Structure dependent resistivity and dielectric characteristics of tantalum oxynitride thin films produced by magnetron sputtering

tThe main purpose of this work is to present and to interpret the change of electrical properties of TaxNyOzthin films, produced by DC reactive magnetron sputtering. Some parameters were varied during deposi-tion: the flow of the reactive gases mixture (N2and O2, with a constant concentration ratio of 17:3); thesubstrate voltage bias (grounded, −50 V or −100 V) and the substrate (glass, (1 0 0) Si or high speed steel).The obtained films exhibit significant differences. The variation of the deposition parameters inducesvariations of the composition, microstructure and morphology. These differences cause variation of theelectrical resistivity essentially correlated with the composition and structural changes. The gradualdecrease of the Ta concentration in the films induces amorphization and causes a raise of the resistivity.The dielectric characteristics of some of the high resistance TaxNyOzfilms were obtained in the sampleswith a capacitor-like design (deposited onto high speed steel, with gold pads deposited on the dielectricTaxNyOzfilms). Some of these films exhibited dielectric constant values higher than those reported forother tantalum based dielectric films.

Automatically estimating iSAX parameters

The Symbolic Aggregate Approximation (iSAX) is widely used in time series data mining. Its popularity arises from the fact that it largely reduces time series size, it is symbolic, allows lower bounding and is space efficient. However, it requires setting two parameters: the symbolic length and alphabet size, which limits the applicability of the technique. The optimal parameter values are highly application dependent. Typically, they are either set to a fixed value or experimentally probed for the best configuration. In this work we propose an approach to automatically estimate iSAX’s parameters. The approach – AutoiSAX – not only discovers the best parameter setting for each time series in the database, but also finds the alphabet size for each iSAX symbol within the same word. It is based on simple and intuitive ideas from time series complexity and statistics. The technique can be smoothly embedded in existing data mining tasks as an efficient sub-routine. We analyze its impact in visualization interpretability, classification accuracy and motif mining. Our contribution aims to make iSAX a more general approach as it evolves towards a parameter-free method.