Direct femtosecond laser inscription in transparent dielectrics

Since 1996 direct femtosecond inscription in transparent dielectrics has become the subject of intensive research. This enabling technology significantly expands the technological boundaries for direct fabrication of 3D structures in a wide variety of materials. It allows modification of non-photosensitive materials, which opens the door to numerous practical applications. In this work we explored the direct femtosecond inscription of waveguides and demonstrated at least one order of magnitude enhancement in the most critical parameter - the induced contrast of the refractive index in a standard borosilicate optical glass. A record high induced refractive contrast of 2.5×10-2 is demonstrated. The waveguides fabricated possess one of the lowest losses, approaching level of Fresnel reflection losses at the glassair interface. High refractive index contrast allows the fabrication of curvilinear waveguides with low bend losses. We also demonstrated the optimisation of the inscription regimes in BK7 glass over a broad range of experimental parameters and observed a counter-intuitive increase of the induced refractive index contrast with increasing translation speed of a sample. Examples of inscription in a number of transparent dielectrics hosts using high repetition rate fs laser system (both glasses and crystals) are also presented. Sub-wavelength scale periodic inscription inside any material often demands supercritical propagation regimes, when pulse peak power is more than the critical power for selffocusing, sometimes several times higher than the critical power. For a sub-critical regime, when the pulse peak power is less than the critical power for self-focusing, we derive analytic expressions for Gaussian beam focusing in the presence of Kerr non-linearity as well as for a number of other beam shapes commonly used in experiments, including astigmatic and ring-shaped ones. In the part devoted to the fabrication of periodic structures, we report on recent development of our point-by-point method, demonstrating the shortest periodic perturbation created in the bulk of a pure fused silica sample, by using third harmonics (? =267 nm) of fundamental laser frequency (? =800 nm) and 1 kHz femtosecond laser system. To overcome the fundamental limitations of the point-by-point method we suggested and experimentally demonstrated the micro-holographic inscription method, which is based on using the combination of a diffractive optical element and standard micro-objectives. Sub-500 nm periodic structures with a much higher aspect ratio were demonstrated. From the applications point of view, we demonstrate examples of photonics devices by direct femtosecond fabrication method, including various vectorial bend-sensors fabricated in standard optical fibres, as well as a highly birefringent long-period gratings by direct modulation method. To address the intrinsic limitations of femtosecond inscription at very shallow depths we suggested the hybrid mask-less lithography method. The method is based on precision ablation of a thin metal layer deposited on the surface of the sample to create a mask. After that an ion-exchange process in the melt of Ag-containing salts allows quick and low-cost fabrication of shallow waveguides and other components of integrated optics. This approach covers the gap in direct fs inscription of shallow waveguide. Perspectives and future developments of direct femtosecond micro-fabrication are also discussed.

Multi-valued control problems and mixture density network

We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.

Cascaded nonlinear absorption of femtosecond laser pulses in dielectrics

We investigated the energy deposition process leading to the waveguide inscription in transparent dielectrics both experimentally and theoretically. Parameters of multiphoton absorption process and inscription thresholds were measured in a range of materials including YAG, ZnSe, RbPb2Cl5 crystals, and in fused silica and BK7 glasses.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

The diagenesis and palaeomagnetism of permian and triassic sediments from central Spain

Detailed diagenetic and palaeomagnetic studies have been made of Permian and Triassic rocks from the Iberian Cordillera, Spain. Five stratigraphical units comprising the Autunian, Saxonian, Buntsandstein, Muschelkalk, and Keuper have been studied in a number of sections which have been well documented sedimentologically. Autuninan rocks have a characteristic remanence which is exclusively reversed and corresponds to the Kiaman Interval. The pole position identified is consistent with previous studies, which indicate the rotation of Iberia in post-Triassic times. The Saxonian facies show complex multicomponent magnetizations; no polarity zonation can be resolved. The Buntsandstein is remarkable in that much of it is remagnetised in a direction similar to the present-day local geomagnetic field direction. The secondary remanence is carried by fine-grained haematite which has been formed as a result of carbonate dissolution associated with structural inversion of the Iberian Cordillera. Dating of diagenetic events associated with this remagnetization is also possible. Fragments of primary remanence are preserved in some fine-grained lithologies of the Buntsandstein. These indicate that normal and reversed zones of magnetization were originally present. The magnetization of the Muschelkalk and Keuper carbonates is also complex; secondary components similar to those of the Buntsandstein are present but there is evidence that primary components were predominantly normal during Karnian times.

Analysis of powder caking in multicomponent powders using atomic force microscopy to examine particle properties

Atomic force microscopy has been used to study the surface properties of model spray dried powders. Phase imaging, nanoindentation and force modulation microscopy have differentiated between the different surface material properties of the particles, revealing a regular dispersion of soft, oil rich areas distributed across the particles' surface. Humidity and temperature cycling effects on the caking behavior of the particles have also been investigated, with significant morphology changes and onset of caking found to occur within relatively short periods of time.

Mass transfer and chemical reaction on a distillation plate

A multistage distillation column in which mass transfer and a reversible chemical reaction occurred simultaneously, has been investigated to formulate a technique by which this process can be analysed or predicted. A transesterification reaction between ethyl alcohol and butyl acetate, catalysed by concentrated sulphuric acid, was selected for the investigation and all the components were analysed on a gas liquid chromatograph. The transesterification reaction kinetics have been studied in a batch reactor for catalyst concentrations of 0.1 - 1.0 weight percent and temperatures between 21.4 and 85.0 °C. The reaction was found to be second order and dependent on the catalyst concentration at a given temperature. The vapour liquid equilibrium data for six binary, four ternary and one quaternary systems are measured at atmospheric pressure using a modified Cathala dynamic equilibrium still. The systems with the exception of ethyl alcohol - butyl alcohol mixtures, were found to be non-ideal. Multicomponent vapour liquid equilibrium compositions were predicted by a computer programme which utilised the Van Laar constants obtained from the binary data sets. Good agreement was obtained between the predicted and experimental quaternary equilibrium vapour compositions. Continuous transesterification experiments were carried out in a six stage sieve plate distillation column. The column was 3" in internal diameter and of unit construction in glass. The plates were 8" apart and had a free area of 7.7%. Both the liquid and vapour streams were analysed. The component conversion was dependent on the boilup rate and the reflux ratio. Because of the presence of the reaction, the concentration of one of the lighter components increased below the feed plate. In the same region a highly developed foam was formed due to the presence of the catalyst. The experimental results were analysed by the solution of a series of simultaneous enthalpy and mass equations. Good agreement was obtained between the experimental and calculated results.

Nanostructured Cu/Nb multilayers subjected to helium ion-irradiation

Helium ion-irradiation experiments have been performed in single layer Cu films, Nb films and Cu/Nb multilayer films with layer thickness varying from 2.5 nm to 100 nm each layer. Peak helium concentration approaches a few atomic percent with 6-9 displacement-per-atom in Cu and Nb. He bubbles were observed in single layer Cu and Nb films, as well as in Cu 100 nm/Nb 100 nm multilayers with helium bubbles aligned along layer interfaces. Helium bubbles are not resolved via transmission electron microscopy in Cu 2.5 nm/Nb 2.5 nm multilayers. These studies indicate that layer interface may play an important role in annihilating ion-irradiation induced defects such as vacancies and interstitials and have implications in improving the radiation tolerance of metallic materials using nanostructured multilayers. © 2007 Elsevier B.V. All rights reserved.

Plasma assisted femtosecond laser inscription in dielectrics

Principles of the femtosecond fabrication of the optoelectronic components in glass are explained and illustrated by examples of the in-bulk writing. The results of the experimental investigation of the dependence of the induced index change on the pulse energy and the numerical modelling of the corresponding laser-glass interaction are presented. The distribution of the plasma density is simulated that may bridge the gap between the models of the pulse propagation and the induced permanent refractive index change. © 2006 American Institute of Physics.

Strengthening mechanisms in nanostructured copper/304 stainless steel multilayers

Nanostructured Cu/304 stainless steel (SS) multilayers were prepared by magnetron sputtering. 304SS has a face-centered-cubic (fcc) structure in bulk. However, in the Cu/304SS multilayers, the 304SS layers exhibit the fcc structure for layer thickness of =5 nm in epitaxy with the neighboring fcc Cu. For 304SS layer thickness larger than 5 nm, body-centered-cubic (bcc) 304SS grains grow on top of the initial 5 nm fcc SS with the Kurdjumov-Sachs orientation relationship between bcc and fcc SS grains. The maximum hardness of Cu/304SS multilayers is about 5.5 GPa (factor of two enhancement compared to rule-of-mixtures hardness) at a layer thickness of 5 nm. Below 5 nm, hardness decreases with decreasing layer thickness. The peak hardness of fcc/fcc Cu/304SS multilayer is greater than that of Cu/Ni, even though the lattice-parameter mismatch between Cu and Ni is five times greater than that between Cu and 304SS. This result may primarily be attributed to the higher interface barrier stress for single-dislocation transmission across the {111} twinned interfaces in Cu/304SS as compared to the {100} interfaces in Cu/Ni.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.