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.

Assessment of Daily Life Mobility Levels using Wearable Inertial Sensors and Minimun Measured Input Models

Tracking activities during daily life and assessing movement parameters is essential for complementing the information gathered in confined environments such as clinical and physical activity laboratories for the assessment of mobility. Inertial measurement units (IMUs) are used as to monitor the motion of human movement for prolonged periods of time and without space limitations. The focus in this study was to provide a robust, low-cost and an unobtrusive solution for evaluating human motion using a single IMU. First part of the study focused on monitoring and classification of the daily life activities. A simple method that analyses the variations in signal was developed to distinguish two types of activity intervals: active and inactive. Neural classifier was used to classify active intervals; the angle with respect to gravity was used to classify inactive intervals. Second part of the study focused on extraction of gait parameters using a single inertial measurement unit (IMU) attached to the pelvis. Two complementary methods were proposed for gait parameters estimation. First method was a wavelet based method developed for the estimation of gait events. Second method was developed for estimating step and stride length during level walking using the estimations of the previous method. A special integration algorithm was extended to operate on each gait cycle using a specially designed Kalman filter. The developed methods were also applied on various scenarios. Activity monitoring method was used in a PRIN’07 project to assess the mobility levels of individuals living in a urban area. The same method was applied on volleyball players to analyze the fitness levels of them by monitoring their daily life activities. The methods proposed in these studies provided a simple, unobtrusive and low-cost solution for monitoring and assessing activities outside of controlled environments.

Statistical Physics and Modeling of Human Mobility

In this thesis, we extend some ideas of statistical physics to describe the properties of human mobility. By using a database containing GPS measures of individual paths (position, velocity and covered space at a spatial scale of 2 Km or a time scale of 30 sec), which includes the 2% of the private vehicles in Italy, we succeed in determining some statistical empirical laws pointing out "universal" characteristics of human mobility. Developing simple stochastic models suggesting possible explanations of the empirical observations, we are able to indicate what are the key quantities and cognitive features that are ruling individuals' mobility. To understand the features of individual dynamics, we have studied different aspects of urban mobility from a physical point of view. We discuss the implications of the Benford's law emerging from the distribution of times elapsed between successive trips. We observe how the daily travel-time budget is related with many aspects of the urban environment, and describe how the daily mobility budget is then spent. We link the scaling properties of individual mobility networks to the inhomogeneous average durations of the activities that are performed, and those of the networks describing people's common use of space with the fractional dimension of the urban territory. We study entropy measures of individual mobility patterns, showing that they carry almost the same information of the related mobility networks, but are also influenced by a hierarchy among the activities performed. We discover that Wardrop's principles are violated as drivers have only incomplete information on traffic state and therefore rely on knowledge on the average travel-times. We propose an assimilation model to solve the intrinsic scattering of GPS data on the street network, permitting the real-time reconstruction of traffic state at a urban scale.