Nanostructured ferroelectric materials

Nanostructured materials are central to the evolution of future electronics and information technologies. Ferroelectrics have already been established as a dominant branch in the electronics sector because of their diverse application range such as ferroelectric memories, ferroelectric tunnel junctions, etc. The on-going dimensional downscaling of materials to allow packing of increased numbers of components onto integrated circuits provides the momentum for the evolution of nanostructured ferroelectric materials and devices. Nanoscaling of ferroelectric materials can result in a modification of their functionality, such as phase transition temperature or Curie temperature (TC), domain dynamics, dielectric constant, coercive field, spontaneous polarisation and piezoelectric response. Furthermore, nanoscaling can be used to form high density arrays of monodomain ferroelectric nanostructures, which is desirable for the miniaturisation of memory devices. This thesis details the use of various types of nanostructuring approaches to fabricate arrays of ferroelectric nanostructures, particularly non-oxide based systems. The introductory chapter reviews some exemplary research breakthroughs in the synthesis, characterisation and applications of nanoscale ferroelectric materials over the last decade, with priority given to novel synthetic strategies. Chapter 2 provides an overview of the experimental methods and characterisation tools used to produce and probe the properties of nanostructured antimony sulphide (Sb2S3), antimony sulpho iodide (SbSI) and lead titanate zirconate (PZT). In particular, Chapter 2 details the general principles of piezoresponse microscopy (PFM). Chapter 3 highlights the fabrication of arrays of Sb2S3 nanowires with variable diameters using newly developed solventless template-based approach. A detailed account of domain imaging and polarisation switching of these nanowire arrays is also provided. Chapter 4 details the preparation of vertically aligned arrays of SbSI nanorods and nanowires using a surface-roughness assisted vapour-phase deposition method. The qualitative and quantitative nanoscale ferroelectric properties of these nanostructures are also discussed. Chapter 5 highlights the fabrication of highly ordered arrays of PZT nanodots using block copolymer self-assembled templates and their ferroelectric characterisation using PFM. Chapter 6 summarises the conclusions drawn from the results reported in chapters 3, 4 and 5 and the future work.

Multi-scale modelling of atomic layer deposition

High-permittivity ("high-k") dielectric materials are used in the transistor gate stack in integrated circuits. As the thickness of silicon oxide dielectric reduces below 2 nm with continued downscaling, the leakage current because of tunnelling increases, leading to high power consumption and reduced device reliability. Hence, research concentrates on finding materials with high dielectric constant that can be easily integrated into a manufacturing process and show the desired properties as a thin film. Atomic layer deposition (ALD) is used practically to deposit high-k materials like HfO2, ZrO2, and Al2O3 as gate oxides. ALD is a technique for producing conformal layers of material with nanometer-scale thickness, used commercially in non-planar electronics and increasingly in other areas of science and technology. ALD is a type of chemical vapor deposition that depends on self-limiting surface chemistry. In ALD, gaseous precursors are allowed individually into the reactor chamber in alternating pulses. Between each pulse, inert gas is admitted to prevent gas phase reactions. This thesis provides a profound understanding of the ALD of oxides such as HfO2, showing how the chemistry affects the properties of the deposited film. Using multi-scale modelling of ALD, the kinetics of reactions at the growing surface is connected to experimental data. In this thesis, we use density functional theory (DFT) method to simulate more realistic models for the growth of HfO2 from Hf(N(CH3)2)4/H2O and HfCl4/H2O and for Al2O3 from Al(CH3)3/H2O.Three major breakthroughs are discovered. First, a new reaction pathway, ’multiple proton diffusion’, is proposed for the growth of HfO2 from Hf(N(CH3)2)4/H2O.1 As a second major breakthrough, a ’cooperative’ action between adsorbed precursors is shown to play an important role in ALD. By this we mean that previously-inert fragments can become reactive once sufficient molecules adsorb in their neighbourhood during either precursor pulse. As a third breakthrough, the ALD of HfO2 from Hf(N(CH3)2)4 and H2O is implemented for the first time into 3D on-lattice kinetic Monte-Carlo (KMC).2 In this integrated approach (DFT+KMC), retaining the accuracy of the atomistic model in the higher-scale model leads to remarkable breakthroughs in our understanding. The resulting atomistic model allows direct comparison with experimental techniques such as X-ray photoelectron spectroscopy and quartz crystal microbalance.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.