Computational modeling of defects in nanoscale device materials

This PhD thesis concerns the computational modeling of the electronic and atomic structure of point defects in technologically relevant materials. Identifying the atomistic origin of defects observed in the electrical characteristics of electronic devices has been a long-term goal of first-principles methods. First principles simulations are performed in this thesis, consisting of density functional theory (DFT) supplemented with many body perturbation theory (MBPT) methods, of native defects in bulk and slab models of In0.53Ga0.47As. The latter consist of (100) - oriented surfaces passivated with A12O3. Our results indicate that the experimentally extracted midgap interface state density (Dit) peaks are not the result of defects directly at the semiconductor/oxide interface, but originate from defects in a more bulk-like chemical environment. This conclusion is reached by considering the energy of charge transition levels for defects at the interface as a function of distance from the oxide. Our work provides insight into the types of defects responsible for the observed departure from ideal electrical behaviour in III-V metal-oxidesemiconductor (MOS) capacitors. In addition, the formation energetics and electron scattering properties of point defects in carbon nanotubes (CNTs) are studied using DFT in conjunction with Green’s function based techniques. The latter are applied to evaluate the low-temperature, low-bias Landauer conductance spectrum from which mesoscopic transport properties such as the elastic mean free path and localization length of technologically relevant CNT sizes can be estimated from computationally tractable CNT models. Our calculations show that at CNT diameters pertinent to interconnect applications, the 555777 divacancy defect results in increased scattering and hence higher electrical resistance for electron transport near the Fermi level.

Assessing the immune phenotype of cardiac syndrome x: a prospective study of biomarkers

Cardiac Syndrome X (CSX), the presence of angina pectoris with objective signs of myocardial ischaemia despite angiographically normal epicardial coronary arteries, appears to be due to coronary microvascular dysfunction and is known to be associated with an elevation of several inflammatory biomarkers, suggesting a possible role for inflammation in its pathogenesis. We aimed to further characterise this relationship by prospectively analysing a wide variety of molecular biomarkers in a cohort of CSX patients thereby charting the changes in biomarkers throughout the natural history of CSX from its initial diagnosis to eventual disease quiescence. We found that CSX patients, when compared to healthy controls, have a persistent low-grade systemic inflammatory response characterised by an elevation of Tumour Necrosis Factor and Interferon-gamma, regardless of the presence of contemporaneous signs or symptoms of disease activity. Interleukin-6 and C-reactive Protein (CRP) are only elevated when patients have clinical evidence of disease activity and may be state markers in CSX. Moreover, CRP levels appear to correlate with signals of disease severity such as the time taken to develop symptoms during exercise stress testing. We have also demonstrated that the enzyme Indoleamine-2,3- dioxygenase is upregulated in active disease thus providing a possible explanation for the increased burden of psychological disease encountered in CSX. Analysis of the microRNA transcriptome showed that miR-143 is significantly under-expressed in CSX patients. This could allow phenotype switching in vascular smooth muscle cells with the resultant vascular remodelling causing reduced vessel responsiveness to local rheological stimuli and reduced luminal diameter with consequent increased microvascular resistance during times of increased myocardial oxygen demand, thereby limiting maximal hyperaemia during exercise. Our findings corroborate many previous hypotheses regarding the role of inflammation in CSX, generate new insights into possible pathogenic mechanisms and offer new therapeutic targets for the future management of this important cardiological condition.

Design of digital differentiators

A digital differentiator simply involves the derivation of an input signal. This work includes the presentation of first-degree and second-degree differentiators, which are designed as both infinite-impulse-response (IIR) filters and finite-impulse-response (FIR) filters. The proposed differentiators have low-pass magnitude response characteristics, thereby rejecting noise frequencies higher than the cut-off frequency. Both steady-state frequency-domain characteristics and Time-domain analyses are given for the proposed differentiators. It is shown that the proposed differentiators perform well when compared to previously proposed filters. When considering the time-domain characteristics of the differentiators, the processing of quantized signals proved especially enlightening, in terms of the filtering effects of the proposed differentiators. The coefficients of the proposed differentiators are obtained using an optimization algorithm, while the optimization objectives include magnitude and phase response. The low-pass characteristic of the proposed differentiators is achieved by minimizing the filter variance. The low-pass differentiators designed show the steep roll-off, as well as having highly accurate magnitude response in the pass-band. While having a history of over three hundred years, the design of fractional differentiator has become a ‘hot topic’ in recent decades. One challenging problem in this area is that there are many different definitions to describe the fractional model, such as the Riemann-Liouville and Caputo definitions. Through use of a feedback structure, based on the Riemann-Liouville definition. It is shown that the performance of the fractional differentiator can be improved in both the frequency-domain and time-domain. Two applications based on the proposed differentiators are described in the thesis. Specifically, the first of these involves the application of second degree differentiators in the estimation of the frequency components of a power system. The second example concerns for an image processing, edge detection application.