Deposition of c-axis orientation aluminum nitride films on flexible polymer substrates by reactive direct-current magnetron sputtering

Aluminum nitride (AlN) piezoelectric thin films with c-axis crystal orientation on polymer substrates can potentially be used for development of flexible electronics and lab-on-chip systems. In this study, we investigated the effects of deposition parameters on the crystal structure of AlN thin films on polymer substrates deposited by reactive direct-current magnetron sputtering. The results show that low sputtering pressure as well as optimized N 2/Ar flow ratio and sputtering power is beneficial for AlN (002) orientation and can produce a highly (002) oriented columnar structure on polymer substrates. High sputtering power and low N 2/Ar flow ratio increase the deposition rate. In addition, the thickness of Al underlayer also has a strong influence on the film crystallography. The optimal deposition parameters in our experiments are: deposition pressure 0.38 Pa, N 2/Ar flow ratio 2:3, sputtering power 414 W, and thickness of Al underlayer less than 100 nm. © 2012 Elsevier B.V. All rights reserved.

Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics

Simulation of materials at the atomistic level is an important tool in studying microscopic structure and processes. The atomic interactions necessary for the simulation are correctly described by Quantum Mechanics. However, the computational resources required to solve the quantum mechanical equations limits the use of Quantum Mechanics at most to a few hundreds of atoms and only to a small fraction of the available configurational space. This thesis presents the results of my research on the development of a new interatomic potential generation scheme, which we refer to as Gaussian Approximation Potentials. In our framework, the quantum mechanical potential energy surface is interpolated between a set of predetermined values at different points in atomic configurational space by a non-linear, non-parametric regression method, the Gaussian Process. To perform the fitting, we represent the atomic environments by the bispectrum, which is invariant to permutations of the atoms in the neighbourhood and to global rotations. The result is a general scheme, that allows one to generate interatomic potentials based on arbitrary quantum mechanical data. We built a series of Gaussian Approximation Potentials using data obtained from Density Functional Theory and tested the capabilities of the method. We showed that our models reproduce the quantum mechanical potential energy surface remarkably well for the group IV semiconductors, iron and gallium nitride. Our potentials, while maintaining quantum mechanical accuracy, are several orders of magnitude faster than Quantum Mechanical methods.