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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Etched and Regrown Vertical GaN Junction Barrier Schottky Diodes.
Abstract not provided.
Analysis of useful ion yield for Si in GaN by secondary ion mass spectrometry
The optimum detection levels that can be achieved by a secondary ion mass spectrometer are dependent on how efficiently a particular species of interest can be ionized and detected. One can determine in advance whether the analysis of a particular ion in the sample is possible, if the useful ion yield is known. The useful ion yield depends on the element, instrument transmission, the analytical conditions, the sample matrix, etc. The value of the useful ion yield for a species can diverge from one instrument type to another due to its different transmission and ionization probabilities. However, the same tendencies in the results may be expected for all types of instruments. In this paper, the authors present a quantitative secondary ion mass spectrometry analysis of the useful ion yield for the silicon dopant species in a gallium nitride matrix grown by metal organic chemical vapor deposition. Positively ionized cesium was used as the primary ion beam, and its energy was varied in the range from 0.5 to 5 kV. A quadrupole mass analyzer was utilized to collect secondary ion species of interest. The analysis results can be used to determine the primary beam energies for optimal Si sensitivity.
Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference
Not provided.
The Structural Evolution of Dot-Core GaN Substrates with Annealing Under Growth-Like Conditions
Abstract not provided
Synchrotron X-ray Topography Characterization of Commercial GaN Substrates for Power Electronic Applications
Abstract not provided
Structural Characterization of Dot-Core GaN Substrates with Annealing Under Growth-Like Conditions U
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Physics-driven learning of Wasserstein GAN for density reconstruction in dynamic tomography
Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reconstruction methods may not provide the high accuracy needed in many applications and can break down in the presence of unmodeled or anomalous scatter and other experimental artifacts. Incorporating machine-learning models could prove beneficial for accurate density reconstruction, particularly in dynamic imaging, where the time evolution of the density fields could be captured by partial differential equations or by learning from hydrodynamics simulations. In this work, we demonstrate the ability of learned deep neural networks to perform artifact removal in noisy density reconstructions, where the noise is imperfectly characterized. Here, we use a Wasserstein generative adversarial network (WGAN), where the generator serves as a denoiser that removes artifacts in densities obtained from traditional reconstruction algorithms. We train the networks from large density time-series datasets, with noise simulated according to parametric random distributions that may mimic noise in experiments. The WGAN is trained with noisy density frames as generator inputs, to match the generator outputs to the distribution of clean densities (time series) from simulations. A supervised loss is also included in the training, which leads to an improved density restoration performance. In addition, we employ physics-based constraints such as mass conservation during the network training and application to further enable highly accurate density reconstructions. Our preliminary numerical results show that the models trained in our frameworks can remove significant portions of unknown noise in density time-series data.
Application of synchrotron X-ray topography to characterization of ion implanted GaN epitaxial layer
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How to GAN Higher Jet Resolution
QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the resolution of jet images. We test this approach on massless QCD-jets and on fat top-jets and find that the network reproduces their main features even without training on pure samples. In addition, we show how a slim network architecture can be constructed once we have control of the full network performance.
A new era of observationally-infused E3SM: GANs for unifying imagery archives
This paper presents an idea to develop a “Rosetta Stone” for unifying observations from various satellite or remote sensors into a common format that would vastly advance our ability to exploit existing datasets for improving predictability within Earth System Models (ESMs). While the applications of such a unified archive are broad, we believe it will be a critical step toward ushering in a new generation of ESMs that are richly informed, guided by, and validated by extensive observational data. With the vast quantity of both remotely-sensed and in-situ data streams available and coming online, new approaches are needed that can harmonize and thus fully exploit these expensive datasets. While we present the broader idea, we point to examples of applications that impact the water cycle and its representation in ESMs.
Vertical Gallium Nitride (GaN) Transistors For Extreme Environments
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