Physical models for numerical simulation of Si-based nanoscale FETs and sensors

To continuously improve the performance of metal-oxide-semiconductor field-effect-transistors (MOSFETs), innovative device architectures, gate stack engineering and mobility enhancement techniques are under investigation. In this framework, new physics-based models for Technology Computer-Aided-Design (TCAD) simulation tools are needed to accurately predict the performance of upcoming nanoscale devices and to provide guidelines for their optimization. In this thesis, advanced physically-based mobility models for ultrathin body (UTB) devices with either planar or vertical architectures such as single-gate silicon-on-insulator (SOI) field-effect transistors (FETs), double-gate FETs, FinFETs and silicon nanowire FETs, integrating strain technology and high-κ gate stacks are presented. The effective mobility of the two-dimensional electron/hole gas in a UTB FETs channel is calculated taking into account its tensorial nature and the quantization effects. All the scattering events relevant for thin silicon films and for high-κ dielectrics and metal gates have been addressed and modeled for UTB FETs on differently oriented substrates. The effects of mechanical stress on (100) and (110) silicon band structures have been modeled for a generic stress configuration. Performance will also derive from heterogeneity, coming from the increasing diversity of functions integrated on complementary metal-oxide-semiconductor (CMOS) platforms. For example, new architectural concepts are of interest not only to extend the FET scaling process, but also to develop innovative sensor applications. Benefiting from properties like large surface-to-volume ratio and extreme sensitivity to surface modifications, silicon-nanowire-based sensors are gaining special attention in research. In this thesis, a comprehensive analysis of the physical effects playing a role in the detection of gas molecules is carried out by TCAD simulations combined with interface characterization techniques. The complex interaction of charge transport in silicon nanowires of different dimensions with interface trap states and remote charges is addressed to correctly reproduce experimental results of recently fabricated gas nanosensors.

Design and Synthesis of Novel Kinase Inhibitors for the Treatment of Chronic Respiratory Diseases

In 2017, Chronic Respiratory Diseases accounted for almost four million deaths worldwide. Unfortunately, current treatments are not definitive for such diseases. This unmet medical need forces the scientific community to increase efforts in the identification of new therapeutic solutions. PI3K delta plays a key role in mechanisms that promote airway chronic inflammation underlying Asthma and COPD. The first part of this project was dedicated to the identification of novel PI3K delta inhibitors. A first SAR expansion of a Hit, previously identified by a HTS campaign, was carried out. A library of 43 analogues was synthesised taking advantage of an efficient synthetic approach. This allowed the identification of an improved Hit of nanomolar enzymatic potency and moderate selectivity for PI3K delta over other PI3K isoforms. However, this compound exhibited low potency in cell-based assays. Low cellular potency was related to sub optimal phys-chem and ADME properties. The analysis of the X-ray crystal structure of this compound in human PI3K delta guided a second tailored SAR expansion that led to improved cellular potency and solubility. The second part of the thesis was focused on the rational design and synthesis of new macrocyclic Rho-associated protein kinases (ROCKs) inhibitors. Inhibition of these kinases has been associated with vasodilating effects. Therefore, ROCKs could represent attractive targets for the treatment of pulmonary arterial hypertension (PAH). Known ROCK inhibitors suffer from low selectivity across the kinome. The design of macrocyclic inhibitors was considered a promising strategy to obtain improved selectivity. Known inhibitors from literature were evaluated for opportunities of macrocyclization using a knowledge-based approach supported by Computer Aided Drug Design (CADD). The identification of a macrocyclic ROCK inhibitor with enzymatic activity in the low micro molar range against ROCK II represented a promising result that validated this innovative approach in the design of new ROCKs inhibitors.

Development of a Computer-Based methodology for tolerance selection and optimization applied to the automotive sector

The design optimization of industrial products has always been an essential activity to improve product quality while reducing time-to-market and production costs. Although cost management is very complex and comprises all phases of the product life cycle, the control of geometrical and dimensional variations, known as Dimensional Management (DM), allows compliance with product and process requirements. Hence, the tolerance-cost optimization becomes the main practice to provide an effective application of Design for Tolerancing (DfT) and Design to Cost (DtC) approaches by enabling a connection between product tolerances and associated manufacturing costs. However, despite the growing interest in this topic, a profitable application in the industry of these techniques is hampered by their complexity: the definition of a systematic framework is the key element to improving design optimization, enhancing the concurrent use of Computer-Aided tools and Model-Based Definition (MBD) practices. The present doctorate research aims to define and develop an integrated methodology for product/process design optimization, to better exploit the new capabilities of advanced simulations and tools. By implementing predictive models and multi-disciplinary optimization, a Computer-Aided Integrated framework for tolerance-cost optimization has been proposed to allow the integration of DfT and DtC approaches and their direct application for the design of automotive components. Several case studies have been considered, with the final application of the integrated framework on a high-performance V12 engine assembly, to achieve both functional targets and cost reduction. From a scientific point of view, the proposed methodology provides an improvement for the tolerance-cost optimization of industrial components. The integration of theoretical approaches and Computer-Aided tools allows to analyse the influence of tolerances on both product performance and manufacturing costs. The case studies proved the suitability of the methodology for its application in the industrial field, providing the identification of further areas for improvement and refinement.

An evolutionary approach to the design of experiments for combinatorial optimization with an application to enzyme engineering

In a large number of problems the high dimensionality of the search space, the vast number of variables and the economical constrains limit the ability of classical techniques to reach the optimum of a function, known or unknown. In this thesis we investigate the possibility to combine approaches from advanced statistics and optimization algorithms in such a way to better explore the combinatorial search space and to increase the performance of the approaches. To this purpose we propose two methods: (i) Model Based Ant Colony Design and (ii) Naïve Bayes Ant Colony Optimization. We test the performance of the two proposed solutions on a simulation study and we apply the novel techniques on an appplication in the field of Enzyme Engineering and Design.

Design and implementation of a novel multi-constellation FPGA-based dual frequency GNSS receiver for space applications

The PhD activity described in the document is part of the Microsatellite and Microsystem Laboratory of the II Faculty of Engineering, University of Bologna. The main objective is the design and development of a GNSS receiver for the orbit determination of microsatellites in low earth orbit. The development starts from the electronic design and goes up to the implementation of the navigation algorithms, covering all the aspects that are involved in this type of applications. The use of GPS receivers for orbit determination is a consolidated application used in many space missions, but the development of the new GNSS system within few years, such as the European Galileo, the Chinese COMPASS and the Russian modernized GLONASS, proposes new challenges and offers new opportunities to increase the orbit determination performances. The evaluation of improvements coming from the new systems together with the implementation of a receiver that is compatible with at least one of the new systems, are the main activities of the PhD. The activities can be divided in three section: receiver requirements definition and prototype implementation, design and analysis of the GNSS signal tracking algorithms, and design and analysis of the navigation algorithms. The receiver prototype is based on a Virtex FPGA by Xilinx, and includes a PowerPC processor. The architecture follows the software defined radio paradigm, so most of signal processing is performed in software while only what is strictly necessary is done in hardware. The tracking algorithms are implemented as a combination of Phase Locked Loop and Frequency Locked Loop for the carrier, and Delay Locked Loop with variable bandwidth for the code. The navigation algorithm is based on the extended Kalman filter and includes an accurate LEO orbit model.

Elaborazione di un algoritmo di pianificazione di ricostruzioni del cavo orale, dell'orofaringe, della mandibola.

1.Ricostruzione mandibolare La ricostruzione mandibolare è comunemente eseguita utilizzando un lembo libero perone. Il metodo convenzionale (indiretto) di Computer Aided Design e Computer Aided Manifacturing prevede il modellamento manuale preoperatorio di una placca di osteosintesi standard su un modello stereolitografico della mandibola. Un metodo innovativo CAD CAM diretto comprende 3 fasi: 1) pianificazione virtuale 2) computer aided design della dima di taglio mandibolari, della dima di taglio del perone e della placca di osteosintesi e 3) Computer Aided Manufacturing dei 3 dispositivi chirurgici personalizzati. 7 ricostruzioni mandibolari sono state effettuate con il metodo diretto. I risultati raggiunti e le modalità di pianificazione sono descritte e discusse. La progettazione assistita da computer e la tecnica di fabbricazione assistita da computer facilita un'accurata ricostruzione mandibolare ed apporta un miglioramento statisticamente significativo rispetto al metodo convenzionale. 2. Cavità orale e orofaringe Un metodo ricostruttivo standard per la cavità orale e l'orofaringe viene descritto. 163 pazienti affetti da cancro della cavità orale e dell'orofaringe, sono stati trattati dal 1992 al 2012 eseguendo un totale di 175 lembi liberi. La strategia chirurgica è descritta in termini di scelta del lembo, modellamento ed insetting. I modelli bidimensionali sono utilizzati per pianificare una ricostruzione tridimensionale con il miglior risultato funzionale ed estetico. I modelli, la scelta del lembo e l' insetting sono descritti per ogni regione. Complicazioni e risultati funzionali sono stati valutati sistematicamente. I risultati hanno mostrato un buon recupero funzionale con le tecniche ricostruttive descritte. Viene proposto un algoritmo ricostruttivo basato su template standard.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.