Complementary metal-oxide-semiconductor-compatible and self-aligned catalyst formation for carbon nanotube synthesis and interconnect fabrication

We have for the first time developed a self-aligned metal catalyst formation process using fully CMOS (complementary metal-oxide-semiconductor) compatible materials and techniques, for the synthesis of aligned carbon nanotubes (CNTs). By employing an electrically conductive cobalt disilicide (CoSi 2) layer as the starting material, a reactive ion etch (RIE) treatment and a hydrogen reduction step are used to transform the CoSi 2 surface into cobalt (Co) nanoparticles that are active to catalyze aligned CNT growth. Ohmic contacts between the conductive substrate and the CNTs are obtained. The process developed in this study can be applied to form metal nanoparticles in regions that cannot be patterned using conventional catalyst deposition methods, for example at the bottom of deep holes or on vertical surfaces. This catalyst formation method is crucially important for the fabrication of vertical and horizontal interconnect devices based on CNTs. © 2012 American Institute of Physics.

One step self-aligned multilayer patterning process for the fabrication of organic complementary circuits in combination with inkjet printing

Recent development of solution processable organic semiconductors delineates the emergence of a new generation of air-stable, high performance p- and n-type materials. This makes it indeed possible for printed organic complementary circuits (CMOS) to be used in real applications. The main technical bottleneck for organic CMOS to be adopted as the next generation organic integrated circuit is how to deposit and pattern both p- and n-type semiconductor materials with high resolutions at the same time. It represents a significant technical challenge, especially if it can be done for multiple layers without mask alignment. In this paper, we propose a one-step self-aligned fabrication process which allows the deposition and high resolution patterning of functional layers for both p- and n-channel thin film transistors (TFTs) simultaneously. All the dimensional information of the device components is featured on a single imprinting stamp, and the TFT-channel geometry, electrodes with different work functions, p- and n-type semiconductors and effective gate dimensions can all be accurately defined by one-step imprinting and the subsequent pattern transfer process. As an example, we have demonstrated an organic complementary inverter fabricated by 3D imprinting in combination with inkjet printing and the measured electrical characteristics have validated the feasibility of the novel technique. © 2012 Elsevier B.V. All rights reserved.

Shape-Based Retrieval of Construction Site Photographs

Digital photographs of construction site activities are gradually replacing their traditional paper based counterparts. Existing digital imaging technologies in hardware and software make it easy for site engineers to take numerous photographs of “interesting” processes and activities on a daily basis. The resulting photographic data are evidence of the “as-built” project, and can therefore be used in a number of project life cycle tasks. However, the task of retrieving the relevant photographs needed in these tasks is often burdened by the sheer volume of photographs accumulating in project databases over time and the numerous objects present in each photograph. To solve this problem, the writers have recently developed a number of complementary techniques that can automatically classify and retrieve construction site images according to a variety of criteria (materials, time, date, location, etc.). This paper presents a novel complementary technique that can automatically identify linear (i.e., beam, column) and nonlinear (i.e., wall, slab) construction objects within the image content and use that information to enhance the performance of the writers’ existing construction site image retrieval approach.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.

A Grassmann-Rayleigh quotient iteration for computing invariant subspaces

The classical Rayleigh quotient iteration (RQI) allows one to compute a one-dimensional invariant subspace of a symmetric matrix A. Here we propose a generalization of the RQI which computes a p-dimensional invariant subspace of A. Cubic convergence is preserved and the cost per iteration is low compared to other methods proposed in the literature.