Numerical Modelling of Graphene Nanoribbon-fets for Analog and Digital Applications

Graphene, that is a monolayer of carbon atoms arranged in a honeycomb lattice, has been isolated only recently from graphite. This material shows very attractive physical properties, like superior carrier mobility, current carrying capability and thermal conductivity. In consideration of that, graphene has been the object of large investigation as a promising candidate to be used in nanometer-scale devices for electronic applications. In this work, graphene nanoribbons (GNRs), that are narrow strips of graphene, for which a band-gap is induced by the quantum confinement of carriers in the transverse direction, have been studied. As experimental GNR-FETs are still far from being ideal, mainly due to the large width and edge roughness, an accurate description of the physical phenomena occurring in these devices is required to have valuable predictions about the performance of these novel structures. A code has been developed to this purpose and used to investigate the performance of 1 to 15-nm wide GNR-FETs. Due to the importance of an accurate description of the quantum effects in the operation of graphene devices, a full-quantum transport model has been adopted: the electron dynamics has been described by a tight-binding (TB) Hamiltonian model and transport has been solved within the formalism of the non-equilibrium Green's functions (NEGF). Both ballistic and dissipative transport are considered. The inclusion of the electron-phonon interaction has been taken into account in the self-consistent Born approximation. In consideration of their different energy band-gap, narrow GNRs are expected to be suitable for logic applications, while wider ones could be promising candidates as channel material for radio-frequency applications.

[In search of] Digital biomarkers for objective pain assessment

Pain is a highly complex phenomenon involving intricate neural systems, whose interactions with other physiological mechanisms are not fully understood. Standard pain assessment methods, relying on verbal communication, often fail to provide reliable and accurate information, which poses a critical challenge in the clinical context. In the era of ubiquitous and inexpensive physiological monitoring, coupled with the advancement of artificial intelligence, these new tools appear as the natural candidates to be tested to address such a challenge. This thesis aims to conduct experimental research to develop digital biomarkers for pain assessment. After providing an overview of the state-of-the-art regarding pain neurophysiology and assessment tools, methods for appropriately conditioning physiological signals and controlling confounding factors are presented. The thesis focuses on three different pain conditions: cancer pain, chronic low back pain, and pain experienced by patients undergoing neurorehabilitation. The approach presented in this thesis has shown promise, but further studies are needed to confirm and strengthen these results. Prior to developing any models, a preliminary signal quality check is essential, along with the inclusion of personal and health information in the models to limit their confounding effects. A multimodal approach is preferred for better performance, although unimodal analysis has revealed interesting aspects of the pain experience. This approach can enrich the routine clinical pain assessment procedure by enabling pain to be monitored when and where it is actually experienced, and without the involvement of explicit communication,. This would improve the characterization of the pain experience, aid in antalgic therapy personalization, and bring timely relief, with the ultimate goal of improving the quality of life of patients suffering from pain.