Scaled down physical properties of semiconductor nanowires for nanoelectronics scaling up

Semiconductor nanowires (NWs) are one- or quasi one-dimensional systems whose physical properties are unique as compared to bulk materials because of their nanoscaled sizes. They bring together quantum world and semiconductor devices. NWs-based technologies may achieve an impact comparable to that of current microelectronic devices if new challenges will be faced. This thesis primarily focuses on two different, cutting-edge aspects of research over semiconductor NW arrays as pivotal components of NW-based devices. The first part deals with the characterization of electrically active defects in NWs. It has been elaborated the set-up of a general procedure which enables to employ Deep Level Transient Spectroscopy (DLTS) to probe NW arrays’ defects. This procedure has been applied to perform the characterization of a specific system, i.e. Reactive Ion Etched (RIE) silicon NW arrays-based Schottky barrier diodes. This study has allowed to shed light over how and if growth conditions introduce defects in RIE processed silicon NWs. The second part of this thesis concerns the bowing induced by electron beam and the subsequent clustering of gallium arsenide NWs. After a justified rejection of the mechanisms previously reported in literature, an original interpretation of the electron beam induced bending has been illustrated. Moreover, this thesis has successfully interpreted the formation of NW clusters in the framework of the lateral collapse of fibrillar structures. These latter are both idealized models and actual artificial structures used to study and to mimic the adhesion properties of natural surfaces in lizards and insects (Gecko effect). Our conclusion are that mechanical and surface properties of the NWs, together with the geometry of the NW arrays, play a key role in their post-growth alignment. The same parameters open, then, to the benign possibility of locally engineering NW arrays in micro- and macro-templates.

Path planning for image based visual servoing

This thesis deals with Visual Servoing and its strictly connected disciplines like projective geometry, image processing, robotics and non-linear control. More specifically the work addresses the problem to control a robotic manipulator through one of the largely used Visual Servoing techniques: the Image Based Visual Servoing (IBVS). In Image Based Visual Servoing the robot is driven by on-line performing a feedback control loop that is closed directly in the 2D space of the camera sensor. The work considers the case of a monocular system with the only camera mounted on the robot end effector (eye in hand configuration). Through IBVS the system can be positioned with respect to a 3D fixed target by minimizing the differences between its initial view and its goal view, corresponding respectively to the initial and the goal system configurations: the robot Cartesian Motion is thus generated only by means of visual informations. However, the execution of a positioning control task by IBVS is not straightforward because singularity problems may occur and local minima may be reached where the reached image is very close to the target one but the 3D positioning task is far from being fulfilled: this happens in particular for large camera displacements, when the the initial and the goal target views are noticeably different. To overcame singularity and local minima drawbacks, maintaining the good properties of IBVS robustness with respect to modeling and camera calibration errors, an opportune image path planning can be exploited. This work deals with the problem of generating opportune image plane trajectories for tracked points of the servoing control scheme (a trajectory is made of a path plus a time law). The generated image plane paths must be feasible i.e. they must be compliant with rigid body motion of the camera with respect to the object so as to avoid image jacobian singularities and local minima problems. In addition, the image planned trajectories must generate camera velocity screws which are smooth and within the allowed bounds of the robot. We will show that a scaled 3D motion planning algorithm can be devised in order to generate feasible image plane trajectories. Since the paths in the image are off-line generated it is also possible to tune the planning parameters so as to maintain the target inside the camera field of view even if, in some unfortunate cases, the feature target points would leave the camera images due to 3D robot motions. To test the validity of the proposed approach some both experiments and simulations results have been reported taking also into account the influence of noise in the path planning strategy. The experiments have been realized with a 6DOF anthropomorphic manipulator with a fire-wire camera installed on its end effector: the results demonstrate the good performances and the feasibility of the proposed approach.

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.

Functional engineering of hybrid heterostructures for application in electronic, optical and optoelectronic nanostructured devices

Interfacing materials with different intrinsic chemical-physical characteristics allows for the generation of a new system with multifunctional features. Here, this original concept is implemented for tailoring the functional properties of bi-dimensional black phosphorus (2D bP or phosphorene) and organic light-emitting transistors (OLETs). Phosphorene is highly reactive under atmospheric conditions and its small-area/lab-scale deposition techniques have hampered the introduction of this material in real-world applications so far. The protection of 2D bP against the oxygen by means of functionalization with alkane molecules and pyrene derivatives, showed long-term stability with respect to the bare 2D bP by avoiding remarkable oxidation up to 6 months, paving the way towards ultra-sensitive oxygen chemo-sensors. A new approach of deposition-precipitation heterogeneous reaction was developed to decorate 2D bP with Au nanoparticles (NP)s, obtaining a “stabilizer-free” that may broaden the possible applications of the 2D bP/Au NPs interface in catalysis and biodiagnostics. Finally, 2D bP was deposited by electrospray technique, obtaining oxidized-phosphorous flakes as wide as hundreds of µm2 and providing for the first time a phosphorous-based bidimensional system responsive to electromechanical stimuli. The second part of the thesis focuses on the study of organic heterostructures in ambipolar OLET devices, intriguing optoelectronic devices that couple the micro-scaled light-emission with electrical switching. Initially, an ambipolar single-layer OLET based on a multifunctional organic semiconductor, is presented. The bias-depending light-emission shifted within the transistor channel, as expected in well-balanced ambipolar OLETs. However, the emitted optical power of the single layer-based device was unsatisfactory. To improve optoelectronic performance of the device, a multilayer organic architecture based on hole-transporting semiconductor, emissive donor-acceptor blend and electron-transporting semiconductor was optimized. We showed that the introduction of a suitable electron-injecting layer at the interface between the electron-transporting and light-emission layers may enable a ≈ 2× improvement of efficiency at reduced applied bias.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

Low-molecular-weight gels: from the rational design to the application of versatile soft materials

Low-molecular-weight (LMW) gels are a versatile class of soft materials that gained increasing interest over the last few decades. They are made of a small percentage, often lower than 1.0 %, of organic molecules called gelators, dispersed in a liquid medium. Such molecules have a molecular weight usually lower than 1 kDa. The gelator molecules start to interact after the addition of a trigger, and form fibres, whose entanglement traps the solvent through capillary forces. A plethora of LMW gelators have been designed, including short peptides. Such gelators present several advantages: the synthesis is easy and can be easily scaled up; they are usually biocompatible and biodegradable; the gelation phenomenon can be rationalised by making small variation on the peptide scaffold; they find application in several fields. In this thesis, an overview of several peptide based LMW gels is presented. In each study, the gelation conditions were carefully studied, and the final materials were thoroughly investigated. First, the gelation ability of a fluorinated phenylalanine was assessed, to understand how the presence of a rigid moiety and the presence of fluorine may influence the gelation. In this context, a method for the dissolution of sensitive gelators was studied. Then, the control over the gel formation was studied both over time and space, taking advantage of either the pH-annealing of the gel or the reaction-diffusion of a hydrolysing reagent. Some gels were probed for various applications. Due to their ability of trapping water and organic solvents, we used gels for trapping pollutants dissolved in water, as well as a medium for the controlled release of either fragrances or bioactive compounds. Finally, the interaction of the gel matrix with a light-responsive molecule was assessed to understand wether the gel properties or the interaction of the additive with light were affected.