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.