Magnetooptical properties of dilute nitride nanowires

Abstract

Nanotrådar av utspädda nitrider för framtidens optoelektronikNanotrådar gjorda av utspädda nitrider är ett nytt och spännande material som kan bli användbart i framtidens optoelektronik. Sådana nanotrådar kan nämligen lysa och absorbera ljus mycket effektivt, och i många olika färger. De ger dessutom upphov till exotiska nanostrukturer, så kallade kvantprickar, som spontant bildas i nanotrådarna. PrefaceThis thesis summarizes the outcome of Mattias Jansson's doctoral studies, conducted between 2015-2020 at the Department of Physics, Chemistry and Biology at Linköping University. The thesis is divided into two parts: The first part contains an introduction to the research field and of the experimental techniques used in this work. The second part contains the main research results, presented in seven scientific papers. ix x Preface AcknowledgementsFirst and foremost, to Irina Buyanova and Weimin Chen, thank you for hiring me as a PhD student in your research group. I cannot emphasize enough how lucky I feel to have ended up here. Thank you for pushing me to become a better experimentalist and researcher.To Jan Stehr, thank you for your guidance, support and inspiration, both inside and outside of the lab. And, of course, thank you for teaching me how to perform ODRM measurements (in 120 easy steps). I want to thank all past and present colleagues in the Functional Electronic Materials group for the support and inspiration you have provided. Especially, I wish to thank Yuttapoom Puttisong, Shula Chen, Stanislav Filippov and Yuqing Huang, for your support and guidance in the lab from day one, and for the inspiration you have given me in how to be a good researcher. Stanislav, thank you for introducing me to the µPL technique and the dilute nitride nanowires. To Roman Balagula, thank you for the fun and interesting times in the lab and during the office discussions, and for your helpful tips regarding the measurements. Thank you also for sharing the mixed excitement and frustration over the polarization measurements. To Bin Zhang, thank you for the collaborations in measuring the GaBiAs nanowires, and for your often difficult but always interesting questions and discussions. Till Pontus Höjer, tack för de intressanta diskussionerna vi haft på labbet, och över lunchen. Förlåt för att jag klev på ditt prov.To Professors Fumitaro Ishikawa at Ehime University and Charles Tu at University of California, San Diego, I direct my deep thanks for providing such excellent samples. I want to thank Professor Anna Fontcuberta i Morral and Luca Francaviglia at EPFL, for your collaboration involving the CL measurements. I also want to thank Thomas Lingefelt for your technical assistance with the electron microscopes, and for teaching me how to use the SEM, and Justinas Palisaitis for your help with the TEM measurements.I want to thank Plamen Paskov for allowing me to use the photon correlation setup, and for having me as a lab assistant in your course, where I learned a lot. Till Martin Eriksson, tack för all ovärderlig hjälp du get...

Research Scenarios of Autonomous Vehicles, the Sensors and Measurement Systems Used in Experiments

“…− emergency braking before an obstacle [34,71,72], where the aim is to minimize the risk of collision; − avoiding a suddenly appearing obstacle in a critical situation where it is not possible to stop the AV [34,72]; − 2D object detection, localization, and tracking: car detection [70,73,74] and distance estimation to other vehicles [27,75]; pedestrian detection [20,21,69]; bicyclist/motorcyclist detection [20,73,74].…”

“…− 3D object detection on the road [72,76] using lidar and sensor fusion (e.g., lidar and camera), where the aim is to obtain information about the obstacle in the spatial coordinate system: car detection [19,75,77]; pedestrian detection [23,24,78]; bicyclist/motorcyclist detection [23,25,73]; cone detection [67]; detection of walls, trees, bushes, and poles [78]; detection and tracking of unknown objects [79].…”

“…The sensors of the perception systems are mainly vision sensors (cameras) recording RGB images [31,32,70], radars using radio waves [55,57,71], and lidars based on laser beams [24,25,33]. Most solutions based on vision sensors use single-lens cameras that provide a 2D image for the road situation analysis system [24,32,72]. There are also vehicles and systems that use stereo cameras [31,67,74].…”

“…The advantage of this type of sensor is their operation at high driving velocities, especially over long ranges. The maximum range of the radar can be over 150 m [72] (at a maximum of 25 m for the Stereolabs ZED camera [31,96]). The disadvantage of radars compared to cameras is the small amount of information acquired about the spatial dimensions of objects.…”

“…There are separate scenarios for the detection and localization of objects in 3D space, based on the sensory fusion of several perception sensors (cameras with lidars or radars) [24,67,77]. Cameras are used in emergency braking systems and collision warning systems about an obstacle (e.g., in front of another vehicle, pedestrian, or bicyclist) [51,56,72]. They are also used for issues related to monitoring and warning about objects in the blind spot of the vehicle [44].…”