Accuracy and precision of thickness determination from position-averaged convergent beam electron diffraction patterns using a single-parameter metric

Cryogenic 4D-STEM analysis of an amorphous-crystalline polymer blend: combined nanocrystalline and amorphous phase mapping

Understanding and visualizing the heterogeneous structure of immiscible semicrystalline polymer systems is critical for optimizing their morphology and microstructure. We demonstrate a cryogenic 4D-STEM technique using a combination of amorphous radial profile mapping and correlative crystalline growth processing methods to map both the crystalline and amorphous phase distribution in an isotactic polypropylene (iPP)/ethylene-octene copolymer (EO) multilayer film with 5-nm step size. The resulting map shows a very sharp interface between the amorphous iPP and EO with no preferential crystalline structure near or at the interface, reinforcing the expected incompatibility and immiscibility of iPP and EO, which is a short-chain branched polyethylene. This technique provides a method for direct observation of interfacial structure in an unstained semicrystalline complex multicomponent system with a single cryogenic 4D-STEM dataset.

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Cryogenic 4D-STEM can simultaneously map amorphous and crystalline structure

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Direct observation of beam-sensitive polymers reveals nanostructure near interfaces

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Cryogenic 4D-STEM enables mapping of chemically/structurally similar polymers in blend

Local thickness and composition measurements from scanning convergent-beam electron diffraction of a binary non-crystalline material obtained by a pixelated detector

The nanometer-scale spatial distributions of local thickness or composition of noncrystalline materials are generally measured by spectroscopy with scanning transmission electron microscopy (STEM). Since spectroscopy requires a high electron dose and causes irradiation damage, alternative non-spectroscopic methods are required to measure the local thickness or composition of electron-sensitive noncrystalline materials. Here, we focus on the radial distribution function (RDF) of the electron diffraction of non-crystalline materials. We confirm that the RDF of the electron diffraction obtained by simulation contains information on the thickness and composition. Next, we demonstrate the determination of both thickness and composition from experimentally obtained RDFs. Although some constraints are required, we determine the local thickness and composition of a BaO-SiO2 glass sample by comparing the RDFs of diffraction measured by a high-speed pixelated detector with those of the simulated diffractions. Collaterally, this determination method can improve the quality of STEM images.

Atomic resolution convergent beam electron diffraction analysis using convolutional neural networks

Two types of convolutional neural network (CNN) models, a discrete classification network and a continuous regression network, were trained to determine local sample thickness from convergent beam diffraction (CBED) patterns of SrTiO₃ collected in a scanning transmission electron microscope (STEM) at atomic column resolution. Acquisition of atomic resolution CBED patterns for this purpose requires careful balancing of CBED feature size in pixels, acquisition speed, and detector dynamic range. The training datasets were derived from multislice simulations, which must be convolved with incoherent source broadening. Sample thicknesses were also determined using quantitative high-angle annular dark-field (HAADF) STEM images acquired simultaneously. The regression CNN performed well on sample thinner than 35 nm, with 70% of the CNN results within 1 nm of HAADF thickness, and 1.0 nm overall root mean square error between the two measurements. The classification CNN was trained for a thicknesses up to 100 nm and yielded 66% of CNN results within one classification increment of 2 nm of HAADF thickness. Our approach depends on methods from computer vision including transfer learning and image augmentation.