past. This motivated us to present a new method that enables better comparison of the two datasets at the cost of additional measurements, so-called calibration curves. Furthermore, we also analyze the measurements acquired using 2D pixel array detector (PAD) that provide a more detailed angular profile. We demonstrate that the radial profiles of STEM and/or 2D-PAD data are sensitive to material composition. Moreover, scattering processes are affected by thickness of the sample as well. Hence, comparing the two experimental and simulation data can help to estimate composition or the thickness of the sample.

In-Lens Band-Pass Filter for Secondary Electrons in Ultrahigh Resolution SEM.

Scanning electron microscopes come equipped with different types of detectors for the collection of signal electrons emitted from samples. In-lens detection systems mostly consist of several auxiliary electrodes that help electrons to travel in a direction towards the detector. This paper aims to show that a through-the-lens detector in a commercial electron microscope Magellan 400 FEG can, under specific conditions, work as an energy band-pass filter of secondary electrons that are excited by the primary beam electrons. The band-pass filter properties verify extensive simulations of secondary and backscattered electrons in a precision 3D model of a microscope. A unique test sample demonstrates the effects of the band-pass filter on final image and contrast with chromium and silver stripes on a silicon substrate, manufactured by a combination of e-beam lithography, wet etching, and lift-off technique. The ray tracing of signal electrons in a detector model predicate that the through-the-lens detector works as a band-pass filter of the secondary electrons with an energy window of about 3 eV. By moving the energy window along the secondary electron energy spectrum curve of the analyzed material, we select the energy of the secondary electrons to be detected. Energy filtration brings a change in contrast in the image as well as displaying details that are not otherwise visible.