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.

Bayesian correlated clustering to integrate multiple datasets.

MOTIVATION: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct-but often complementary-information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. RESULTS: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI's performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques-as well as to non-integrative approaches-demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods.

Complementary Phone Error training

This paper introduces a novel method for the training of a complementary acoustic model with respect to set of given acoustic models. The method is based upon an extension of the Minimum Phone Error (MPE) criterion and aims at producing a model that makes complementary phone errors to those already trained. The technique is therefore called Complementary Phone Error (CPE) training. The method is evaluated using an Arabic large vocabulary continuous speech recognition task. Reductions in word error rate (WER) after combination with a CPE-trained system were obtained with up to 0.7% absolute for a system trained on 172 hours of acoustic data and up to 0.2% absolute for the final system trained on nearly 2000 hours of Arabic data.

The production of radionuclides for nuclear medicine from a compact, low-energy accelerator system.

The field of nuclear medicine is reliant on radionuclides for medical imaging procedures and radioimmunotherapy (RIT). The recent shut-downs of key radionuclide producers have highlighted the fragility of the current radionuclide supply network, however. To ensure that nuclear medicine can continue to grow, adding new diagnostic and therapy options to healthcare, novel and reliable production methods are required. Siemens are developing a low-energy, high-current - up to 10MeV and 1mA respectively - accelerator. The capability of this low-cost, compact system for radionuclide production, for use in nuclear medicine procedures, has been considered.