Abstract
We present a novel concept for 3D model-based geometry reconstruction (MBGR) of semiconductor quantum dots (QDs) form imaging of bulk-like samples (thickness 100–300 nm) by transmission electron microscopy (TEM). The approach includes an appropriate model for the QD configuration in real space, a database of simulated TEM images and a statistical procedure for the estimation of QD properties and classification of QD types based on machine learning techniques. For the numerical simulation of TEM images we use an elasticity solver to obtain the strain profile, which enters a solver for the Darwin-Howie-Whelan equations, describing propagation of the electron wave through the sample.
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