Macroeconomic forecasting is a classic problem, today most often modeled using time series analysis. Few attempts have been made using machine learning methods, and even fewer incorporating unconventional data, such as that from social media. In this thesis, a Generative Adversarial Network (GAN) is used to predict U.S. unemployment, beating the ARIMA benchmark on all horizons. Furthermore, attempts at using Twitter data and the Natural Language Processing (NLP) model DistilBERT are performed. While these attempts do not beat the benchmark, they do show promising results with predictive power. The models are also tested at predicting the U.S. stock index S&P 500. For these models, the Twitter data does improve the accuracy and shows the potential of social media data when predicting a more erratic index with less seasonality that is more responsive to current trends in public discourse. The results also show that Twitter data can be used to predict trends in both unemployment and the S&P 500 index. This sets the stage for further research into NLP-GAN models for macroeconomic predictions using social media data. / Makroekonomiska prognoser är sedan länge en svår utmaning. Idag löses de oftast med tidsserieanalys och få försök har gjorts med maskininlärning. I denna uppsats används ett generativt motstridande nätverk (GAN) för att förutspå amerikansk arbetslöshet, med resultat som slår samtliga riktmärken satta av en ARIMA. Ett försök görs också till att använda data från Twitter och den datorlingvistiska (NLP) modellen DistilBERT. Dessa modeller slår inte riktmärkena men visar lovande resultat. Modellerna testas vidare på det amerikanska börsindexet S&P 500. För dessa modeller förbättrade Twitterdata resultaten vilket visar på den potential data från sociala medier har när de appliceras på mer oregelbunda index, utan tydligt säsongsberoende och som är mer känsliga för trender i det offentliga samtalet. Resultaten visar på att Twitterdata kan användas för att hitta trender i både amerikansk arbetslöshet och S&P 500 indexet. Detta lägger grunden för fortsatt forskning inom NLP-GAN modeller för makroekonomiska prognoser baserade på data från sociala medier.

In der Arbeit wurden die zwei ternären Oxide GdScO3 und AlTiOx strukturell und elektrisch charakterisiert und in laterale AlGaN/GaN-MISHEMTs integriert. GdScO3 wächst hexagonal und epitaktisch bei einer Abscheidung mittels PLD bei 700°C auf einer AlGaN/GaN Heterostruktur auf. Die demonstrierten MISHEMTs zeigen einen deutlich verringerten Gate-Leckstrom. Zeit- und beleuchtungsabhängige Drain-Strom Messungen im ausgeschaltetem Transistor weisen allerdings auf photoinduzierte Trapzustände mit langer Lebensdauer im Oxid hin, die den Drain-Leckstrom limitieren. Die AlTiOx Mischoxide wurden mittels ALD abgeschieden. Dabei wurde die Stöchiometrie über das Zyklenverhältnis zwischen Al2O3 und TiO2 variiert. Es konnte gezeigt werden, dass der Brechungsindex, die Permittivität, die Bandlücke und das Bandalignment zum GaN über die Stöchiometrie eingestellt werden können. Durch die Implementierung eines high-k last Prozesses konnten schaltbare MISHEMTs prozessiert werden. Durch die Simulation der Bandstruktur konnten die Einsatzspannungsverschiebung und ein Maximum des Drain-Stroms im ausgeschaltetem Zustand über die Ermittlung der Barrierendicke für Elektronen erklärt werden. Für eine Passivierung mit TiO2 wurde ein um 2,5 Größenordnungen reduzierter Drain-Leckstrom bei gleichzeitig nur minimal verschobener Einsatzspannung gemessen.:Inhaltsverzeichnis 1 Einleitung 7 2 Grundlagen 9 2.1 Der III-V Halbleiter Galiumnitrid . . . . . . . . . . . . . . . . . . . . . 9 2.2 Der Hetero-Feldeffekttransisor . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Performance Einschränkungen am unpassivierten HFET . . . . . . . . 14 2.4 Gatedielektrika für MISHEMTs . . . . . . . . . . . . . . . . . . . . . . 17 2.4.1 Verwendete Dielektrika . . . . . . . . . . . . . . . . . . . . . . . 19 2.4.2 Limitationen in MISHEMTs . . . . . . . . . . . . . . . . . . . . 23 2.5 Atomlagenabscheidung . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5.1 Der ALD-Prozess . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5.2 Abscheidung ternärer Verbindungen . . . . . . . . . . . . . . . . 27 3 Charakterisierungsmethoden 31 3.1 Kapazitäts-Spannungs-Messungen an MIS-Kondensatoren . . . . . . . . 31 3.2 Photo-assisted Kapazitäts-Spannungsmessungen . . . . . . . . . . . . . 34 3.3 Messungen am Transistor . . . . . . . . . . . . . . . . . . . . . . . . . 35 4 Probenherstellung 39 4.1 Atomlagenabscheidung . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.2 Prozessoptimierung am HFET . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.1 Mesa-Ätzung . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.2.2 Formierung der ohmschen Kontakte . . . . . . . . . . . . . . . . 47 4.3 Strukturierung der Oxide . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5 Gadoliniumscandiumoxid 53 5.1 Strukturelle Charakterisierung . . . . . . . . . . . . . . . . . . . . . . . 53 5.2 PhotoCV-Messungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.3 MISHEMT mit GdScO3 . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.4 Zusammenfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6 Aluminium-Titanoxid Mischschichten 65 6.1 Voruntersuchungen am TiO2 . . . . . . . . . . . . . . . . . . . . . . . . 65 6.2 Strukturelle Charakterisierung an AlTiOx . . . . . . . . . . . . . . . . 67 6.2.1 Stöchiometrie . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.2.2 Kristallisationsverhalten . . . . . . . . . . . . . . . . . . . . . . 70 6.3 Bestimmung des Bandalignments . . . . . . . . . . . . . . . . . . . . . 72 6.3.1 UV/Vis Messungen . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.3.2 Röntgenphotoelektronenspektroskopie . . . . . . . . . . . . . . . 74 6.3.3 Bandalignment zum GaN . . . . . . . . . . . . . . . . . . . . . 77 6 Inhaltsverzeichnis 6.4 Elektrische Charakterisierung . . . . . . . . . . . . . . . . . . . . . . . 79 6.4.1 CV-Messungen . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.4.2 IV-Messungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.4.3 PhotoCV-Messungen . . . . . . . . . . . . . . . . . . . . . . . . 82 6.5 Zusammenfassung der AlTiOx Charakterisierung . . . . . . . . . . . . . 86 6.6 MISHEMTs mit AlTiOx . . . . . . . . . . . . . . . . . . . . . . . . . . 88 6.6.1 high-k first MISHEMTs . . . . . . . . . . . . . . . . . . . . . . . 88 6.6.2 High-k last MISHEMTs . . . . . . . . . . . . . . . . . . . . . . . 89 6.7 Einordnung der Transistorergebnisse . . . . . . . . . . . . . . . . . . . 94 7 Zusammenfassung 99 Anhang 103 Abkürzungsverzeichnis 111 Symbolverzeichnis 113 Abbildungsverzeichnis 115 Tabellenverzeichnis 121 Literatur 123 Publikationen 141 Danksagung 143

High electron mobility transistors (HEMTs) realized using AlxGa1-xN/GaN are relatively new technology which is prominent for high-speed and high-power applications. Some of the main problems with this technology were identified as dynamic RDSon, current collapse and threshold voltage instabilities due to the off-state stress. What was less investigated is the effect of the off-state stress on the leakage current at lower voltages. In this work, multiple devices with various initial leakage currents are stressed at different stress conditions (drain voltage, temperature, duration) and the development of drain-source on-state resistance (RDSon), threshold voltage (Vth) and drain-source leakage current (IDSS) after stress are tracked. It was found out that devices with initially higher leakage exhibit higher RDSon and Vth before stress, which simulations attributed to the higher Al mole fraction in the back-barrier or less unintentional doping in the channel layer. During the off-state stress (VDS = 900 V), the leakage current shortly rises and then sharply drops, presumably because of the charge redistribution in the back-barrier. After the stress, no larger changes were observed for RDSon and Vth , but they were for the leakage current i.e., initially low leakage devices had post-stress leakage increase, while initially high leakage devices had post-stress leakage decrease. This is assumed to be caused by the charge redistribution. Parasitic capacitance measurements showed the rise of the pre-stress input, output and reverse transfer capacitances with the pre-stress leakage, which could presumably be explained by higher Al mole fraction inducing more charges in the channel layer, deeming higher Al mole fraction in the back-barrier as a main assumed cause for all the observed effects. After the stress, capacitance changes were tentatively explained by the charge redistribution in the back-barrier. Finally, high temperature was shown to significantly reduce the observed long time to recovery. However, more measurements are needed to further observe this influence. Additionally, more experiments (e.g., on wafer, G-ω measurement, etc.) are needed in general to further investigate the mechanisms behind these memory effects. / Transistorer med extra hög elektronmobilitet, sk HEMT-ar (high electron mobility transistor) kan fabriceras med hjälp av materialen galliumnitrid (GaN) och aluminium-galliumnitrid (AlxGa1-xN). GaN HEMT-ar lämpar sig mycket väl för kraftkomponenter för höga frekvenser. Några av problemen med denna nya teknologi är hög resistans mellan ”drain” och ”source”, RDSon, reduktion av mättnadsströmmen (”current collapse”) och instabiliteter hos styrets tröskelspänning (Vth) när komponenterna stressas i avslaget tillstånd. Mindre känt är effekterna av stress för en avslagen komponent på läckströmmen (IDSS) vid låga påkänningar. I detta arbete har komponenter med varierande läckströmmar utsatts för olika typer av stress, t ex drain-spänning och höjd temperatur under olika långa tider samtidigt som parametrarna RDSon, Vth och IDSS har uppmätts. Det kunde noteras att komponenter med initialt högre läckströmmar hade högre RDSon och Vth innan de utsattes för stress, vilket med hjälp av simuleringar kunde visas bero på högre Al-molfraktion i skiktet under AlxGa1-xN-lagret, alternativt lägre dopning i kanal-lagret. Vid stress (VDS = 900 V) med komponenterna i avslaget tillstånd ökade läckströmmen kortvarigt för att sedan minska, troligtvis beroende på att laddningar omfördelas i lagret under kanal-skiktet. Efter stress sågs inte några större förändringar hos RDSon och Vth , men förändringar syntes på läckströmmen: komponenter med initialt låga läckströmmar visade ökning av läckströmmen efter stress, medan komponenter med initialt höga läckströmmar visade en post-stress minskning av läckström. Detta antas bero på den nämnda omfördelningen av laddningar djupare ner i komponenterna. Mätningar av parasitisk kapacitans visade på en ökning av pre-stress in- och ut-kapacitanserna (Ciss, Coss) samt ”gate-drain”-kapacitansen som funktion av pre-stress läckströmmar. Detta kan möjligtvis förklaras av att en högre andel Al inducerar mer laddningar i kanalen, vilket indikerar att högre Al-molfraktion i skiktet under kanal-lagret är orsaken till många av de effekter som setts. Kapacitansförändringar efter stress förklaras troligen av att laddningarna i detta lager återgår. Till sist noteras också att hög temperatur tydligt reducerade den långa tiden som komponenterna behövde för att återhämta sig. Det var också tydligt att mer mätningar är nödvändiga för att ytterligare säkerställa de uppmätta förändringarna, t ex mätningar av G-ω på icke kapslade chips, skulle kunna öka förståelsen för dessa minneseffekter.

<p dir="ltr">Rapid evolution in wireless technology and the increasing demand for high bandwidth communication for 5G/6G and the Internet of Things (IoT) have necessitated a growing number of components in radio front-end modules in an increasingly overcrowded radio frequency (RF) spectrum. Low-cost ad-hoc radios have drawn consumer interest, enabling new devices like microelectromechanical (MEMS) resonators for on-chip clocking, frequency-selective filters, RF signal processing, and spectral sensing for their small footprint and low power consumption. Gallium nitride (GaN) is an attractive electromechanical material due to its high coupling coefficient, acoustic velocity, and low viscoelastic losses. These benefits enable high-Q MEMS resonators in GaN monolithic microwave integrated circuits (MMICs) with scaling capability up to mm-wave frequencies, making this technology platform a contender for high-performance programmable radios in RF/mm-wave, sensors for harsh environments, and information processing in quantum systems.</p><p dir="ltr">The bias-dependent control mechanism of the 2D electron gas (2DEG) in GaN heterostructures can be exploited to design different switchable and tunable devices for reconfigurable MEMS components. This work presents, for the first time, a comprehensive study of the electromechanical performances of different transduction mechanisms in switchable GaN MEMS resonators. A unique OFF-state shunt design, where the 2DEG in an AlN/GaN heterostructure is utilized to control electromechanical transduction in Lamb mode resonators, is also experimentally demonstrated in this work. To make a valid comparison among switchable transducers, equivalent circuit models are developed to extract key parameters from the measurements by fitting them in both ON and OFF states. The switchable transducer with Ohmic interdigitated transducers (IDTs) and Schottky control gate shows superior performance among the designs under consideration with complete suppression of the mechanical mode in the OFF state and a maximum frequency-quality factor product of 5x10¹²s^-1 and a figure-of-merit of 5.18 at 1GHz in the ON state.</p><p dir="ltr">Over the past few years, there have been numerous efforts to scale the frequencies of MEMS devices in the GaN platform towards mm-wave frequencies. However, challenges remain due to the multi-layer thick buffer, typical in the growth of GaN epilayer on a substrate. This work presents the investigation of SweGaN QuanFINEbuffer-free and ultrathin GaN-on-SiC for the performance of surface acoustic wave (SAW) devices beyond 10GHz. Finite element analysis (FEA) is performed to find the range of frequencies for the Sezawa mode in the structure. Transmission lines and resonators are designed, fabricated, and characterized. Modified Mason circuit models are developed for each class of devices to extract critical performance metrics and benchmark with the state-of-the-art and theoretical limits for GaN. Sezawa modes are observed at frequencies up to 14.3GHz, achieving a record high in GaN MEMS to the best of our knowledge. A maximum piezoelectric coupling of 0.61% and frequency-quality factor product of 6x10¹²s^-1 are achieved for Sezawa resonators at 11GHz, with a minimum propagation loss of 0.26dB/λ for the two-port devices. The devices also exhibit high linearity with input third-order intercept points (IIP3) of 65dBm at 9GHz.</p><p dir="ltr">This work also investigates tunable acoustoelectric (AE) devices in the QuanFINE platform, leveraging its inherent 2DEG in the AlGaN/GaN heterostructure. Using 9.7GHz Sezawa mode acoustic delay lines, we report the highest frequency of AE in GaN to date. Active and passive AE devices are designed for voltage-dependent non-reciprocity and propagation loss without modification to the standard process for the High Electron Mobility Transistors (HEMTs) in MMICs. Drain/source Ohmic contacts control the drift velocity of the 2DEG, and the Schottky gate modulates 2DEG carrier concentration, resulting in a 30dB/cm separation between forward and reverse acoustic waves for a 2.56kV/cm lateral DC electric field and a maximum change in propagation loss of 50dB/cm for -5V DC at the control gate, respectively. The QuanFINEtechnology with AlGaN/GaN heterostructure enables a platform for switchable MEMS resonators and tunable acoustoelectric devices in MMICs for reconfigurable front end approaching mm-wave frequencies.</p>

Developments in computer vision has sought to design deep neural networks which trained on a large set of images are able to generate high quality artificial images which share semantic qualities with the original image set. A pivotal shift was made with the introduction of the generative adversarial network (GAN) by Goodfellow et al.. Building on the work by Goodfellow more advanced models using the same idea have shown great improvements in terms of both image quality and data diversity. GAN models generate images by feeding samples from a vector space into a generative neural network. The structure of these so called latent vector samples show to correspond to semantic similarities of their corresponding generated images. In this thesis the DCGAN model is trained on a novel data set consisting of image sequences of the growth process of basil plants from germination to harvest. We evaluate the trained model by comparing the DCGAN performance on benchmark data sets such as MNIST and CIFAR10 and conclude that the model trained on the basil plant data set achieved similar results compared to the MNIST data set and better results in comparison to the CIFAR10 data set. To argue for the potential of using more advanced GAN models we compare the results from the DCGAN model with the contemporary StyleGAN2 model. We also investigate the latent vector space produced by the DCGAN model and confirm that in accordance with previous research, namely that the DCGAN model is able to generate a latent space with data specific semantic structures. For the DCGAN model trained on the data set of basil plants, the latent space is able to distinguish between images of early stage basil plants from late stage plants in the growth phase. Furthermore, utilizing the sequential semantics of the basil plant data set, an attempt at generating an artificial growth sequence is made using linear interpolation. Finally we present an unsuccessful attempt at visualising the latent space produced by the DCGAN model using a rudimentary approach at inverting the generator network function. / Utvecklingen inom datorseende har syftat till att utforma djupa neurala nätverk som tränas på en stor mängd bilder och kan generera konstgjorda bilder av hög kvalitet med samma semantiska egenskaper som de ursprungliga bilderna. Ett avgörande skifte skedde när Goodfellow et al. introducerade det generativa adversariella nätverket (GAN). Med utgångspunkt i Goodfellows arbete har flera mer avancerade modeller som använder samma idé uppvisat stora förbättringar när det gäller både bildkvalitet och datamångfald. GAN-modeller genererar bilder genom att mata in vektorer från ett vektorrum till ett generativt neuralt nätverk. Strukturen hos dessa så kallade latenta vektorer visar sig motsvara semantiska likheter mellan motsvarande genererade bilder. I detta examensarbete har DCGAN-modellen tränats på en ny datamängd som består av bildsekvenser av basilikaplantors tillväxtprocess från groning till skörd. Vi utvärderar den tränade modellen genom att jämföra DCGAN-modellen mot referensdataset som MNIST och CIFAR10 och drar slutsatsen att DCGAN tränad på datasetet för basilikaväxter uppnår liknande resultat jämfört med MNIST-dataset och bättre resultat jämfört med CIFAR10-datasetet. För att påvisa potentialen av att använda mer avancerade GAN-modeller jämförs resultaten från DCGAN-modellen med den mer avancerade StyleGAN2-modellen. Vi undersöker också det latenta vektorrum som produceras av DCGAN-modellen och bekräftar att DCGAN-modellen i enlighet med tidigare forskning kan generera ett latent rum med dataspecifika semantiska strukturer. För DCGAN-modellen som tränats på datamängden med basilikaplantor lyckas det latenta rummet skilja mellan bilder av basilikaplantor i tidiga stadier och sena stadier av plantor i tillväxtprocessen. Med hjälp av den sekventiella semantiken i datamängden för basilikaväxter gjörs dessutom ett försök att generera en artificiell tillväxtsekvens med hjälp av linjär interpolation. Slutligen presenterar vi ett misslyckat försök att visualisera det latenta rummet som produceras av DCGAN-modellen med hjälp av ett rudimentärt tillvägagångssätt för att invertera den generativa nätverksfunktionen.

In this report the influence of the growth conditions on the surface morphology of AlGaN/GaN heterostructures grown on sapphire-based and bulk GaN substrates is nondestructively investigated with focus on the decoration of defects and the surface roughness. Under Ga-rich conditions specific types of dislocations are unintentionally decorated with shallow hillocks. In contrast, under Ga-lean conditions deep pits are inherently formed at these defect sites. The structural data show that the dislocation density of the substrate sets the limit for the density of dislocation-mediated surface structures after MBE overgrowth and no noticeable amount of surface defects is introduced during the MBE procedure. Moreover, the transfer of crystallographic information, e.g. the miscut of the substrate to the overgrown structure, is confirmed. The combination of our MBE overgrowth with the employed surface morphology analysis by atomic force microscopy (AFM) provides a unique possibility for a nondestructive, retrospective analysis of the original substrate defect density prior to device processing.

Recently, a variety of machine learning techniques have been applied to different healthcare sectors, and the results appear to be promising. One such sector is healthcare planning, in which patient data is used to produce statistical models for predicting the load on different units of the healthcare system. This research introduces an attempt to design and implement a privacy-preserving synthetic data generation method adapted explicitly to patients’ health data and for healthcare planning. A Privacy-preserving Conditional Generative Adversarial Network (PPCGAN) is used to generate synthetic data of Healthcare events, where a well-designed noise is added to the gradients in the training process. The concept of differential privacy is used to ensure that adversaries cannot reveal the exact training samples from the trained model. Notably, the goal is to produce digital patients and model their journey through the healthcare system. / Nyligen har en mängd olika maskininlärningstekniker tillämpats på olika hälso- och sjukvårdssektorer, och resultaten verkar lovande. En sådan sektor är vårdplanering, där patientdata används för att ta fram statistiska modeller för att förutsäga belastningen på olika enheter i sjukvården. Denna forskning introducerar ett försök att utforma och implementera en sekretessbevarande syntetisk datagenereringsmetod som uttryckligen anpassas till patienters hälsodata och för vårdplanering. Ett sekretessbevarande villkorligt generativt kontradiktoriskt nätverk (PPCGAN) används för att generera syntetisk data från hälsovårdshändelser, där ett väl utformat brus läggs till gradienterna i träningsprocessen. Begreppet differentiell integritet används för att säkerställa att motståndare inte kan avslöja de exakta träningsproven från den tränade modellen. Målet är särskilt att producera digitala patienter och modellera deras resa genom sjukvården.

The incorporation of Gallium Nitride (GaN) Power Amplifiers (PAs) into future high power aperture radar systems is certain; however, the introduction of this technology into multifunction radar systems will present new challenges to radar engineers. This dissertation describes a broad investigation into amplitude and phase transients produced by GaN PAs when they are excited with multifunction radar waveforms. These transients are the result of self-heating electrothermal memory effects and are manifested as interpulse instabilities that can negatively impact the coherent processing of multiple pulses. A behavioral model based on a Foster network topology has been developed to replicate the measured amplitude and phase transients accurately. This model has been used to develop a digital predistortion technique that successfully mitigates the impact of the transients. The Moving Target Indicator (MTI) Improvement Factor and the Root Mean Square (RMS) Pulse-to-Pulse Stability are used as metrics to assess the impact of the transients on radar system performance and to test the effectiveness of a novel digital predistortion concept. / Ph. D.

Doctor of Philosophy / Department of Physics / Jingyu Lin / Time-resolved deep ultraviolet (DUV) Photoluminescence (PL) spectroscopy system has been employed to systematically monitor crystalline quality, identify the defects and impurities, and investigate the light emission mechanism in III-nitride semiconducting materials and photonic structures. A time correlated single photon counting system and streak camera with corresponding time resolutions of 20 and 2 ps, respectively, were utilized to study the carrier excitation and recombination dynamics. A closed cycle He-flow cryogenic system was employed for temperature dependent measurements. This system is able to handle sample temperatures in a wide range (from 10 to 900 K). Structural, electrical, and morphological properties of the material were monitored by x-ray diffraction (XRD), Hall-effect measurement, and atomic force microscopy (AFM), respectively. Most of the samples studied here were synthesized in our laboratory by metal organic chemical vapor deposition (MOCVD). Some samples were bulk AlN synthesized by our collaborators, which were also employed as substrates for homoepilayer growth. High quality AlN epilayers with (0002) XRD linewidth as narrow as 50 arcsec and screw type dislocation density as low as 5x10[superscript]6 cm[superscript]-2 were grown on sapphire substrates. Free exciton transitions related to all valence bands (A, B, and C) were observed in AlN directly by PL, which allowed the evaluation of crystal field (Δ[subscript]CF) and spin-orbit (Δ[subscript]SO) splitting parameters exerimentally. Large negative Δ[subscript]CF and, consequently, the difficulties of light extraction from AlN and Al-rich AlGaN based emitters due to their unique optical polarization properties have been further confirmed with these new experimental data. Due to the ionic nature of III-nitrides, exciton-LO phonon Frohlich interaction is strong in these materials, which is manifested by the appearance of phonon replicas accompanying the excitonic emission lines in their PL spectra. The strength of the exciton-phonon interactions in AlN has been investigated by measuring the Huang-Rhys factor. It compares the intensity of the zero phonon (exciton emission) line relative to its phonon replica. AlN bulk single crystals, being promising native substrate for growing nitride based high quality device structures with much lower dislocation densities (<10[superscript]4 cm[superscript]-2), are also expected to be transparent in visible to UV region. However, available bulk AlN crystals always appear with an undesirable yellow or dark color. The mechanism of such undesired coloration has been investigated. MOCVD was utilized to deposit ~0.5 μm thick AlN layer on top of bulk crystal. The band gap of strain free AlN homoepilayers was 6.100 eV, which is ~30 meV lower compared to hetero-epitaxial layers on sapphire possessing compressive strain. Impurity incorporation was much lower in non-polar m-plane growth mode and the detected PL signal at 10 K was about an order of magnitude higher from a-plane homo-epilayers compared to that from polar c-plane epilayers. The feasibility of using Be as an alternate p-type dopant in AlN has been studied. Preliminary studies indicate that the Be acceptor level in AlN is ~330 meV, which is about 200 meV shallower than the Mg level in AlN. Understanding the optical and electronic properties of native point defects is the key to achieving good quality material and improving overall device performance. A more complete picture of optical transitions in AlN and GaN has been reported, which supplements the understanding of impurity transitions in AlGaN alloys described in previous reports.

Le nitrure de gallium (GaN) est un matériau en plein essor depuis le début des années 1990 pour des applications dans le domaine de l'optoélectronique telles que les diodes électroluminescentes (DELs) bleues ou blanches, les diodes lasers (DLs) bleues ou les détecteurs ultra-violets. L'activité épitaxie de GaN par la technique de croissance HVPE (Epitaxie en phase vapeur par la méthode aux hydrures), a vu le jour au LASMEA en 1998. Les premières études expérimentales et de modélisation réalisées sur des échantillons de faibles dimensions (surface d'environ 1 à 3 cm2) ont conduit à la mise en évidence des mécanismes de croissance et à la maîtrise du procédé. Le développement de ce matériau à l'échelle industrielle a nécessité de travailler sur des surfaces de dimension plus grandes de l'ordre de deux pouces. Un nouveau dispositif expérimantal HVPE a été conçu dans ce sens, mis en place au sein du laboratoire et le procédé a été validé. De nouvelles investigations ont été menées sur l'étude de l'épitaxie sélective de GaN pour la réalisation de structures périodiques de faible dimensionnalité à morphologies contrôlées. Des structures de morphologies poutres et pyramidales de GaN de 1 à 2 µm de large ont ainsi pu être épitaxiées par la technique HVPE. Une analyse systématique de la variation des conditions de croissance est effectuée, visant à maîtriser l'ensemble des paramètres qui influent sur les morphologies et les dimensions des structures. Cette étude est couplée à la compréhension des mécanismes de croissance mis en jeu au cours de l'épitaxie sélective de GaN.