$tem diffraction pattern analysis software$

STEM experiments have successfully measured the 2D position of atomic columns with picometer-precision 4, measured the vibrational spectra of single-atom defects 5, mapped solid-liquid interfaces in lithium-metal batteries 6, and determined the 3D position and chemical species of each atom in a nanoparticle 7. A STEM experiment focuses an electron beam on to a sample, with the probe dimensions ranging from tens of nanometers down to the atomic scale, which is made possible by hardware aberration correction 2, 3. Scanning transmission electron microscopy (STEM) has emerged as one of the primary nanoscale materials characterization tools 1. Our code, models, and training library are open-source and may be adapted to different diffraction measurement problems. We evaluated FCU-Net against simulated and experimental datasets, where it substantially outperforms conventional analysis methods. FCU-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures, orientations, thicknesses, and microscope parameters, which are augmented with experimental artifacts.

$tem diffraction pattern analysis software$

We implement a Fourier space, complex-valued deep-neural network, FCU-Net, to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images. Deep-learning methods have the potential to invert these complex signals, but require a large number of training examples. Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions, but this technique is limited when the electron beam undergoes multiple scattering. A fast, robust pipeline for strain mapping of crystalline materials is important for many technological applications.

$tem diffraction pattern analysis software$

Tem diffraction pattern analysis software