Progress in the understanding of Ni silicide formation for advanced MOS structures

Metallic silicides have been used as contact materials on source/drain and gate in metal-oxide semiconductor (MOS) structure for 40 years. Since the 65 nm technology node, NiSi is the preferred material for contact in microelectronic due to low resistivity, low thermal budget, and low Si consumption. Ni(Pt)Si with 10 at.% Pt is currently employed in recent technologies since Pt allows to stabilize NiSi at high temperature. The presence of Pt and the very low thickness (<10 nm) needed for the device contacts bring new concerns for actual devices. In this work, in situ techniques [X-ray diffraction (XRD), X-ray reflectivity (XRR), sheet resistance, differential scanning calorimetry (DSC)] were combined with atom probe tomography (APT) to study the formation mechanisms as well as the redistribution of dopants and alloy elements (Pt, Pd.) during the silicide formation. Phenomena like nucleation, lateral growth, interfacial reaction, diffusion, precipitation, and transient phase formation are investigated. The effect of alloy elements (Pt, Pd.) and dopants (As, B.) as well as stress and defects induced by the confinement in devices on the silicide formation mechanism and alloying element redistribution is examined. In particular APT has been performed for the three-dimensional (3D) analysis of MOSFET at the atomic scale. The advances in the understanding of the mechanisms of formation and redistribution are discussed. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Structured Model of Video Reproduces Primary Visual Cortical Organisation

The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.