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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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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fast and accurate simulations of calorimeter showers with normalizing flows

In this study, we introduce caloflow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce many-channel calorimeter showers with extremely high fidelity, providing a fresh alternative to computationally expensive geant4 simulations, as well as other state-of-the-art fast simulation frameworks based on generative adversarial networks (GANs) or variational autoencoders (VAEs). In addition to the usual histograms of physical features and images of calorimeter showers, we introduce a new metric for judging the quality of generative modeling: the performance of a classifier trained to differentiate real from generated images. We show that GAN-generated images can be identified by the classifier with nearly 100% accuracy, while images generated from caloflow are better able to fool the classifier. More broadly, normalizing flows offer several advantages compared to other state-of-the-art approaches (GANs and VAEs), including tractable likelihoods, stable and convergent training, and principled model selection. Normalizing flows also provide a bijective mapping between data and the latent space, which could have other applications beyond simulation, for example, to detector unfolding.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Thermodynamics of boron incorporation in BGaN

We study the thermodynamics of boron (B) incorporation into gallium nitride (GaN) using first-principles calculations. In the dilute limit, we have calculated the formation energies of different configurations of the B impurity in GaN and found that substitution on the cation site is favored over substitution on the anion site. Furthermore, under p-type conditions, interstitial boron can become the more favorable configuration and will ultimately limit the p-type conductivity. At higher B concentrations we use the generalized quasi-chemical approximation to elucidate the thermodynamic stability of boron gallium nitride (BGaN) alloys. We also investigate the effects of strain, which will be present if BGaN alloys are grown pseudomorphically on a GaN substrate.Without strain, B incorporation at typical growth conditions is limited to about 1.4% at 800 °C. Pseudomorphic strain raises the limit to 3.0% at the same temperature, close to experimentally observed levels of B incorporation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Interdigitated electrode geometry variation and external quantum efficiency of GaN/AlGaN-based metal-semiconductor-metal UV photodetectors

Aluminum gallium-nitride (Al x Ga 1–x N)-based metal–semiconductor–metal (MSM) ultraviolet photodetectors photodetectors (PD’s) have been successfully designed and fabricated using conventional photolithography techniques and tested experimentally to study their spectral sensitivity across different Al content, x, with x varying from 0 to 0.3. (Al)GaN-based UV PD’s have wide and tunable direct band gaps. The ability to easily select the photodetected wavelength by simply varying the aluminum content of GaN thin film (Al x Ga 1–x N) is a significant advantage of these group III–V compounds. Typically, MSM PD’s grown on (Al)GaN thin films result in ultrafast photodetection because of their highly mobile carriers. These devices are limited by the carrier transit time due to the negligible capacitance presented by the interdigitated fingers. Various electrode geometries were fabricated to investigate the influence of metal contact shapes on the devices’ performance indices with emphasis on the response speed and bias-voltage–independent efficiency. Here, coupled with the independently measured Hall mobility and electric field, we computed the carrier transit time of the devices to be as short as 1.31 ps and the bias-voltage–independent external quantum efficiencies were as high as 70% at 60 V for n-doped and intrinsic devices when operated in the reverse bias regime. Here, (Al x )Ga 1–x N photodetectors were designed to explore spectral sensitivity by altering x from 0 to 0.3.

42 ENGINEERING↗
DOE OSTI2020

Investigation of Superluminescent Diodes for Smart Lighting Systems. Final Report

The solid-state lighting ecosystem has evolved very rapidly over the last few years, with significant improvements in the technical performance of light-emitting diodes (LEDs) and the commoditization of LED-based lighting fixtures. As the performance of conventional lighting products begins to saturate, there is growing market interest in “Lighting as a Service” applications that will leverage advanced systems to impart new functionalities to lighting and improve energy efficiency, human health, and productivity. These “Smart Lighting” systems will include high-performance light sources, specialized sensors, and dynamic controls to deliver high quality, energy efficient, color tunable lighting with customized spatial light delivery and integrated visible light communication capability. To achieve these capabilities, smart lighting systems will place greater demands on the performance of light sources. Highly efficient sources with additional functionalities compared to conventional LEDs, such as small form factor, large modulation bandwidth, and spatially coherent output beams, will be required. While laser diodes have been proposed as potential sources for smart lighting systems, they also exhibit several properties that pose challenges, such as temporal coherence, extremely high spatial coherence, and ultra-narrow linewidths. To address these issues, we propose an investigation of an alternative device architecture known as a superluminescent diode (SLD). SLDs are similar in form to ridge laser diodes and share many of the same characteristics, such as stimulated emission operation, spatially coherent output, small form factor, and the potential for large modulation bandwidth. However, the operating principle for SLDs is distinct from laser diodes in that SLDs lack a strong cavity feedback mechanism, resulting in spatially coherent but temporally incoherent light output. Thus, SLDs may address the issues with laser diodes for lighting, while simultaneously maintaining some of the desirable characteristics of both laser diodes and LEDs. The objectives of this proposal are to design, grow, and fabricate blue (450 nm) SLDs on polar c-plane and nonpolar m-plane free-standing GaN substrates, and to evaluate their potential as sources in smart lighting systems through basic device characterization and detailed investigations of their efficiency droop and modulation bandwidth. The primary scientific aims of this work are to understand the fundamental role of optical gain in the superluminescent (non-lasing) regime on efficiency droop and modulation bandwidth in III-nitride emitters and to understand the effects of higher optical gain on SLD performance by comparing polar c-plane and nonpolar m-plane SLDs. The University of New Mexico (UNM) will collaborate with Sandia National Laboratories (SNL) and the Center for Integrated Nanotechnologies to design, fabricate, grow, and characterize the SLDs. UNM will perform the design and epitaxial growth, while SNL will focus on the fabrication and device characterization. The novelty of the proposed work includes the first comprehensive theoretical and experimental investigations of efficiency droop and the first investigation of modulation bandwidth in GaN-based emitters operating in the superluminescent regime. Moreover, the comparison of c-plane and m-plane SLDs will enable the first analysis of the effects of higher optical gain on the device performance. The development of high-performance GaN-based SLDs may enable efficiency gains and improved functionality in next-generation smart lighting systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Quantification of morphological change in materials based on image data utilizing machine learning techniques

Computed tomography (CT) resolution has become high enough to monitor morphological changes due to aging in materials in long-term applications. We explored the utility of the critic of a generative adversarial network (GAN) to automatically detect such changes. The GAN was trained with images of pristine Pharmatose, which is used as a surrogate energetic material. It is important to note that images of the material with altered morphology were only used during the test phase. The GAN-generated images visually reproduced the microstructure of Pharmatose well, although some unrealistic particle fusion was seen. Calculated morphological metrics (volume fraction, interfacial line length, and local thickness) for the synthetic images also showed good agreement with the training data, albeit with signs of mode collapse in the interfacial line length. While the critic exposed changes in particle size, it showed limited ability to distinguish images by particle shape. The detection of shape differences was also a more challenging task for the selected morphological metrics that related to energetic material performance. We further tested the critic with images of aged Pharmatose. Subtle changes due to aging are difficult for the human analyst to detect. Both critic and morphological metrics analysis showed image differentiation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Generative network-based approaches to generate stochastic realizations

Generative Adversarial Network (GAN) – based models have been successfully applied in generating different geological models in the literature. However, it is still challenging to use GAN to generate geological realizations with extremely sparse conditioning data (e.g. several well data), which may be regarded as local noise by GAN during the training process. In this work, we propose a novel conditional Generative Adversarial Neural Operator (cGANO) to tackle this challenge. In cGANO, the mapping between conditioning data and output is established through the U-shaped neural operators (UNO), which better preserves local information. Another advantage of using UNO comes from its grid-independent property, which makes the generation of downscaling stochastic geologic realizations possible. We tested the model performance on the IBDP geostatistical dataset with 100 realizations.

58 GEOSCIENCES↗
DOE OSTI2022

X-Ray Scattering and Electron Microscopy Characterization

This research added to the understanding of p-type activation in GaN vertical transistors for power applications by identifying key defects which inhibit p-type activation. We also developed an approach to eliminate such defects with the appropriate annealing treatments. Additionally we addressed the limitations of GaN substrates used in the production of these devices and exploited the non-uniform distribution of substrate defects to better understand the relationship between device performance and defect density. Through this work, we also established how irregularities – step bunching and haze formation – during epitaxial growth are related to defects in the underlying substrates. Our charter was to work with several other teams to determine connections between defects and performance of GaN-based vertical transistors and have several presentations and publications as well as manuscripts and follow-up collaborations. The research at UCLA supported two full graduate students, one of whom is graduating this summer as well as three undergraduate students.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Scalable & Non-Destructive Characterization Strategy to Study Semiconductor/Dielectric Interfaces and Predict Wafer-Level Device Performance

The defect density present at the dielectric-semiconductor interface in an MOS structure directly influences the channel carrier characteristics in semiconductor devices, especially in wide bandgap material systems used in power devices. While these trap defects are typically quantified through electrical characterization of MOS-capacitor test structures, this treatment offers very little insight into the physical nature of interface defects. Such shortcomings demand a physical characterization strategy to guide fabrication optimization. X-ray photoelectron spectroscopy (XPS) is suggested as a viable technique to determine chemical data for dielectric interfaces formed using atomic layer deposition (ALD) on GaN substrates. Previously, 1-D XPS characterization has confirmed the presence of a Ga x O y interlayer between ALD dielectrics and the GaN substrate. In this work, XPS data is serially collected to form 2-D images of an ALD-Al 2 O 3 /GaN interface as a proof-of-concept experiment for in-situ XPS quality monitoring during ALD processing. The information provided by this work reveals some of the challenges for incorporating XPS characterization as an in-situ strategy during fabrication of GaN-based devices. Separately, electrical mapping of a 2-D array of ALD-Al 2 O 3 /GaN MOS-capacitor devices provide a means to quantify the spatial variations in interface quality across a single wafer. Physical characterization techniques, such as time-of-flight secondary ion mass spectroscopy, provide additional chemical information about the Al 2 O 3 /Ga x O y /GaN structure that complement the electrical mapping results. This analysis shows that a higher Ga x O y content correlates with higher interface state defects for trap energies deep in the band gap.

42 ENGINEERING↗
DOE OSTI2023

20 kV Gallium Nitride pn Diode Electro-Magnetic Pulse Arrestor for Grid Reliability (Final Report)

This project developed vertical Gallium Nitride (GaN) pn diodes under two main thrusts: (1) A focus on relatively higher-voltage devices for use as fast EMP arrestors to protect the electric grid; and (2) A focus on a Foundry effort to establish the manufacturability of relatively lower-voltage devices. For the first thrust, the aim was to develop devices that go into avalanche breakdown to clamp the voltage across sensitive grid equipment subject to voltage transients induced by electromagnetic pulses (EMPs). Devices with breakdown voltages exceeding 6.5 kV were achieved, and breakdown times shorter than 1 ns were demonstrated, which is sufficiently fast to protect against the fast component of an EMP-induced signal. Key challenges included the epitaxial growth of thick (50 um or more), low-doped (low 10 15 cm -3 range) GaN layers comprising the drift regions of the diodes, as well as the design and fabrication of edge termination structures (step-etched junction termination extensions) to prevent premature breakdown. Midway through the project, an additional emphasis was put on large-area, high-current devices, and forward currents of approximately 400 A were achieved in composite devices towards the end of the project. Experimental and theoretical studies of impact ionization and avalanche ruggedness were also conducted. For the second thrust (the Foundry), the focus was primarily on 1.2-kV-class devices, although towards the end of the project outstanding results on 3.3-kV-class devices were also achieved. The aim of the Foundry was to develop a high-yield, reliable, and economic vertical GaN pn diode process. The Foundry conducted characterization of incoming epitaxial material and correlated this information with the yield and performance of fully processed devices. Wafer maps of diode characteristics such as forward and reverse current as well as capacitance-voltage curves were measured using auto-probing. Several mask sets comprising different-area devices as well as multiple edge termination designs (implanted junction termination extensions, guard rings, and combinations thereof) were studied, and machine-learning-based approaches were utilized to analyze the data. Packaging and reliability efforts were also undertaken for the Foundry diodes, which are necessary for a viable commercial process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data and Application to Ukraine

With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

17 WIND ENERGY↗
DOE OSTI2024

Volumetric carrier injection in InGaN quantum well light emitting diodes

InGaN/GaN quantum well (QW) light emitting diodes (LEDs) are essential components of solid-state lighting and displays. However, the efficiency of long wavelength (green to red) devices is inferior to that of blue LEDs. To a large degree, this occurs because the equilibration of injected holes between multiple QWs of the active region is hindered by GaN quantum confinement and polarization barriers. This drawback could be overcome by volumetric hole injection into all QWs through semipolar QWs present on the facets of V-defects that form at threading dislocations in polar GaN-based structures. In this work, we have tested the viability of this injection mechanism and studied its properties by time-resolved and near-field spectroscopy techniques. Here, we have found that indeed the hole injection via the V-defects does take place, the mechanism is fast, and the hole spread from the V-defect is substantial, making this type of injection feasible for efficient long wavelength GaN LEDs.

InGaN/GaN quantum wells↗
NASA NTRS1993

Growth kinetics and characterizations of gallium nitride thin films by remote PECVD

Thin films of GaN have been deposited at relatively low growth temperatures by remote plasma-enhanced chemical-vapor deposition (RPECVD), using a plasma excited NH3, and trimethylgallium (TMG), injected downstream from the plasma. The activation energy for GaN growth has been tentatively assigned to the dissociation of NH groups as the primary N-atom precursors in the surface reaction with adsorbed TMG, or TMG fragments. At high He flow rates, an abrupt increase in the growth rate is observed and corresponds to a change in the reaction mechanism attributed to the formation of atomic N. XRD reveals an increased tendency to ordered growth in the (0001) direction with increasing growth temperature, He flow rate, and RF plasma power. IR spectra show the fundamental lattice mode of GaN at 530 cm without evidence for vibrational modes of hydrocarbon groups.

Choi, S. W.↗
NASA NTRS2003

Toward the Development of Group III-V Photodetectors and Imaging Arrays

A collaboration between researchers at NASA Goddard Space Flight Center (GSFC) (Code 718.1) and the Johns Hopkins University Applied Physics Laboratory (APL) on the development of gallium nitride (GaN) based photodetectors has been in existence since July 1994. This collaboration, based on APL undertaking the material growth and GSFC undertaking the device processing, has led to discrete GaN photoconductive detectors with superior characteristics to those of similar devices reported in the literature and, more recently, to the development of state-of-the art 256x256 imaging arrays with the pixels indium bump-bonded to a silicon readout circuit (RIOC). The object of this proposal is to continue the collaboration for the period 1998-2002 by outlining a program of work at the APL on the metalorganic chemical vapor deposition (MOCVD) growth of GaN and related materials for UV detector applications. In particular, emphasis will be placed on the optimization of growth on 2 in diameter substrates, on the growth of In(sub x)Ga(1-x)N and Al(sub x)Ga(1-x)N alloy structures to produce devices with a wider range of tailored cut-off wavelengths, and on the growth of pn-junction structures for photovoltaic devices.

Wickenden, Dennis K.↗
NASA NTRS2018

Cryogenic Parametric Characterization of Gallium Nitride Switches

This report presents the parametric characterization results of four GaN field-effect transistor (FET) devices from three manufacturers, one of which is a cascode device, and compares those results to a Si power metal-oxide-semiconductor fieldeffect transistor (MOSFET) and a SiC power MOSFET. The devices were first characterized at ambient temperature, then at cryogenic temperatures down to -196 C (LN2 temperature), and finally at ambient temperature again in the event that the device parameters were permanently affected by the cryogenic temperatures. In general, the results indicate that the GaN devices show significant improvement overall at cryogenic temperatures in the parameters characterized, such as onresistance and leakage currents, compared to the Si and SiC devices. The results show that the SiC device tested should not be used at cryogenic temperatures due to the significant increase in on-resistance. The results also show that the GaN and Si parameters characterized were either not affected by the cryogenic temperatures or changed by no more than +/-20 percent post LN2 submersion. The device that exhibited the most parametric change post LN2 submersion was the SiC power MOSFET in its leakage currents.

Gonzalez, Marcelo C.↗
NASA NTRS2020

CyberGAN: Generating High-fidelity Cybersecurity Data With Generative Adversarial Networks

Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.

Zhang, Yuening↗
DOE OSTI2021

Application-driven Privacy-preserving Data Publishing with Correlated Attributes

Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial networks. Given a target application, PR-GAN automatically modifies the data to hide sensitive attributes - which may be hidden and can be inferred by machine learning algorithms - while preserving the data utility in the target application. Unlike prior works, the public's possible knowledge of the correlation between the target application and sensitive attributes is built into our modeling. We formulate our problem as an optimization problem, show that an optimal solution exists and use generative adversarial networks (GAN) to create perturbations. We further show that our method provides privacy guarantees under the Pufferfish framework, an elegant generalization of the differential privacy that allows for the modeling of prior knowledge on data and correlations. Through experiments, we show that our method outperforms conventional methods in effectively hiding the sensitive attributes while guaranteeing high performance in the target application, for both property inference and training purposes. Finally, we demonstrate through further experiments that once our model learns a privacy-preserving task, such as hiding subjects' identity, on a group of individuals, it can perform the same task on a separate group with minimal performance drops.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Synthetic Data Generation Using Machine Learning

Robust machine learning techniques for image analysis require a substantial amount of data to yield confident results. In the nuclear domain, data scarcity is a substantial challenge because there are so few facilities worldwide. This research focuses on being able alleviate the data scarcity problem by generating synthetic data to bridge the gap between large and small datasets. This work achieves that goal using a Generative Adversarial Network (GAN) architectural approach, by training a model on real-world data and expands that small dataset through synthetic data amendments. Model performance is impacted by the size of the real-world dataset and the number of training epochs utilized. This means that 1) It is important to develop your GAN to be optimized with the specific data type, and 2) approaches taken when training the GAN should be specialized to encompass important aspects of the dataset that it is generating. By taking a step to improve dataset sizes in this way, the gap between models trained by parties with significant amount of data and those without access to large data, closes, allowing for robust analyses of satellite imagery for nuclear domain applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Engagement: Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations

We present our SciDAC FASTMath-HEP partnership results for tuning generative adversarial models (GANs) for high energy physics applications. The GANs are used in hybrid simulations to accelerate otherwise time-consuming computations. We optimize for both, prediction accuracy and variability with the goal to find GAN architectures that are reliable and robust.

high energy physics↗