TCAD approaches to multidimensional simulation of advanced semiconductor devices

Technology scaling increasingly emphasizes complexity and non-ideality of the electrical behavior of semiconductor devices and boosts interest on alternatives to the conventional planar MOSFET architecture. TCAD simulation tools are fundamental to the analysis and development of new technology generations. However, the increasing device complexity is reflected in an augmented dimensionality of the problems to be solved. The trade-off between accuracy and computational cost of the simulation is especially influenced by domain discretization: mesh generation is therefore one of the most critical steps and automatic approaches are sought. Moreover, the problem size is further increased by process variations, calling for a statistical representation of the single device through an ensemble of microscopically different instances. The aim of this thesis is to present multi-disciplinary approaches to handle this increasing problem dimensionality in a numerical simulation perspective. The topic of mesh generation is tackled by presenting a new Wavelet-based Adaptive Method (WAM) for the automatic refinement of 2D and 3D domain discretizations. Multiresolution techniques and efficient signal processing algorithms are exploited to increase grid resolution in the domain regions where relevant physical phenomena take place. Moreover, the grid is dynamically adapted to follow solution changes produced by bias variations and quality criteria are imposed on the produced meshes. The further dimensionality increase due to variability in extremely scaled devices is considered with reference to two increasingly critical phenomena, namely line-edge roughness (LER) and random dopant fluctuations (RD). The impact of such phenomena on FinFET devices, which represent a promising alternative to planar CMOS technology, is estimated through 2D and 3D TCAD simulations and statistical tools, taking into account matching performance of single devices as well as basic circuit blocks such as SRAMs. Several process options are compared, including resist- and spacer-defined fin patterning as well as different doping profile definitions. Combining statistical simulations with experimental data, potentialities and shortcomings of the FinFET architecture are analyzed and useful design guidelines are provided, which boost feasibility of this technology for mainstream applications in sub-45 nm generation integrated circuits.

Assessing poverty with survey data. Uni-dimensional, multidimensional and resilience poverty analysis in Kenya

Traditionally Poverty has been measured by a unique indicator, income, assuming this was the most relevant dimension of poverty. Sen’s approach has dramatically changed this idea shedding light over the existence of many more dimensions and over the multifaceted nature of poverty; poverty cannot be represented by a unique indicator that only can evaluate a specific aspect of poverty. This thesis tracks an ideal path along with the evolution of the poverty analysis. Starting from the unidimensional analysis based on income and consumptions, this research enter the world of multidimensional analysis. After reviewing the principal approaches, the Foster and Alkire method is critically analyzed and implemented over data from Kenya. A step further is moved in the third part of the thesis, introducing a new approach to multidimensional poverty assessment: the resilience analysis.

Multidimensional Measures of Firm Competitiveness: a Model-Based Approach

The concept of competitiveness, for a long time considered as strictly connected to economic and financial performances, evolved, above all in recent years, toward new, wider interpretations disclosing its multidimensional nature. The shift to a multidimensional view of the phenomenon has excited an intense debate involving theoretical reflections on the features characterizing it, as well as methodological considerations on its assessment and measurement. The present research has a twofold objective: going in depth with the study of tangible and intangible aspect characterizing multidimensional competitive phenomena by assuming a micro-level point of view, and measuring competitiveness through a model-based approach. Specifically, we propose a non-parametric approach to Structural Equation Models techniques for the computation of multidimensional composite measures. Structural Equation Models tools will be used for the development of the empirical application on the italian case: a model based micro-level competitiveness indicator for the measurement of the phenomenon on a large sample of Italian small and medium enterprises will be constructed.

A projection into the ideas and technologies of "non-place" and "space of flow" in urban context

The meaning of a place has been commonly assigned to the quality of having root (rootedness) or sense of belonging to that setting. While on the contrary, people are nowadays more concerned with the possibilities of free moving and networks of communication. So, the meaning, as well as the materiality of architecture has been dramatically altered with these forces. It is therefore of significance to explore and redefine the sense and the trend of architecture at the age of flow. In this dissertation, initially, we review the gradually changing concept of "place-non-place" and its underlying technological basis. Then we portray the transformation of meaning of architecture as influenced by media and information technology and advanced methods of mobility, in the dawn of 21st century. Against such backdrop, there is a need to sort and analyze architectural practices in response to the triplet of place-non-place and space of flow, which we plan to achieve conclusively. We also trace the concept of flow in the process of formation and transformation of old cities. As a brilliant case study, we look at Persian Bazaar from a socio-architectural point of view. In other word, based on Robert Putnam's theory of social capital, we link social context of the Bazaar with architectural configuration of cities. That is how we believe "cities as flow" are not necessarily a new paradigm.

Multidimensional analysis of complex networks

Complex Networks analysis turn out to be a very promising field of research, testified by many research projects and works that span different fields. Those analysis have been usually focused on characterize a single aspect of the system and a study that considers many informative axes along with a network evolve is lacking. We propose a new multidimensional analysis that is able to inspect networks in the two most important dimensions, space and time. To achieve this goal, we studied them singularly and investigated how the variation of the constituting parameters drives changes to the network as a whole. By focusing on space dimension, we characterized spatial alteration in terms of abstraction levels. We proposed a novel algorithm that, by applying a fuzziness function, can reconstruct networks under different level of details. We verified that statistical indicators depend strongly on the granularity with which a system is described and on the class of networks. We keep fixed the space axes and we isolated the dynamics behind networks evolution process. We detected new instincts that trigger social networks utilization and spread the adoption of novel communities. We formalized this enhanced social network evolution by adopting special nodes (called sirens) that, thanks to their ability to attract new links, were able to construct efficient connection patterns. We simulated the dynamics of the system by considering three well-known growth models. Applying this framework to real and synthetic networks, we showed that the sirens, even when used for a limited time span, effectively shrink the time needed to get a network in mature state. In order to provide a concrete context of our findings, we formalized the cost of setting up such enhancement and provided the best combinations of system's parameters, such as number of sirens, time span of utilization and attractiveness.

Multidimensional item response theory models with general and specific latent traits for ordinal data

The aim of the thesis is to propose a Bayesian estimation through Markov chain Monte Carlo of multidimensional item response theory models for graded responses with complex structures and correlated traits. In particular, this work focuses on the multiunidimensional and the additive underlying latent structures, considering that the first one is widely used and represents a classical approach in multidimensional item response analysis, while the second one is able to reflect the complexity of real interactions between items and respondents. A simulation study is conducted to evaluate the parameter recovery for the proposed models under different conditions (sample size, test and subtest length, number of response categories, and correlation structure). The results show that the parameter recovery is particularly sensitive to the sample size, due to the model complexity and the high number of parameters to be estimated. For a sufficiently large sample size the parameters of the multiunidimensional and additive graded response models are well reproduced. The results are also affected by the trade-off between the number of items constituting the test and the number of item categories. An application of the proposed models on response data collected to investigate Romagna and San Marino residents' perceptions and attitudes towards the tourism industry is also presented.