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.

Pulse mixing by Fibonacci and Thue-Morse ordered semiconductor layers

The properties of combinatorial frequency generation by two-tone Gaussian pulses incident at oblique angles on quasiperiodic (Fibonacci and Thue-Morse) stacks of binary semiconductor layers are discussed. The analysis has been performed using the self-consistent model taking into account the nonlinear dynamics of mobile charges in the layers. The effects of the stack arrangements and constituent layer parameters on the combinatorial frequency waveforms are presented for the specific structures of both types

The Application of Semiconductor Photocatalysis for the Removal of Cyanotoxins from Water and Design Concepts for Solar Photocatalytic Reactors for Large Scale Water Treatment

There has been a significant increase in the occurrence of cyanobacterial blooms in freshwaters over the past few decades due to escalating nutrient levels. These cyanobacteria release a range of toxins, for example microcystins which are chemically very stable. Many cyanotoxins are consequently very difficult to remove from water using existing treatment technologies. Semiconductor photocatalysis, however, has proven to be a very effective process for the removal of these compounds from water. In this chapter we consider the application of this highly versatile and exciting technology for the decomposition of cyanotoxins. Furthermore design concepts for solar photocatalytic reactors that could be utilized for the removal of these toxins are also considered

Removal of microorganisms and their chemical metabolites from water using semiconductor photocatalysis

Semiconductor photocatalysis has been applied to the remediation of an extensive range of chemical pollutants in water over the past 30 years. The application of this versatile technology for removal of micro-organisms and cyanotoxins has recently become an area that has also been the subject of extensive research particularly over the past decade. This paper considers recent research in the application of semiconductor photocatalysis for the treatment of water contaminated with pathogenic micro-organisms and cyanotoxins. The basic processes involved in photocatalysis are described and examples of recent research into the use of photocatalysis for the removal of a range of microorganisms are detailed. The paper concludes with a review of the key research on the application of this process for the removal of chemical metabolites generated from cyanobacteria.

Factors affecting the photoelectrochemical fixation of carbon dioxide with semiconductor colloids

Carbon dioxide was reduced photocatalytically using aqueous CdS or ZnS colloids containing tetramethylammonium chloride to give the dimeric and tetrameric products namely, oxalate, glyoxylate, glycolate and tartrate. A model is presented to explain the role of the tetramethylammonium ions. Studies were also performed using ZnO, SiC, BaTiO₃ and Sr TiO₃, which in the absence of tetramethylammonium ions produced formate and formaldehyde. The relative quantum efficiencies of the six semiconductors were related to their band gaps and conduction band potentials. The role and effectiveness of several 'hole acceptor' (electron donor) compounds in this process is shown to be related to their redox potentials.

Destruction of cyanobacterial toxins by semiconductor photocatalysis

The rapid destruction of microcystin, a cyanobacterial toxin, using a titanium dioxide photocatalyst is observed; the process is extremely efficient with high concentrations of toxin completely undetectable within 10-40 min, depending on the initial concentration.

Semiconductor photocatalysis: an environmentally acceptable alternative production technique and effluent treatment process

New environmentally acceptable production methods are required to help reduce the environmental impact of many industrial processes. One potential route is the application of photocatalysis using semiconductors. This technique has enabled new environmentally acceptable synthetic routes for organic synthesis which do not require the use of toxic metals as redox reagents. These photocatalysts also have more favourable redox potentials than many traditional reagents. Semiconductor photocatalysis can also be applied to the treatment of polluted effluent or for the destruction of undesirable by-products of reactions. In addition to the clean nature of the process the power requirements of the technique can be relatively low, with some reactions requiring only sunlight. 

Density functional theory study of mixed-phase TiO2: heterostructures and electronic properties

In this work, density functional theory calculations have been performed to study the geometric, electronic, and energetic properties of two-phase TiO2 composites built by joining two single-phase TiO2 slabs, aiming at verifying possible improvement of the photo-activities of the composites through phase separation of excitons. We find that such desired electronic properties can be determined by several factors. When both the HOMO and LUMO levels of one of the two single-phase TiO2 slabs are higher than the corresponding ones of the other, the composite may have native electronic structures with phase-separated HOMO-LUMO states, especially when the two slabs exhibit highly matched surface lattices. For those pairs of TiO2 slabs with the HOMO and LUMO levels of one phase being within the range of those of the other, though the energetically favored composite give HOMO-LUMO states within one phase, one may still be able to separate them and move the HOMO state to the interface region by destabilizing the interactions between the two slabs.

Nonreciprocal nonlinear scattering by stacked magnetoactive semiconductor layers

The combinatorial frequency generation by the periodic stacks of magnetically biased semiconductor layers has been modelled in the self-consistent problem formulation, taking into account the nonlinear dynamics of carriers. It has been shown that the nonlinear response of the magnetoactive semiconductor periodic structure is strongly enhanced by magnetic bias and combinations of the layer physical and geometrical parameters. The effects of the pump wave nonreciprocal reflectance and field displacement on the efficiency of three-wave mixing process is illustrated by the simulation results

Quasi-periodic stacks of semiconductor layers: Combinatorial frequency generation

The nonlinear scattering and combinatorial frequency generation by the quasi-periodic Fibonacci and Thue-Morse stacks of semiconductor layers have been investigated taking into account the nonlinear charge dynamics. It has been shown that the mixing processes in passive semiconductor structures are driven by the competitive effects of the collision of charges and resonance interactions of carriers with pump waves. The effects of the stack arrangements and constituent layer parameters on the efficiency of the combinatorial frequency generation are discussed. 

Powder semiconductor photocatalysis in aqueous solution: An overview of kinetics-based reaction mechanisms

A brief, historical overview of 10 apparently different, although in some cases, upon inspection, closely related, popular proposed reaction mechanisms and their associated rate equations, is given and in which the rate expression for each mechanism is derived from basic principles, Appendix A. In Appendix B, each of the 5 main mechanisms are tested using datasets, comprising initial reaction rate vs. organic pollutant concentration, [P] and incident irradiance, ρ, data, reported previously for TiO2, where P is phenol, 4-chlorophenol and formic acid. The best of those tested, in terms of overall fit, simplicity, usefulness and versatility is the disrupted adsorption kinetic model proposed by Ollis. The usual basic assumptions made in constructing these mechanisms are reported and the main underlying concerns explored.