Samsung Advanced Institute of Technology

Energy-dispersive X-ray spectroscopy (EDX) is often performed simultaneously with high angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) for nanoscale physico-chemical analysis. However, high quality STEM-EDX tomographic imaging is still challenging due to the fundamental limitations of the EDX analysis technique, such as sample degradation with prolonged scan time, low probability of an X-ray generation, etc. To address this, here we propose a novel unsupervised deep learning method for high quality three-dimensional (3D) EDX tomography of core-shell nanocrystals which are normally permanently damaged under prolonged electron beam exposure. In particular, the proposed deep learning STEM-EDX tomography method was used to reconstruct Au nanoparticles and InP/ZnSe/ZnS core-shell quantum dots, and the reconstruction results confirm the accuracy of the proposed methods. Furthermore, the shape and thickness uniformity of the reconstructed ZnSe/ZnS shell has been proven to be closely correlated with optical properties of the quantum dots, including quantum efficiency and chemical stability.