Deposition of diamond-like carbon films by photon-enhanced chemical vapour deposition of methane using a windowless hydrogen lamp

Thin films of diamond-like carbon (DLC) have been deposited using a novel photon-enhanced chemical vapour deposition (photo-CVD) method. This low energy method may be a way to produce better interfaces in electronic devices by reducing damage due to ion bombardment. Methane requires high energy photons for photolysis to take place and these are not transmitted in most photo-CVD methods owing to the presence of a window between the lamp and the deposition environment. In our photo-CVD system there is no window and all the high energy photons are transmitted into the reaction gas. Initial work has proved promising and this paper presents recent results. Films have been characterized by measuring electron energy loss spectra, by ellipsometry and by fabricating and testing diode structures. Results indicate that the films are of a largely amorphous nature and are semiconducting. Diode structures have on/off current ratios of up to 106.

Nanostructured carbon films from supersonic cluster beam deposition: Structure and morphology

Nanostructured carbon thin films have been grown by deposition of cluster beams produced by a supersonic expansion. Due to separation effects typical of supersonic beams, films with different nanostructures can be grown by the simple intercepting of different regions of the cluster beam with a substrate. Films show a low-density porous structure, which has been characterized by Raman and Brillouin spectroscopy. Film morphology suggests that growth processes are similar to those occurring in a ballistic deposition regime.

Characterization of defect removal in hydrogenated and deuterated amorphous silicon thin film transistors

Thin film transistors (TFTs) utilizing an hydrogenated amorphous silicon (a-Si:H) channel layer exhibit a shift in the threshold voltage with time under the application of a gate bias voltage due to the creation of metastable defects. These defects are removed by annealing the device with zero gate bias applied. The defect removal process can be characterized by a thermalization energy which is, in turn, dependent upon an attempt-to-escape frequency for defect removal. The threshold voltage of both hydrogenated and deuterated amorphous silicon (a-Si:D) TFTs has been measured as a function of annealing time and temperature. Using a molecular dynamics simulation of hydrogen and deuterium in a silicon network in the H2 * configuration, it is shown that the experimental results are consistent with an attempt-to-escape frequency of (4.4 ± 0.3) × 1013 Hz and (5.7 ± 0.3) × 1013 Hz for a-Si:H and a-Si:D respectively which is attributed to the oscillation of the Si-H and Si-D bonds. Using this approach, it becomes possible to describe defect removal in hydrogenated and deuterated material by the thermalization energies of (1.552 ± 0.003) eV and (1.559 ± 0.003) eV respectively. This correlates with the energy per atom of the Si-H and Si-D bonds. © 2006 Elsevier B.V. All rights reserved.

Cluster analysis of heterogeneous rank data

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.

Warped Mixtures for Nonparametric Cluster Shapes

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.