Tetrahedral amorphous carbon-silicon heterojunction band energy offsets

Non-hydrogenated tetrahedral amorphous carbon (ta-C) has shown superior field emission characteristics. The understanding of the emission mechanism has been hindered by the lack of any directly measured data on the band offsets between ta-C and Si. In this paper results from direct in situ X-ray photoemission spectroscopy (XPS) measurements of the band-offset between ta-C and Si are reported. The measurements were carried out using a filtered cathodic vacuum arc (FCVA) deposition system attached directly to an ultra-high vacuum (UHV) XPS chamber via a load lock chamber. Repeated XPS measurements were carried out after monolayer depositions on in situ cleaned Si substrates. The total film thickness for each set of measurements was approximately 5 nm. Analysis of the data from undoped ta-C on n and p Si show the unexpected result that the conduction band barrier between Si and ta-C remains around 1.0 eV, but that the valence band barrier changes from 0.7 to 0.0 eV. The band line up derived from these barriers suggests that the Fermi level in the ta-C lies 0.3 eV above the valence band on both p and n+Si. The heterojunction barriers when ta-C is doped with nitrogen are also presented. The implications of the heterojunction energy barrier heights for field emission from ta-C are discussed.

On-line learning of mutually orthogonal subspaces for face recognition by image sets.

We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge.