Use of artificial intelligence techniques for the description of processes in Ni/Al multilayers

Abstract

Knowledge discovery is a novel research area in the field of artificial intelligence. Its aim is to discover empirical laws that govern the behavior of complex systems using measurements of system variables. In this paper a brief description of the goldhorn knowledge discovery system is presented. goldhorn discovers differential equations and has features for handling noisy data, including some digital filters. In the present case, this method was used to describe analytically atomic migration in thin layers. A multilayer structure of nickel and aluminum was deposited on a copper substrate using the triode sputtering system and hollow cathode CVD plasma deposition. The composition of the elements in the deposited layers was determined by Auger electron spectroscopy (AES). The structure was then annealed for different times. After annealing, the samples were analyzed again. The AES data were then analyzed by the goldhorn software package in order to obtain an analytical description of atomic migration as a function of the relative concentration of elements in a layer. The analysis shows that the rate of migration of Al in Ni depends on the relative concentrations of the elements. Different phases appeared to be indicated via the changes in the slope of the curve. Our results show that knowledge discovery is a very useful tool for analyzing complex processes such as atomic migration in multilayer systems.