Transmission of two novel mutations in a pedigree with familial lecithin:cholesterol acyltransferase deficiency:structure-function relationships and studies in a compound heterozygous proband

Two novel mutations were identified in a compound heterozygous male with lecithin:cholesterol acyltransferase (LCAT) deficiency. Exon sequence determination of the LCAT gene of the proband revealed two novel heterozygous mutations in exons one (C110T) and six (C991T) that predict non-conservative amino acid substitutions (Thr13Met and Pro307Ser, respectively). To assess the distinct functional impact of the separate mutant alleles, studies were conducted in the proband's 3-generation pedigree. The compound heterozygous proband had negligible HDL and severely reduced apolipoprotein A-I, LCAT mass, LCAT activity, and cholesterol esterification rate (CER). The proband's mother and two sisters were heterozygous for the Pro307Ser mutation and had low HDL, markedly reduced LCAT activity and CER, and the propensity for significant reductions in LCAT protein mass. The proband's father and two daughters were heterozygous for the Thr13Met mutation and also displayed low HDL, reduced LCAT activity and CER, and more modest decrements in LCAT mass. Mean LCAT specific activity was severely impaired in the compound heterozygous proband and was reduced by 50% in individuals heterozygous for either mutation, compared to wild type family members. It is also shown that the two mutations impair both catalytic activity and expression of the circulating protein.

Water-hydrophobic compound interactions with the microbial cell

The structural interactions of biological macromolecules, their biochemical activities and, ultimately, the metabolic function of cellular systems are dependent upon weak inter- and intra-molecular forces such as hydrogen bonds, Van der Waals forces, and the hydrophobic effect. Water molecules, and those of hydrophobic substances such as hydrocarbons, can take part in and/or modify these interactions and thereby determine the operational and structural stability of the microbial cell and its macromolecular systems. We explain how the cytosol, plasma membrane and the extracellular solution form a material and energetic continuum; and discuss the behavior of hydrophobic substances of extracellular origin as they migrate into the plasma membrane and into the cell's interior. The adverse effects of substances with a log P octanol-water =2, that partition into the hydrophobic domains of biological macromolecules, are discussed in relation to microbial cell function; and we speculate whether the cellular stress that they induce is symmetrical or asymmetrical in nature. In the context of the microbial environment, we take a situational-functional approach to consider how hydrophobic stressors interact with the microbial cell, and what types of evasion tactics microbes can employ to minimize their inhibitory activities. Finally, we discuss the ecological implications of hydrocarbon-induced cellular stress for microbial systems.

Effect of magnetic bias on Gaussian pulse scattering by stacked nonlinear semiconductor layers

The nonlinear scattering of pulses by periodic stacks of semiconductor layers with magnetic bias has been studied in the self-consistent problem formulation, taking into account mobility of carriers. The three-wave mixing technique has been applied to the analysis of the waveform evolution in the stacks illuminated by two Gaussian pulses with different central frequencies and lengths. The effects of external magnetic bias, and stack physical and geometrical parameters on the properties of the scattered waveforms are discussed. © 2013 IEEE.

Pulsed second harmonic generation by stacks of magnetically biased semiconductor layers

The pulsed second harmonic generation (SHG) by periodic stacks of nonlinear semiconductor layers with external magnetic bias has been studied in the self-consistent problem formulation, taking into account mobility of carriers. The products of nonlinear scattering in the three-wave mixing process are examined. It is demonstrated that the waveform evolution in magnetoactive weakly nonlinear semiconductor periodic structure illuminated by Gaussian pulse is strongly affected by the magnetic bias and collision frequency of the carriers. The effect of nonreciprocity on the SHG efficiency is discussed and illustrated by the examples. © 2013 European Microwave Association.

Wave mixing by periodic nonlinear semiconductor multilayers

The properties of the combinatorial frequency generation and wave scattering by periodic stacks of nonlinear passive semiconductor layers are explored. It is demonstrated that the nonlinearity in passive weakly nonlinear semiconductor medium has the resistive nature associated with the dynamics of carriers. The features of the combinatorial frequency generation and the effects of the pump wave scattering and parameters of the constituent semiconductor layers on the efficiency of the frequency mixing are discussed and illustrated by the examples. © 2013 IEICE.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

Improved Thermal Performance of SOI Using Compound Buried Layer

The buried oxide (BOX) layer in silicon on insulator (SOI) was replaced by a compound buried layer (CBL) containing layers of SiO2, polycrystalline silicon (polysilicon), and SiO2. The undoped polysilicon in the CBL acted as a dielectric with a higher thermal conductivity than SiO2. CBL provides a reduced thermal resistance with the same equivalent oxide thickness as a standard SiO2 buried layer. Thermal resistance was further reduced by lateral heat flow through the polysilicon. Reduction in thermal resistance by up to 68% was observed, dependent on polysilicon thickness. CBL SOI substrates were designed and manufactured to achieve a 40% reduction in thermal resistance compared with an 1.0-μm SiO2 BOX. Power bipolar transistors with an active silicon layer thickness of 13.5 μm manufactured on CBL SOI substrates showed a 5%-17% reduction in thermal resistance compared with the standard SOI. This reduction was dependent on transistor layout geometry. Between 65% and 90% of the heat flow from these power transistors is laterally through the thick active silicon layer. Analysis confirmed that CBL SOI provided a 40% reduction in the vertical path thermal resistance. Devices employing thinner active silicon layers will achieve the greater benefit from reduction in vertical path thermal resistance offered by CBL SOI.

Nonreciprocal scattering by stacked nonlinear magneto-active semiconductor layers

The combinatorial frequency generation by the periodic stacks of magnetically biased semiconductor layers has been modelled in a self-consistent problem formulation, taking into account the nonlinear dynamics of carriers. It is shown that magnetic bias not only renders nonreciprocity of the three-wave mixing process but also significantly enhances the nonlinear interactions in the stacks, especially at the frequencies close to the intrinsic magneto-plasma resonances of the constituent layers. The main mechanisms and properties of the combinatorial frequency generation and emission from the stacks are illustrated by the simulation results, and the effects of the individual layer parameters and the structure arrangement on the stack nonlinear and nonreciprocal response are discussed. © 2014 Elsevier B.V. All rights reserved.

CHARACTERIZATION OF SURFACE-PLASMONS ON METAL-OXIDE-SEMICONDUCTOR STRUCTURES

Using the Otto (prism-air gap-sample) configuration p-polarized light of wavelength 632.8 nm has been coupled with greater than 80% efficiency to surface plasmons on the aluminium electrode of silicon-silicon dioxide-aluminium structures. The results show that if the average power per unit area dissipated on the metal film exceeds approximately 1 mW mm-2, then the coupling gap and thus the characteristics of the surface plasmon resonance are noticeably altered. In modelling the optical response of such systems the inclusion of both a non-uniform air coupling gap and a thin cermet layer at the aluminium surface may be necessary.

Microstructure, properties and application of YAl2 intermetallic compound as particle reinforcements

An yttrium aluminum (YAl2) intermetallic compound ingot was prepared in an induction furnace under vacuum. The microstructure of YAl2 ingot was characterized by optical microscopy, scanning electron microscopy, and X-ray diffraction. The load bearing response of YAl2 intermetallic was investigated and compared with SiC ceramic by indentation combined with optical microscopy and scanning electron microscopy. Additionally, the tensile properties of the Mg–Li matrix composites reinforced with ultrafine YAl2 particles fabricated by planet ball milling were tested. The results show that the intermetallic compound ingot in this experiment is composed of a main face-centered-cubic structure YAl2 phase, a small amount of YAl phase, and minor Y and Al-rich phases. YAl2 intermetallic compound has excellent stability and shows better capability in crack resistance than SiC ceramic. The YAl2 intermetallic compound has better deformation compatibility with the Mg–14Li–3Al matrix than SiC reinforcement with the matrix, which leads to the superior resistance to crack for YAl2p/Mg–14Li–3Al composite compared to SiCp/Mg–14Li–3Al composite.

Gaussian pulse mixing by stacked semiconductor layers

The nonlinear scattering of two Gaussian pulses with different central frequencies incident at slant angles on the periodic stack of binary semiconductor layers has been modelled in the self-consistent problem formulation taking into account the dynamics of charges. The effects of the pump pulse length and central frequencies, and the stack physical and geometrical parameters on the properties of the emitted combinatorial frequency waveforms are analysed and discussed.

Insight Extraction for Semiconductor Manufacturing Processes

In the semiconductor manufacturing environment it is very important to understand which factors have the most impact on process outcomes and to control them accordingly. This is usually achieved through design of experiments at process start-up and long term observation of production. As such it relies heavily on the expertise of the process engineer. In this work, we present an automatic approach to extracting useful insights about production processes and equipment based on state-of-the-art Machine Learning techniques. The main goal of this activity is to provide tools to process engineers to accelerate the learning-by-observation phase of process analysis. Using a Metal Deposition process as an example, we highlight various ways in which the extracted information can be employed.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

Electron recombination, photoionization, and scattering via many-electron compound resonances

Highly excited eigenstates of atoms and ions with open f shell are chaotic superpositions of thousands, or even millions, of Hartree-Fock determinant states. The interaction between dielectronic and multielectronic configurations leads to the broadening of dielectronic recombination resonances and relative enhancement of photon emission due to opening of thousands of radiative decay channels. The radiative yield is close to 100% for electron energy <1 eV and rapidly decreases for higher energies due to opening of many autoionization channels. The same mechanism predicts suppression of photoionization and relative enhancement of the Raman scattering. Results of our calculations of the recombination rate are in agreement with the experimental data for W20+ and Au25+.