Hot-carrier reliability of 20V MOS transistors in 0.13 μm CMOS technology

This paper presents results of reliability investigation of 20 V N-Drift MOS transistor in 0.13 μm CMOS technology. Due to high performances required for CMOS applications, adding high voltage devices becomes a big challenge to guarantee the reliability criteria. In this context, new reliability approaches are needed. Safe Operating Area are defined for switch, V_ds limited and V_gs limited applications in order to improve circuit designs. For V_ds limited applications, deep doping dose effects in drift area are investigated in correlation to lifetime evaluations based on device parameter shifts under hot carrier stressing. To further determine the amount and locations of hot carriers injections, accurate 2D technological and electrical simulations are performed and permit to select the best compromise between performance and reliability for N-Drift MOS transistor.     

Some perspectives on the analysis and control of complementarity systems

This paper is devoted to presenting controllability and stabilizability issues associated to a class of nonsmooth dynamical systems, namely complementarity dynamical systems. The main existing results are summarized, and some possible research directions are provided. Convex analysis and complementarity problems are claimed to be the main analysis tools for control related studies. This paper mainly focuses on mechanical applications.     

The Metaflow architecture

The Metaflow architecture, a unified approach to maximizing the performance of superscalar microprocessors, is introduced. The Metaflow architecture exploits inherent instruction-level parallelism in conventional sequential programs by hardware means, without relying on optimizing compilers. It is based on a unified structure, the DRIS (deferred-scheduling, register-renaming instruction shelf), that manages out-of-order execution and most of the attendant problems. Coupling the DRIS with a speculative-execution mechanism that avoids conditional branch stalls results in performance limited only be inherent instruction-level parallelism and available execution resources. Although presented in the context of superscalar machines, the technique is equally applicable to a superpipelined implementation. Lightning, the first implementation of the Metaflow architecture, which executes the Sparc RISC instruction set is described     

Comparison between photocatalytic ozonation and other oxidation processes for the removal of phenols from water

Photocatalytic ozonation (1O₃ + VUV + TiO₂), ozonation (O₃), catalytic ozonation (O₃ + TiO₂), ozone photolysis (O₃ + VUV), photocatalysis (TiO₂ + VUV) and photolysis (VUV) have been compared in terms of formation of intermediates, extent of, mineralization (TOC, COD, chloride, nitrate) and kinetics in the aqueous treatment of three phenols (phenol, p‐chlorophenol and p‐nitrophenol). In all cases, photocatalytic ozonation led to lower degradation times for chemical oxygen demand and total organic carbon removal. Intermediates formed were similar in the different oxidation systems with some exceptions. They can be classified into three different types: polyphenols (resorcinol, catechol, hydroquinone), unsaturated carboxylic acids (maleic and fumaric acids) and saturated carboxylic acids (glyoxylic, formic and oxalic acids). First order kinetic equations have been checked for the oxidation processes studied in the case of the parent compound. Rate constants of these systems have also been calculated. Copyright © 2005 Society of Chemical Industry     

Neural networks and blood cell identification

The objective of this project is to propose a method of identifying cells found in human blood and to classify them based upon their morphological features using neural networks. The project focuses on three major blood cell types, namely, erythrocytes, leukocytes and platelets. The data are collected using peripheral blood smears from clinical patients. The image acquisition requires 100x magnification on all the blood smears, the preprocessing involves the use of median and edge enhance filters; the feature extraction is done by performing the wavelet transform on the images. Finally classification of the blood cell types is done using ALOPEX and Back Propagation trained neural networks. The efficacy of both networks is then compared by comparing their outputs and number of iterations required to reach the final result.