Studio di leds con diverse densita di dislocazioni tramite l'analisi del coefficiente di poole-frenkel

E' stato considerato un High-Dislocation Density Light Emitting Diode (HDD LED)ed è stato analizzato l'andamento di corrente a varie temperature. Dai risultati ottenuti è stato possibile ricavare il coefficiente di Poole-Frenkel, e da esso risalire alla densità di dislocazioni del dispositivo.

Morphology of leds by atomic force microscopy

The concern of this work is to present the characterization of blue emitting GaN-based LED structures by means of Atomic Force Microscopy. Here we show a comparison among the samples with different dislocation densities, in order to understand how the dislocations can affect the surface morphology. First of all we have described the current state of art of the LEDs in the present market. Thereafterwards we have mentioned in detail about the growth technique of LED structures and the methodology of the characterization employed in our thesis. Finally, we have presented the details of the results obtained on our samples studied, followed by discussions and conclusions. L'obiettivo di questa tesi é quello di presentare la caratterizzazione mediante Microscopia a Forza Atomica di strutture di LED a emissione di luce blu a base di nitruro di gallio (GaN). Viene presentato un confronto tra campioni con differente densità di dislocazioni, allo scopo di comprendere in che modo la presenza di dislocazioni influisce sulla morfologia della superficie. Innanzitutto, viene descritto il presente stato dell'arte dei LED. Successivamente, sono forniti i dettagli riguardanti la tecnica di crescita delle strutture dei LED e il metodo di caratterizzazione adottato. Infine, vengono mostrati e discussi i risultati ottenuti dallo studio dei campioni, seguiti dalle conclusioni.

Model-Based validation of Driver Drowsiness Detection System for ADAS

The work described in this Master’s Degree thesis was born after the collaboration with the company Maserati S.p.a, an Italian luxury car maker with its headquarters located in Modena, in the heart of the Italian Motor Valley, where I worked as a stagiaire in the Virtual Engineering team between September 2021 and February 2022. This work proposes the validation using real-world ECUs of a Driver Drowsiness Detection (DDD) system prototype based on different detection methods with the goal to overcome input signal losses and system failures. Detection methods of different categories have been chosen from literature and merged with the goal of utilizing the benefits of each of them, overcoming their limitations and limiting as much as possible their degree of intrusiveness to prevent any kind of driving distraction: an image processing-based technique for human physical signals detection as well as methods based on driver-vehicle interaction are used. A Driver-In-the-Loop simulator is used to gather real data on which a Machine Learning-based algorithm will be trained and validated. These data come from the tests that the company conducts in its daily activities so confidential information about the simulator and the drivers will be omitted. Although the impact of the proposed system is not remarkable and there is still work to do in all its elements, the results indicate the main advantages of the system in terms of robustness against subsystem failures and signal losses.