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.

A dynamic sampling methodology for plasma etch processes using Gaussian process regression

Plasma etch is a key process in modern semiconductor manufacturing facilities as it offers process simplification and yet greater dimensional tolerances compared to wet chemical etch technology. The main challenge of operating plasma etchers is to maintain a consistent etch rate spatially and temporally for a given wafer and for successive wafers processed in the same etch tool. Etch rate measurements require expensive metrology steps and therefore in general only limited sampling is performed. Furthermore, the results of measurements are not accessible in real-time, limiting the options for run-to-run control. This paper investigates a Virtual Metrology (VM) enabled Dynamic Sampling (DS) methodology as an alternative paradigm for balancing the need to reduce costly metrology with the need to measure more frequently and in a timely fashion to enable wafer-to-wafer control. Using a Gaussian Process Regression (GPR) VM model for etch rate estimation of a plasma etch process, the proposed dynamic sampling methodology is demonstrated and evaluated for a number of different predictive dynamic sampling rules. © 2013 IEEE.

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.

Hydrogel-Forming Microneedles Prepared from ‘‘Super Swelling’’ Polymers Combined with Lyophilised Wafers for Transdermal Drug Delivery

We describe, for the first time, hydrogel-forming microneedle arrays prepared from "super swelling" polymeric compositions. We produced a microneedle formulation with enhanced swelling capabilities from aqueous blends containing 20% w/w Gantrez S-97, 7.5% w/w PEG 10,000 and 3% w/w Na2CO3 and utilised a drug reservoir of a lyophilised wafer-like design. These microneedle-lyophilised wafer compositions were robust and effectively penetrated skin, swelling extensively, but being removed intact. In in vitro delivery experiments across excised neonatal porcine skin, approximately 44 mg of the model high dose small molecule drug ibuprofen sodium was delivered in 24 h, equating to 37% of the loading in the lyophilised reservoir. The super swelling microneedles delivered approximately 1.24 mg of the model protein ovalbumin over 24 h, equivalent to a delivery efficiency of approximately 49%. The integrated microneedle-lyophilised wafer delivery system produced a progressive increase in plasma concentrations of ibuprofen sodium in rats over 6 h, with a maximal concentration of approximately 179 µg/ml achieved in this time. The plasma concentration had fallen to 71±6.7 µg/ml by 24 h. Ovalbumin levels peaked in rat plasma after only 1 hour at 42.36±17.01 ng/ml. Ovalbumin plasma levels then remained almost constant up to 6 h, dropping somewhat at 24 h, when 23.61±4.84 ng/ml was detected. This work represents a significant advancement on conventional microneedle systems, which are presently only suitable for bolus delivery of very potent drugs and vaccines. Once fully developed, such technology may greatly expand the range of drugs that can be delivered transdermally, to the benefit of patients and industry. Accordingly, we are currently progressing towards clinical evaluations with a range of candidate molecules.

A simple and low-cost method for the preparation of self-supported TiO2-WO3 ceramic heterojunction wafers

Robust, bilayer heterojunction photodiodes of TiO₂-WO₃ were prepared successfully by a simple, low-cost powder pressing technique followed by heat-treatment. Exclusive photoirradiation of the TiO₂ side of the photodiode resulted in a rapid colour change (dark blue) on the WO₃ surface as a result of reduction of W⁶⁺ to W⁵⁺ (confirmed by X-ray photoelectron spectroscopy). This colour was long lived and shown to be stable in a dry environment in air for several hours. A similar photoirradiation experiment in the presence of a mask showed that charge transfer across the heterojunction occurred approximately normal to the TiO₂ surface, with little smearing out of the mask image. As a result of the highly efficient vectorial charge separation, the photodiodes showed a tremendous increase in photocatalytic activity for the degradation of stearic acid, compared to wafers of the respective individual materials when tested separately.

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.