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We report on the automatic alignment of a transmission electron microscope equipped with an orbital angular momentum sorter using a convolutional neural network. The neural network is able to control all relevant parameters of both the electron-optical setup of the microscope and the external voltage source of the sorter without input from the user. It can compensate for mechanical and optical misalignments of the sorter, in order to optimize its spectral resolution. The alignment is completed over a few frames and can be kept stable by making use of the fast fitting time of the neural network.
The reduction of the active cell size to the nanoscale is crucial for the improvement of the phase change memory devices (PCM) based on Ge-Sb-Te (GST) alloys. The self-assembly of Au catalyzed Ge1Sb2Te4 (GST-124) nanowires (NWs) has been achieved by metal organic chemical vapor deposition. The atomic arrangement of the NWs has been investigated and the stacking sequence has been identified, by combining the direct observation by High Angle Annular Dark Field (HAADF) imaging and simulations. It has been assessed that Ge and Sb atoms can randomly occupy the same sites in the crystal lattice, despite the adverse predictions of the theoretical models elaborated for the bulk material.
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