title: Clustering and nearest neighbour distances in atom-probe tomography.
abstract: The measurement of chemical composition of tiny clusters is a tricky problem in both atom-probe tomography experiments and atomic simulations. A new approach relying on the distribution of the first nearest neighbour (1NN) distances between solute atoms in the 3D space composed of A and B atoms was developed. This new approach, the 1NN method, is shown to be an elegant way to get the composition of tiny B-enriched clusters embedded in a random AB solid solution. The theoretical statistical distributions of first neighbour distances P(r) for both random solid solution and solute-enriched clusters finely dispersed in a depleted matrix are established. It is shown that the most probable distance of P(r) gives directly the phase composition. Applications of this model to both one-phase SiGe alloy and boron-doped silicon containing small clusters indicate that this new approach is quite reliable.
authors: Université de Rouen, GPM, UMR CNRS 6634 BP 12, Avenue de l'Université, Saint Etienne de Rouvray, France. thomas.philippe@etu.univ-rouen.fr
authors: T
authors: T
authors: Philippe
authors: F
authors: F
authors: De Geuser
authors: S
authors: S
authors: Duguay
authors: W
authors: W
authors: Lefebvre
authors: O
authors: O
authors: Cojocaru-Mirédin
authors: G
authors: G
authors: Da Costa
authors: D
authors: D
authors: Blavette