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We studied the wurtzite to rock salt phase transition in gallium nitride ( GaN ). Using the mapping algorithm of COMSUBS we found 435 possible mechanisms for this transition. We then used FIREBALL to do density functional theory calculations and found enthalpy barrier heights for the transition pathway. We used this to determine the mechanisms that are the most favorable for GaN. The most favorable mechanisms for GaN are those that break no bonds during the phase transition. The bond-preserving mechanisms involve bilayer sliding of (010) hexagonal plane in the plus or minus [100] hexagonal direction.

Most semantic segmentation methods based on Convolutional Neural Networks (CNNs) rely on supervised pixel-level labelling, but because pixel-level labelling is time-consuming and laborious, synthetic images are generated by software, and their label information is already embedded inside the data; therefore, labelling can be done automatically. This advantage makes synthetic datasets widely used in training deep learning models for real-world cases. Still, compared to supervised learning with real-world labelled images, the accuracy of the models trained using synthetic datasets is not high when applied to real-world data. So, researchers have turned their interest to Unsupervised Domain Adaptation (UDA), which is mainly used to transfer knowledge learned from one domain to another. That is why we can use synthetic data to train the model. Then, the model can use what it learned to deal with real-world problems. UDA is an essential part of transfer learning. It aims to make two domain feature distributions as close as possible. In other words, UDA is mainly used to migrate the learned knowledge from one domain to another, so the knowledge and distribution learned from the source domain feature space can be migrated to the target space to improve the prediction accuracy of the target domain. However, compared with the traditional supervised learning model, the accuracy of UDA is not high when the trained UDA is used for scene segmentation of real images. The reason for the low accuracy of UDA is that the domain gap between the source and target domains is too large. The image distribution information learned by the model from the source domain cannot be applied to the target domain, which limits the development of UDA. Therefore we propose a new UDA model called TwinLossGAN, which will reduce the domain gap in two steps. The first step is to mix images from the source and target domains. The purpose is to allow the model to learn the features of images from both domains well. Mixing is performed by selecting a synthetic image on the source domain and then selecting a real-world image on the target domain. The two selected images are input to the segmenter to obtain semantic segmentation results separately. Then, the segmentation results are fed into the mixing module. The mixing model uses the ClassMix method to copy and paste some segmented objects from one image into another using segmented masks. Additionally, it generates inter-domain composite images and the corresponding pseudo-label. Then, in the second step, we modify a Generative Adversarial Network (GAN) to reduce the gap between domains further. The original GAN network has two main parts: generator and discriminator. In our proposed TwinLossGAN, the generator performs semantic segmentation on the source domain images and the target domain images separately. Segmentations are trained in parallel. The source domain synthetic images are segmented, and the loss is computed using synthetic labels. At the same time, the generated inter-domain composite images are fed to the segmentation module. The module compares its semantic segmentation results with the pseudo-label and calculates the loss. These calculated twin losses are used as generator loss for the GAN cycle for iterations. The GAN discriminator examines whether the semantic segmentation results originate from the source or target domain. The premise was that we retrieved data from GTA5 and SYNTHIA as the source domain data and images from CityScapes as the target domain data. The result was that the accuracy indicated by the TwinLossGAN that we proposed was much higher than the base UDA models.

In this work, we propose a novel and effective method for constraining the output space of the ill-posed problem of unsupervised image-to-image translation. We make the assumption that the environment of the source domain is known, and we propose to explicitly enforce preservation of the ground-truth labels on the images translated from the source to the target domain. We run empirical experiments on preserving information such as semantic segmentation and disparity and show evidence that our method achieves improved performance over the baseline model UNIT on translating images from SYNTHIA to Cityscapes. The generated images are perceived as more realistic in human surveys and have reduced errors when using them as adapted images in the domain adaptation scenario. Moreover, the underlying ground-truth preservation assumption is complementary to alternative approaches and by combining it with the UNIT framework, we improve the results even further. / I det här arbetet föreslår vi en ny och effektiv metod för att begränsa värdemängden för det illa-definierade problemet som utgörs av oövervakad bild-till-bild-översättning. Vi antar att miljön i källdomänen är känd, och vi föreslår att uttryckligen framtvinga bevarandet av grundfaktaetiketterna på bilder översatta från källa till måldomän. Vi utför empiriska experiment där information som semantisk segmentering och skillnad bevaras och visar belägg för att vår metod uppnår förbättrad prestanda över baslinjemetoden UNIT på att översätta bilder från SYNTHIA till Cityscapes. De genererade bilderna uppfattas som mer realistiska i undersökningar där människor tillfrågats och har minskat fel när de används som anpassade bilder i domänpassningsscenario. Dessutom är det underliggande grundfaktabevarande antagandet kompletterat med alternativa tillvägagångssätt och genom att kombinera det med UNIT-ramverket förbättrar vi resultaten ytterligare.

Recently a new method for training generative neural networks called Generative Adversarial Networks (GAN) has shown great results in the computer vision domain and shown potential in other generative machine learning tasks as well. GAN training is an adversarial training method where two neural networks compete and attempt to outperform each other, and in the process they both learn. In this thesis the effectiveness of GAN training is tested on conversational agents also called chat bots. To test this, current state-of-the-art training methods such as Maximum Likelihood Estimation (MLE) models are compared with GAN method trained models. Model performance was measured by closeness of the model distribution from the target distribution after training. This thesis shows that the GAN method performs worse the MLE in some scenarios but can outperform MLE in some cases. / Nyligen har en ny metod för att träna generativa neurala nätverk kallad Generative Adversarial Networks (GAN) visat bra resultat inom datorseendedomänen och visat potential inom andra maskininlärningsområden också GAN-träning är en träningsmetod där två neurala nätverk tävlar och försöker överträffa varandra, och i processen lär sig båda. I detta examensarbete har effektiviteten av GAN-träning testats på konversationsagenter, som också kallas Chat bots. För att testa det här jämfördes modeller tränade med nuvarande state-of- the-art träningsmetoder, så som Maximum likelihood-metoden (ML), med GAN-tränade modeller. Modellernas prestation mättes genom distans från modelldistribution till måldistribution efter träning. Det här examensarbetet visar att GAN-metoden presterar sämre än ML-metoden i vissa scenarier men kan överträffa ML i vissa fall.

In this research, the transport properties of ZnO were studied through the use of electron and neutron beam irradiation. Acceptor states are known to form deep in the bandgap of doped ZnO material. By subjecting doped ZnO materials to electron and neutron beams we are able to probe, identify and modify transport characteristics relating to these deep accepter states. The impact of irradiation and temperature on minority carrier diffusion length and lifetime were monitored through the use of the Electron Beam Induced Current (EBIC) method and Cathodoluminescence (CL) spectroscopy. The minority carrier diffusion length, L, was shown to increase as it was subjected to increasing temperature as well as continuous electron irradiation. The near-band-edge (NBE) intensity in CL measurements was found to decay as a function of temperature and electron irradiation due to an increase in carrier lifetime. Electron injection through application of a forward bias also resulted in a similar increase of minority carrier diffusion length. Thermal and electron irradiation dependences were used to determine activation energies for the irradiation induced effects. This helps to further our understanding of the electron injection mechanism as well as to identify possible defects responsible for the observed effects. Thermal activation energies likely represent carrier delocalization energy and are related to the increase of diffusion length due to the reduction in recombination efficiency. The effect of electron irradiation on the minority carrier diffusion length and lifetime can be attributed to the trapping of non-equilibrium electrons on neutral acceptor levels. The effect of neutron irradiation on CL intensity can be attributed to an increase in shallow donor concentration. Thermal activation energies resulting from an increase in L or decay of CL intensity monitored through EBIC and CL measurements for p-type Sb doped ZnO were found to be the range of Ea = 112 to 145 meV. P-type Sb doped ZnO nanowires under the influence of temperature and electron injection either through continuous beam impacting or through forward bias, displayed an increase in L and corresponding decay of CL intensity when observed by EBIC or CL measurements. These measurements led to activation energies for the effect ranging from Ea = 217 to 233 meV. These values indicate the possible involvement of a SbZn-2VZn acceptor complex. For N-type unintentionally doped ZnO, CL measurements under the influence of temperature and electron irradiation by continuous beam impacting led to a decrease in CL intensity which resulted in an electron irradiation activation energy of approximately Ea = 259 meV. This value came close to the defect energy level of the zinc interstitial. CL measurements of neutron irradiated ZnO nanostructures revealed that intensity is redistributed in favor of the NBE transition indicating an increase of shallow donor concentration. With annealing contributing to the improvement of crystallinity, a decrease can be seen in the CL intensity due to the increase in majority carrier lifetime. Low energy emission seen from CL spectra can be due to oxygen vacancies and as an indicator of radiation defects.

This research investigated the effect of electron injection in III-Nitrides. The combination of electron beam induced current and cathodoluminescence measurements was used to understand the impact of electron injection on the minority carrier transport and optical properties. In addition, the application of the electron injection effect in optoelectronic devices was investigated. The impact of electron injection on the minority carrier diffusion length was studied at various temperatures in Mg-doped p-GaN, p-Al[subscript x]Ga[subscript 1-x]N, and p-Al[subscript x]Ga[subscript 1-x] N/GaN superlattices. It was found that Lsubscript n] experienced a multi-fold linear increase and that the rate of change of L[subscript n] decreased exponentially with increasing temperature. The effect was attributed to a temperature-activated release of the electrons, which were trapped by the Mg levels. The activation energies, E[subscript a], for the electron injection effect in the Mg-doped (Al)GaN samples were found to range from 178 to 267 meV, which is close to the thermal ionization energy of the Mg acceptor. The E[subscript a] observed for Al[subscript 0.15]Ga[subscript 0.85]N and Al[subscript 0.2]Ga[subscript 0.8]N was consistent with the deepening of the Mg acceptor level due to the incorporation of Al into the GaN lattice. The E[subscript a] in the homogeneously doped Al[subscript 0.2]Ga[subscript 0.8]N/GaN superlattice indicates that the main contribution to the electron injection effect comes from the capture of injected electrons by the wells (GaN). The electron injection effect was successfully applied to GaN doped with an impurity (Mn) other than Mg. Electron injection into Mn-doped GaN resulted in a multi-fold increase of the L[subscript n] and a pronounced decrease in the band-to-band cathodoluminescence intensity. The E[subscript a] due to the electron injection effect was estimated from temperature-dependent cathodoluminescence measurements to be 360 meV. The decrease in the band-to-band cathodoluminescence is consistent with an increase in L[subscript n] and these results are attributed to an increase in the minority carrier lifetime due to the trapping of injected electrons by the Mn levels. A forward bias was applied to inject electrons into commercially built p-i-n and Schottky barrier photodetectors. Up to an order of magnitude increase in the peak (360 nm) responsivity was observed. The enhanced photoresponse lasted for over four weeks and was attributed to an electron injection-induced increase of L[subscript n] and the lifetime.

Electrostatic Discharge (ESD), as a subset of Electrical Overstress (EOS), was reported to be in charge of more than 35% of failure in integrated circuits (ICs). Especially in the manufacturing process, the silicon wafer turns out to be a functional ICs after numerous physical, chemical and mechanical processes, each of which expose the sensitive and fragile ICs to ESD environment. In normal end-user applications, ESD from human and machine handling, surge and spike signals in the power supply, and wrong supplying signals, will probably cause severe damage to the ICs and even the whole systems. Generally, ESD protections are evaluated after wafer and even system fabrication, increasing the development period and cost if the protections cannot meet customer's requirements. Therefore, it is important to design and customize robust and area-efficient ESD protections for the ICs at the early development stage. As the technologies generally scaling down, however, ESD protection clamps remain comparable area consumption in the recent years because they provide the discharging path for the ESD energy which rarely scales down. Diode is the most simple and effective device for ESD protection in ICs, but the usage is significantly limited by its low turn-on voltage. MOS devices can be triggered by a dynamic-triggered RC circuit for IOs operating at low voltage, while the one triggered by a static-triggered network, e.g., zener-resistor circuit or grounded-gate configuration, provides a high trigger voltage for high-voltage applications. However, the relatively low current discharging capability makes MOS devices as the secondary choice. Silicon-controlled rectifier (SCR) has become famous due to its high robustness and area efficiency, compared to diode and MOS. In this dissertation, a comprehensive design methodology for SCR based on simulation and measurement are presented for different advanced commercial technologies. Furthermore, an ESD clamp is designed and verified for the first time for the emerging GaN technology. For the SCR, no matter what modification is going to be made, the first concern when drawing the layout is to determine the layout geometrical style, finger width and finger number. This problem for diode and MOS device were studied in detail, so the same method was usually used in SCR. The research in this dissertation provides a closer look into the metal layout effect to the SCR, finding out the optimized robustness and minimized side-effect can be obtained by using specific layout geometry. Another concern about SCR is the relatively low turn-on speed when the IOs under protection is stressed by ESD pulses having very fast rising time, e.g., CDM and IEC 61000-4-2 pulses. On this occasion a large overshoot voltage is generated and cause damage to internal circuit component like gate oxides of MOS devices. The key determination of turn-on speed of SCR is physically investigated, followed by a novel design on SCR by directly connecting the Anode Gate and Cathode Gate to form internal trigger (DCSCR), with improved performance verified experimentally in this dissertation. The overshoot voltage and trigger voltage of the DCSCR will be significantly reduced, in return a better protection for internal circuit component is offered without scarifying neither area or robustness. Even though two SCR's with single direction of ESD current path can be constructed in reverse parallel to form bidirectional protection to pins, stand-alone bidirectional SCR (BSCR) is always desirable for sake of smaller area. The inherent high trigger voltage of BSCR that only fit in high-voltage technologies is overcome by embedding a PMOS transistor as trigger element, making it highly suitable for low-voltage ESD protection applications. More than that, this modification simultaneously introduces benefits including high robustness and low overshoot voltage. For high voltage pins, however, it presents another story for ESD designs. The high operation voltages require that a high trigger voltage and high holding voltage, so as to reduce the false trigger and latch-up risk. For several capacitive pins, the displacement current induced by a large snapback will cause severe damage to internal circuits. A novel design on SCR is proposed to minimize the snapback with adjustable trigger and holding voltage. Thanks to the additional a PIN diode, the similar high robustness and stable thermal leakage performance to SCR is maintained. For academic purpose of ESD design, it is always difficult to obtain the complete process deck in TCAD simulation because those information are highly confidential to the companies. Another challenge of using TCAD is the difficulty of maintaining the accuracy of physics models and predicting the performance of the other structures. In this dissertation a TCAD-aid ESD design methodology is used to evaluate ESD performance before the silicon shuttle. GaN is a promising material for high-voltage high-power RF application compared to the GaAs. However, distinct from GaAs, the leaky problem of the schottky junction and the lack of choice of passive/active components in GaN technology limit the ESD protection design, which will be discussed in this dissertation. However, a promising ESD protection clamp is finally developed based on depletion-mode pHEMT with adjustable trigger voltage, reasonable leakage current and high robustness.

Minority carrier transport properties and the effects of electron irradiation/injection were studied in GaN and ZnO containing dopants known to form acceptor states deep within the materials' bandgap. Minority carrier diffusion length and lifetime changes were investigated using Electron Beam Induced Current (EBIC) method, cathodoluminescence spectroscopy, spectral photoresponse and persistent photoconductivity measurements. It is shown that electron irradiation by the beam of a scanning electron microscope results in a significant increase of minority carrier diffusion length. These findings are supported by the cathodoluminescence measurements that demonstrate the decay of near-band-edge intensity as a consequence of increasing carrier lifetime under continuous irradiation by the electron beam. Temperature-dependent measurements were used to determine the activation energies for the electron irradiation-induced effects. The latter energies were found to be consistent with the involvement of deep acceptor states. Based on these findings, the effects of electron irradiation are explained via the mechanism involving carrier trapping on these levels. Solid-state electron injection was also shown to result in a similar increase of minority carrier lifetime and diffusion length. Solid-state injection was carried out by applying the forward bias to a ZnO homojunction and resulted in a significant improvement of the peak photoresponse of the junction. This improvement was unambiguously correlated with the increase of the minority carrier diffusion length due to electron injection.

In the last 15 years, an immense amount of research has gone into developing high efficiency Gallium Nitride based light emitting diodes (LED). These devices have become increasingly popular in LED displays and solid state lighting. Due to the large difference in refractive index between GaN and Air, a significant amount of light reflects at the boundary and does not escape the device. This drawback decreases external quantum efficiency (EQE) by minimizing light extraction. Scientists and engineers continue to develop creative solutions to enhance light extraction. Some solutions include surface roughening, patterned sapphire substrates, and reflective layers. This study proposes to increase external quantum efficiency and optimize light extraction efficiency of several LED structures using finite difference time domain analysis (FDTD). The structures under investigation include GaN based LEDs with nanoscale top gratings, patterned sapphire substrates in combination with SiO2 nanorod arrays, and reflective surfaces below and above the sapphire substrate. First, we optimize GaN based nanoscale top gratings and increase light extraction by 17.8%. Next, we simulate ITO based top gratings and enhance light extraction by 40%. Third, we optimize patterned sapphire substrate period and width and the vertical position of a SiO2 nanorod array. We achieve as high as 51.8% improvement in light extraction. Finally, we increase light extraction by 160% with the use of a silver reflection layer.