This report presents the design, simulation and fabrication of a spin qubit platform on ultrathin SOI (Silicon-on-Insulator) using A1 FinSET (Single electron transistor) gates and Si side gates. A new design layout is proposed for the double spin qubits co-integrated with a single electron electrometer, a waveguide and a nanomagnet. This platform aims to demonstrate the full operation of double spin qubits by integrating the following three key techniques in one compact footprint: a precisely controlled single electron transfer technology, a high speed charge detection technique and a single spin detection technology based on spin to charge conversion. A single electron transfer device (SETD) integrated with an electrometer is introduced here as the main building block of the spin qubit platform. The single electron transfer device consists of three nanowire (MOSETs) connected in series, and is capacitively coupled to an SET electrometer. A unique layout design for the SETD and a novel single electron transfer voltage pulse sequence are introduced. Simulation and dynamic analysis of this device operation are preformed using a finite element capacitance based simulation method and a Monte Carlo based single electron circuit simulation. The simulations demonstrated the ability of this platform to transfer single electrons and these characteristics are analyzed to optimize the layout. A novel fabrication process to realize high density silicon quantum dots (QDs) with A1 FinSET gates and close proximity Si gates on ultrathin SOI, for single electron transfer and detection, is successfully established with a number of different device layouts realized. In these devices, A1 FinSET gates surround an SOI nanowire channel forming electrically tunable potential barriers and defining QDs among them; Si plunger side gates are included to enable precise control of the QDs potential. Five SETD and electrometer device generations have been realized, tested and analyzed to improve the device yield; this extensive process development work is concluded with a novel fabrication approach to demonstrate successful FinSET A1 gae technology for SOI nanowires. This QDs platform is fabricated using a multi-layer electron beam lithography process that is fully compatible with metal oxide semiconductor technology. The fabrication process is fully developed with a yield of 92% and a great flexibility to enable the realization of more complex structures and even for devices beyond the scope of this project as shown in the appendices of this report.

This thesis deals with issues arising in manufacturing, in particular related to production efficiency. Lot streaming refers to the process of splitting jobs to move production through several stages as quickly as possible, whereas batch scheduling refers to the process of grouping jobs to improve the use of resources and customer satisfaction. We use a network representation and critical path approach to analyse the lot streaming problem of finding optimal sublot sizes and a job sequence in a two-machine flow shop with transportation and setup times. We introduce a model where the number of sublots for each job is not predetermined, presenting an algorithm to assign a new sublot efficiently, and discuss a heuristic to assign a fixed number of sublots between jobs. A model with several identical jobs in an multiple machine flow shop is analysed through a dominant machine approach to find optimal sublot sizes for jobs. For batch scheduling, we tackle the NP-hard problem of scheduling jobs on a batching machine with restricted batch size to minimise the maximum lateness. We design a branch and bound algorithm, and develop local search heuristics for the problem. Different neighbourhoods are compared, one of which is an exponential sized neighbourhood that can be searched in polynomial time. We develop dynamic programming algorithms to obtain lower bounds and explore neighbourhoods efficiently. The performance of the branch and bound algorithm and the local search heuristics is assessed and supported by extensive computational tests.

The objective of this research is to develop a methodology for incorporating cost models based on manufacturing process information within multidisciplinary design optimisation problems. Although cost considerations are critical in product design and development, cost models are rarely used in opti¬mising designs mainly due to the inability in acquiring accurate manufacturing cost estimates in early design. In this thesis, we present a new technique for embedding manufacturing process knowledge within a modelling tool which can be utilised to provide accurate cost estimates in design optimisation applications. We use the proposed cost estimating technique for optimising the geometry of two components from a Rolls-Royce civil aircraft engine by designing a sequential workflow consisting of CAD, analysis and cost models along with optimisation algorithms within an integrated system. Initial results from the first component (which is treated as a model problem) show that significant cost savings as well as shape changes can be achieved by using an accurate cost model in the objective function. The second case study dealt with is the shape optimisation of the initial 2D profile of a high pressure turbine disc. We develop highly flexible geometry parameterisation schemes to accurately represent manufacturing, supplier and inspection constraints inherent in the cost model for the disc. Significant differences in the geometry are achieved when the design is optimised for low manufacturing cost as compared to traditional weight minimisation leading to the second part of this thesis that deals with the hypothesis that low volume and low cost are conflicting attributes. Multiobjective optimisation approaches are then utilised to generate a Pareto front of designs with optimum combinations of both objectives. We then proceed to list the obstacles which prevent a straightforward application of multiobjective techniques to sophisticated design problems and propose modifications which enhance the quality of results achieved. Finally, a flowchart detailing the design optimisation framework used in this thesis is described for the benefit of future applications. We then conclude by stating the salient contributions of this work and interesting avenues of future research that can be pursued.

Increasing integration and complexity in IC design provides challenges for manufacturing testing. This thesis studies how process and supply voltage variation influence defect behaviour to determine the impact on manufacturing test cost and quality. The focus is on logic testing of static CMOS designs with respect to two important defect types in deep submicron CMOS: resistive bridges and full opens. The first part of the thesis addresses testing for resistive bridge defects in designs with multiple supply voltage settings. To enable analysis, a fault simulator is developed using a supply voltage-aware model for bridge defect behaviour. The analysis shows that for high defect coverage it is necessary to perform test for more than one supply voltage setting, due to supply voltage-dependent behaviour. A low-cost and effective test method is presented consisting of multi-voltage test generation that achieves high defect coverage and test set size reduction without compromise to defect coverage. Experiments on synthesised benchmarks with realistic bridge locations validate the proposed method. The second part focuses on the behaviour of full open defects under supply voltage variation. The aim is to determine the appropriate value of supply voltage to use when testing. Two models are considered for the behaviour of full open defects with and without gate tunnelling leakage influence. Analysis of the supply voltage-dependent behaviour of full open defects is performed to determine if it is required to test using more than one supply voltage to detect all full open defects. Experiments on synthesised benchmarks using an extended version of the fault simulator tool mentioned above, measure the quantitative impact of supply voltage variation on defect coverage. The final part studies the impact of process variation on the behaviour of bridge defects. Detailed analysis using synthesised ISCAS benchmarks and realistic bridge model shows that process variation leads to additional faults. If process variation is not considered in test generation, the test will fail to detect some of these faults, which leads to test escapes. A novel metric to quantify the impact of process variation on test quality is employed in the development of a new test generation tool, which achieves high bridge defect coverage. The method achieves a user-specied test quality with test sets which are smaller than test sets generated without consideration of process variation.

This research has investigated the modelling and optimisation of the tilt casting process of Titanium Aluminides (TiAl). This study is carried out in parallel with the experimental research undertaken in IRC at the University of Birmingham. They propose to use tilt casting inside a vacuum chamber and attempt to combine this tilt casting process with Induction Skull Melting (ISM). A totally novel process is developing for investment casting, which is suitable for casting gamma TiAl. As it is known, gamma TiAl alloys has great properties including low density, high specific yield strength, high specific stiffness, good oxidation resistance and good creep resistance at high temperature [Clemens -2000][Appel et at. -2000]. A worldwide research effort has been made to develop gamma TiAl because it can offer a great potential for reducing the weight of high performance components and also engine of power generation gas turbine. Titanium alloys are very reactive at molten condition, and so, they are melted in an ISM crucible in order to avoid crucible contamination. There is still a big challenge to produce a long blade, up to 40 cm, due to the low superheat provided by the Induction Skull Melting (ISM) furnace which is widely used to melt the alloys. Here computational simulation has been seen important to predict the casting defects and to help optimise the experimental process. Computational modelling for the casting process involves a range of interactions of physical phenomena such as heat transfer, free surface fluid flow, solidification and so on. A number of free surface modelling techniques are applied to simulate the interface between the molten metal entering the mould in the filling phase, and the gas escaping. The CFD code PHYSICA developed in the University of Greenwich is used to simulate the above physical phenomena and to simulate the fluid flow both within the rotating mould cavity/crucible assembly and in the porous mould wall (including vents). Modelling the mould in a finite volume method is cumbersome, so an alternative 3D/1D coupled transient heat transfer model has been developed in this study. It is based on the fact that the mould filling for titanium aluminide (TiAl) is carried out during a few seconds and the thermal conductivity of the mould material is very low. Heat can be assumed to transfer mainly in a direction perpendicular to the mould wall ID. ID transient heat transfer model is governed by ID heat conduction equation in the mould part where the coordinates of each defined cell centre were calculated rather than meshing them. The coupling method between ID and 3D model is presented. The model is then validated using two simple geometries which describe two similar states in the mould filling as test cases. It has been applied to model short thin and long blades, especially to obtain accurate thermal boundaries. Comparisons with experiments have also been done. Across the presentation of the results, the factors affect the quality of the casting in the mould filling have been discussed. This thesis also presents a novel Counter Diffusion Method which was developed with suggestions from my supervisors as a corrective mechanism to counter numerical diffusion. This is a novel method to discretise the free surface equation fully implicitly in a fast, efficient way without numerical diffusion. Validation of the novel method was undertaken against the classical collapsing column experiment. The results showed that they are in good agreement. Then the method has been used to model a long thin blade for TiAl. A huge reduction in computational time is seen when the geometry is complex and massive amount of mesh cells are generated. That greatly speeds up the simulations. Solidification is modeled during the cooling which is following the filling stage. Gap formation between metal and mould is covered and the effects of the gap and gap size are presented by the application of model on a long twisted turbine blade.

The main purpose of this investigation was to explore the potential benefits of structural functional surfaces using facilities available within the University. The potential benefits were demonstrated by applying functional surfaces to a set of particular engineering applications. The thesis mainly concentrated on improving the frictional performance of a surface structure for hydrodynamic bearing application. This thesis has also included some preliminary investigation into drag-reducing riblet structures but this chapter mainly discusses the development of a novel experimental apparatus which is needed for precise boundary layer profile measurements and also to obtain the actual surface drag for each sample. To be able to assess these surfaces experimentally, they first, have to be manufactured. So, an extensive literature review of current manufacturing technologies was carried out. Each manufacturing method was ranked in its ability to cost-effectively produce surfaces with accuracy and repeatability also being considered. It was concluded that rolling, currently, has the best ability to structure large surface areas with the lowest costs associated. Other manufacturing methods, such as laser surface texturing, provide excellent repeatability and accuracy as well as the ability to create complex surface structures but is incredibly time-consuming for large surface areas. It was suggested that a hybrid of multiple manufacturing technologies would be incredibly useful for structuring surfaces. By combining rolling with more elaborate surface texturing methods (i.e. use a method such as LST to texture the roller surface), it is possible to amplify the productivity of less efficient methods, substantially. Before any journal components were textured, it was decided to test a batch of ground components. These components were finished with an abrasive tape process. The process parameters were varied for each sample and by doing this, a set of components with different roughness characteristics should have been obtained. The components were measured for 2D roughness parameters, 3D roughness parameters and surface energy. The components were tested on a tribometer apparatus in order to obtain a coefficient of friction (COF) for each sample. Correlation coefficients were then generated for the different surface measurements against COF, so that any strong correlations or trends could be identified. The idea was to try and obtain a reliable performance indicator (PI) so that frictional losses could be identified. It was found that the roughness parameters Sc (core void voume), Ssc (mean summit curvature) and Rku/Sku (profile/surface kurtosis) showed promise in the ability to predict the performance of a surface. The next stage was to texture the surface of the journal component. This would done by the application of the type III texturing grinding process, described by Stepien (Surface Engineering, 24: 219-225), to the cylindrical grinding process. Some initial components were manufactured and the textures generated were found to be of an ellipsoidal shape. In order to guarantee the benefits of such surfaces, the configuration of the surface pattern has to be optimised. A python script was developed during this investigation in order to automate a full modelling process. The computational fluid dynamics (CFD) modelling used a full 3D Navier-Stokes approximation. This script was used in conjunction with the Taguchi optimisation technique and a best surface configuration was found, resulting in a maximum surface drag reduction of 16.6% at a 3μm clearance. Further grinding trials were performed and the input parameters of the process were designed so that surface patterns were close to the recommendations of the optimisation process. The performance of the textured samples was impressive, with a maximum reduction in COF of 18.4% seen against a non-textured component with similar average roughness (Sa) value. Again, all components were measured for the aforementioned roughness parameters and surface energy. Sku continued to predict the best-performing component, showing promise as PI for both non-textured and textured samples.

The work undertaken in this thesis is about the integration of two well-known methodologies: Petri net (PN) model Ii ng/analysis of industrial production processes and Artificial Intelligence (AI) optimisation search techniques. The objective of this integration is to demonstrate its potential in solving a difficult and widely studied problem, the scheduling of Flexible Manufacturing Systems (FIVIS). This work builds on existing results that clearly show the convenience of PNs as a modelling tool for FIVIS. It addresses the problem of the integration of PN and Al based search methods. Whilst this is recognised as a potentially important approach to the scheduling of FIVIS there is a lack of any clear evidence that practical systems might be built. This thesis presents a novel scheduling methodology that takes forward the current state of the art in the area by: Firstly presenting a novel modelling procedure based on a new class of PN (cb-NETS) and a language to define the essential features of basic FIVIS, demonstrating that the inclusion of high level FIVIS constraints is straight forward. Secondly, we demonstrate that PN analysis is useful in reducing search complexity and presents two main results: a novel heuristic function based on PN analysis that is more efficient than existing methods and a novel reachability scheme that avoids futile exploration of candidate schedules. Thirdly a novel scheduling algorithm that overcomes the efficiency drawbacks of previous algorithms is presented. This algorithm satisfactorily overcomes the complexity issue while achieving very promising results in terms of optimality. Finally, this thesis presents a novel hybrid scheduler that demonstrates the convenience of the use of PN as a representation paradigm to support hybridisation between traditional OR methods, Al systematic search and stochastic optimisation algorithms. Initial results show that the approach is promising.

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In this work, an inverse FE modelling program based on the Kalman filter technique has been developed and used to study three material models (linear elastic, hyperelastic and hyper foam). Two error treatment methods have been developed and implemented in the program and their feasibilities for different material systems were systematically investigated. FE models simulating the indentation process of three typical material behaviours have been developed and some important factors including mesh sensitivity, frictional conditions and material properties have been systematically studied to validate the FE models. The use of single indenter and dual indenters have been comparatively studied in terms of accuracy, convergence and robustness of the inverse program, which are important for materials characterisation. The program was evaluated with blind tests using numerical experimental data of known material properties. The validated method was then successfully used to study the properties of EVA foams for midsole of sport shoes and human heel pad using a newly develop continuous indentation testing system. Blind tests have been successfully used to establish the validity, efficiency and robustness of the program with different material models, error treatment and selection of indenters. The results showed that the double indenters method is better than the single indenter method, which is initial value dependent for some materials. The results also showed that the new random error treatment method is applicable to all the three material models while the converged results based on the program with constant error distribution was initial values dependent. The blind test results showed that Kalman filter is a feasible method and the random error treatment is more practical approach for characterisation material and can be universally applied to different materials models. Sensitivity test with perturbation in the indentation force demonstrated that the program is robust against potential experimental noise/errors. The framework established has been successfully used to characterise the properties of EVA foams in comparison with conventional compression, and compression-shear methods. The prediction from indentation tests showed comparable accuracy to the standard combined compression-shear tests, while pure compression could not predict the parameters accurately describing the material at complex situations. The parameters inversely predicted can be directly used in the product design and simulation process. A new in vivo test machine has been developed and performed on human subject with good accuracy and repeatability. The inverse method has been used to predict the elastic and nonlinear parameters of the heel pad. The predict elastic and hyperelastic properties showed good correlation for all the subjects tested. However the nonlinear model is more accurate, in describing the stiffening effect of the heel pad. This method would provide a practical way for detecting the property change of the heel pad with different conditions.