Defined etching of carbon-diamond films on silicon using an oxygen plasma with titanium masking

A technique for pattern transfer onto carbon-diamond films deposited by radio-frequency plasma-enhanced chemical vapour deposition is reported. Such a technique involves standard photolithography processes and reactive ion etching by oxygen and is compatible with present day microelectronic technology. The patterns transferred are well defined with very good resolution. © 1992.

Encapsluation of Co and Pd multi-metal nanowires inside multiwalled carbon nanotubes by microwave plasma chemical vapor deposition

We report the synthesis of multiwalled carbon nanotubes (MWCNTs) encapsulated with Co/Pd magnetic and nonmagnetic multi-metal nanowires using Co and Pd thin-layers deposited on Si substrate by microwave plasma enhanced chemical vapor deposition using a bias-enhanced growth method. Detailed structural and compositional investigations of these metal nanowires inside MWCNTs were carried out by scanning electron microscopy and transmission electron microscopy to elucidate the growth mechanisms. Energy dispersive X-ray spectroscopy revealed that MWCNTs were encapsulated with Co and Pd nanowires, separately, at the tube top and the bottom of Co nanowire, respectively. The face-centered-cubic (fcc) structure of Co nanowires was confirmed by a selected area diffraction pattern. We proposed a fruitful description for the encapsulating mechanisms of both Co and Pd multi-metal nanowires. © 2006 Elsevier B.V. All rights reserved.

Gaussian mixture modeling with Gaussian process latent variable models

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.