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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Effects of structural defects on optical properties of In x Ga 1-x N layers and quantum wells

This review concentrates on the microstructure of In x Ga 1-x N layers and quantum wells (QWs) in relation to their optical properties. The microstructure of In x Ga 1-x N, with a constant In(x) concentration, shifts with layer thickness. Only layers below 100 nm for x = 0.1 are nearly defect-free. A photoluminescence peak is observed at 405 nm, in line with ~10% In, suggesting band-edge luminescence. Layers with greater thickness and In content present a corrugated surface with numerous structural defects, including V-defects, causing redshifts and multi-peaks in photoluminescence up to 490 nm. These defects, resembling those in GaN, lead to a corrugated sample surface. Atomic force microscopy shows a 3.7-fold larger corrugation in samples with 20 QWs compared to those with 5 QWs measured on 2 × 2 μm 2 areas. Like in GaN, dual growth on different crystallographic planes results in varied QW thicknesses, influencing optical traits of devices made from In x Ga 1-x N layers. The purpose of this review and the chosen subject is to highlight the significant contribution of Wladek Walukiewicz and his group to the current research on the properties of In x Ga 1-x N, which are crucial alloys in the field of optoelectronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

High conductivity coherently strained quantum well XHEMT heterostructures on AlN substrates with delta doping

Polarization-induced two-dimensional electron gases (2DEGs) in AlN/GaN/AlN quantum well high-electron-mobility transistors on ultrawide bandgap AlN substrates offer a promising route to advance microwave and power electronics with nitride semiconductors. The electron mobility in thin GaN quantum wells embedded in AlN is limited by high internal electric field and the presence of undesired polarization-induced two-dimensional hole gases (2DHGs). To enhance the electron mobility in such heterostructures on AlN, previous efforts have resorted to thick, relaxed GaN channels with dislocations. In this work, we introduce n-type compensation δ-doping in a coherently strained single-crystal (Xtal) AlN/GaN/AlN heterostructure to counter the 2DHG formation at the GaN/AlN interface, and simultaneously lower the internal electric field in the well. This approach yields a δ-doped XHEMT structure with a high 2DEG density of ∼3.2×1013 cm−2 and a room temperature (RT) mobility of ∼855 cm2/Vs, resulting in the lowest RT sheet resistance 226.7 Ω/□ reported to date in coherently strained AlN/GaN/AlN HEMT heterostructures on the AlN platform.

Physics↗
DOE OSTI2025

THz cyclotron resonance of a 2D hole gas in a GaN/AlN heterostructure

The recent discovery of highly conducting two-dimensional hole gases (2DHGs) in GaN/AlN heterojunctions has opened the door to efficient complementary GaN electronics, a long-standing challenge in wide-bandgap semiconductor device physics. Electrical transport studies and simulations indicate that both heavy- and light-hole valence bands are occupied in these 2DHGs, but direct experimental characterization of the fundamental parameters of the mobile holes remains at an early stage. Here, we use time-domain terahertz spectroscopy and pulsed magnetic fields up to 31 T to directly measure cyclotron resonance of the mobile 2D holes in these GaN-based 2DHGs at low temperature (8 K), revealing key material properties including effective masses, densities, scattering times, and mobilities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Origin of proton irradiation-induced deep acceptors in Al 0.70 Ga 0.30 N

Deep level defect introduction and carrier removal were characterized using steady-state photocapacitance (SSPC), deep level transient spectroscopy (DLTS) and lighted capacitance–voltage for proton irradiated n-type Al 0.70 Ga 0.30 N Schottky diodes grown by metal-organic vapor phase epitaxy on AlN-on-sapphire templates. SSPC observed deep levels in the as-grown diode with zero-phonon transition energies of 2.20, 2.65, 3.10, 3.40, and 4.65 eV relative to the conduction band minimum (E c ), and an additional deep level emerged at 1.20 eV with irradiation. Lighted capacitance–voltage measurements quantified the deep level concentration (N t ) of states detected by SSPC, and it was observed that N t increased with proton fluence only for the 1.2 and 4.65 eV levels. Carrier removal was much larger than the increase in N t of the 1.2 and 4.65 eV deep levels, suggesting that radiation-induced deep level compensators existed beyond what was detected with SSPC. DLTS detected additional, proton-induced deep acceptors at E c —0.55, 0.82, and 1.16 eV, the latter of which is likely the same 1.20 eV deep state observed by SSPC. The concentration of the E c —0.82 eV defect state was large enough to reconcile carrier removal with total deep level introduction. Comparing the E c —0.82 and 1.16 eV deep acceptor levels to previous experimental and theoretical reports suggests that their atomistic origins could be the nitrogen vacancy (V N ) and oxygen substituting on the nitrogen sub-lattice (O N ), respectively. This defect behavior contrasts starkly with GaN, where V N and O N are shallow donors, and demonstrates that the electronic properties of defects can evolve drastically within the AlGaN alloy system ranging from wide bandgap GaN to ultra-wide bandgap AlN.

Armstrong, Andrew Michael [Sandia National Laborat↗
DOE OSTI2023

Heavy ion irradiation induced failure of gallium nitride high electron mobility transistors: effects of in-situ biasing

Abstract While radiation is known to degrade AlGaN/GaN high-electron-mobility transistors (HEMTs), the question remains on the extent of damage governed by the presence of an electrical field in the device. In this study, we induced displacement damage in HEMTs in both ON and OFF states by irradiating with 2.8 MeV Au 4+ ion to fluence levels ranging from 1.72 × 10 10 to 3.745 × 10 13 ions cm −2 , or 0.001–2 displacement per atom (dpa). Electrical measurement is done in situ , and high-resolution transmission electron microscopy (HRTEM), energy dispersive x-ray (EDX), geometrical phase analysis (GPA), and micro-Raman are performed on the highest fluence of Au 4+ irradiated devices. The selected heavy ion irradiation causes cascade damage in the passivation, AlGaN, and GaN layers and at all associated interfaces. After just 0.1 dpa, the current density in the ON-mode device deteriorates by two orders of magnitude, whereas the OFF-mode device totally ceases to operate. Moreover, six orders of magnitude increase in leakage current and loss of gate control over the 2-dimensional electron gas channel are observed. GPA and Raman analysis reveal strain relaxation after a 2 dpa damage level in devices. Significant defects and intermixing of atoms near AlGaN/GaN interfaces and GaN layer are found from HRTEM and EDX analyses, which can substantially alter device characteristics and result in complete failure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Phonon Screening of Excitons in Atomically Thin Semiconductors

Atomically thin semiconductors, encompassing both 2D materials and quantum wells, exhibit a pronounced enhancement of excitonic effects due to geometric confinement. Consequently, these materials have become foundational platforms for the exploration and utilization of excitons. Recent ab initio studies have demonstrated that phonons can substantially screen electron-hole interactions in bulk semiconductors and strongly modify the properties of excitons. While excitonic properties of atomically thin semiconductors have been the subject of extensive theoretical investigations, the role of phonon screening on excitons in atomically thin structures remains unexplored. In this Letter, we demonstrate via ab initio GW-Bethe-Salpeter equation calculations that phonon screening can have a significant impact on optical excitations in atomically thin semiconductors. We further show that the degree of phonon screening can be tuned by structural engineering. We focus on atomically thin GaN quantum wells embedded in AlN and identify specific phonons in the surrounding material, AlN, that dramatically alter the lowest-lying exciton in monolayer GaN via screening. Our studies provide new intuition beyond standard models into the interplay among structural properties, phonon characteristics, and exciton properties in atomically thin semiconductors, and have implications for future experiments.

2-dimensional systems↗
DOE OSTI2021

Adaptive Time-Division Multiplexing Driving System for Solid State Lighting with Multiple Tunable Channels

Next generation solid state lighting enables unlimited control of light and serve much broader functions beyond the basic lighting for illuminance. The emerging lighting for productivity, well-being, healthcare, and growth have revolutionized the scope of lighting. To empower this unlimited controllability, a multi-channel tunable power-electronics driver is essential but has not been satisfactorily addressed so far. Existing solutions are lossy, large, and expensive, due to introduction of additional buck power conversion stages. This paper proposes a time-division multiplexing (TDM) LED driving system which eliminates those buck stages. The proposed design can directly pair with off-the-shelf pulse width modulated (PWM) controllers and since it inherits entire feature sets, it can achieve low loss at standby and at dimmed condition. The simple analog implementation avoids usage of expensive DSPs/MCU. GaN’s high switching frequency capability can push up the multiplexing frequency so a GaN-based flyback was built and proved the concept.

Xue, Lincoln↗
DOE OSTI2021

FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper

The outbreak of the global COVID-19 pandemic emphasizes the importance of collaborative drug discovery for high effectiveness; however, due to the stringent data regulation, data privacy becomes an imminent issue needing to be addressed to enable collaborative drug discovery. In addition to the data privacy issue, the efficiency of drug discovery is another key objective since infectious diseases spread exponentially and effectively conducting drug discovery could save lives. Advanced Artificial Intelligence (AI) techniques are promising to solve these problems: (1) Federated Learning (FL) is born to keep data privacy while learning data from distributed clients; (2) graph neural network (GNN) can extract structural properties of molecules whose underlying architecture is the connected atoms; and (3) generative adversarial network (GAN) can generate novel molecules while retaining the properties learned from the training data. In this work, we make the first attempt to build a holistic collaborative and privacy-preserving FL framework, namely FL- DISCO, which integrates GAN and GNN to generate molecular graphs. Experimental results demonstrate the effectiveness of FL- DISCO on: (1) IID data for ESOL and QM9, where FL-DISCO can generate highly novel compounds with high drug-likeliness, uniqueness and LogP scores compared to the baseline; (2) non- IID data for ESOL and QM9, where FL-DISCO generates 100% novel compounds with high validity and LogP scores compared to the baseline. We also demonstrate how different fractions of clients, generator and discriminator architectures affect our evaluation scores.

Manu, Daniel↗
DOE OSTI2022

Online Power System Event Detection via Bidirectional Generative Adversarial Networks

Accurate and speedy detection of power system events is critical to enhancing the reliability and resiliency of power systems. Although supervised deep learning algorithms show great promise in identifying power system events, they require a large volume of high-quality event labels for training. This paper develops a bidirectional anomaly generative adversarial network (GAN)-based algorithm to detect power system events using streaming PMU data, which does not rely on a huge amount of event labels. By introducing conditional entropy constraint in the objective function of GAN and graph signal processing-based PMU sorting technique, our proposed algorithm significantly outperforms state-of-the-art event detection algorithms in terms of accuracy. To facilitate the adoption of the proposed algorithm, a prototype online platform is also developed using Apache Hadoop, Kafka, and Spark to enable real-time event detection. Here, the accuracy and computational efficiency of the proposed algorithm are validated using a large-scale real-world PMU dataset from the Eastern Interconnection of the United States.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Inverse Design of Two-Dimensional Airfoils Using Conditional Generative Models and Surrogate Log-Likelihoods

Abstract This paper shows how to use conditional generative models in two-dimensional (2D) airfoil optimization to probabilistically predict good initialization points within the vicinity of the optima given the input boundary conditions, thus warm starting and accelerating further optimization. We accommodate the possibility of multiple optimal designs corresponding to the same input boundary condition and take this inversion ambiguity into account when designing our prediction framework. To this end, we first employ the conditional formulation of our previous work BézierGAN–Conditional BézierGAN (CBGAN)—as a baseline, then introduce its sibling conditional entropic BézierGAN (CEBGAN), which is based on optimal transport regularized with entropy. Compared with CBGAN, CEBGAN overcomes mode collapse plaguing conventional GANs, improves the average lift-drag (Cl/Cd) efficiency of airfoil predictions from 80.8% of the optimal value to 95.8%, and meanwhile accelerates the training process by 30.7%. Furthermore, we investigate the unique ability of CEBGAN to produce a log-likelihood lower bound that may help select generated samples of higher performance (e.g., aerodynamic performance). In addition, we provide insights into the performance differences between these two models with low-dimensional toy problems and visualizations. These results and the probabilistic formulation of this inverse problem justify the extension of our GAN-based inverse design paradigm to other inverse design problems or broader inverse problems.

Engineering↗
DOE OSTI2022

Optimization of InGaN quantum well interfaces for fast interwell carrier transport and low nonradiative recombination

Efficient high-power operation of light emitting diodes based on InGaN quantum wells (QWs) requires rapid interwell hole transport and low nonradiative recombination. The transport rate can be increased by replacing GaN barriers with that of InGaN. Introduction of InGaN barriers, however, increases the rate of the nonradiative recombination. In this work, we have attempted to reduce the negative impact of the nonradiative recombination by introducing thin GaN or AlGaN interlayers at the QW/barrier interfaces. The interlayers, indeed, reduce the nonradiative recombination rate and increase the internal quantum efficiency by about 10%. Here, the interlayers do not substantially slow down the interwell hole transport; for 0.5 nm Al 0.10 Ga 0.90 N interlayers the transport rate has even been found to increase. Another positive feature of the interlayers is narrowing of the QW PL linewidth, which is attributed to smoother QW interfaces and reduced fluctuations of the QW width.

carrier transport↗
DOE OSTI2023

Demonstration of p-side down green light emitting diodes with high external quantum efficiencies

GaN/(In,Ga)N heterostructure based visible Light Emitting Diodes (LED) have enabled a wide range of solid-state lighting applications through excellent efficiency and power output in the shorter wavelengths (≤ 475nm) of violet/blue emission. However, the efficiency of emitters in the longer wavelength range (≥ 500nm) drops drastically due to the need to include higher Indium-content in the InGaN quantum wells. Large average polarization fields for high Indium-content quantum wells for conventional P-up structure, opposes the depletion field leading to large electrostatic barriers for both electrons and holes injection. LEDs fabricated along the N-polar direction with a p-up orientation or Ga-polar direction with p-down orientation lower such electrostatic barriers to carrier injection due to alignment of the polarization dipole field and depletion region field. This can therefore theoretically improve the electrical injection efficiency and reduce the forward voltage of operation. Such a Ga-polar p-down LED requires a bottom buried tunnel junction to avoid current spreading issues for a buried p-GaN layer. In this report, we demonstrate for the first time Ga-polar p-down green emitting LEDs using bottom tunnel junctions and having external quantum efficiencies comparable to those of equivalent p-up LEDs grown by Metal Organic Chemical Vapor Deposition (MOCVD).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Vertical Structure of Radiative Heating Rates of the MJO during DYNAMO

The vertical structure of radiative heating rates over the region of the tropi-cal Indian Ocean associated with the MJO during the DYNAMO/ARM MJO Investigation Experiment is presented. The mean and variability of heating rates during active, suppressed, and disturbed phases are determined from the Paci?c Northwest National Laboratory combined remote sensing retrieval (CombRet) from Gan Island, Maldives (0.69? S, 73.15? E). TOA and surface ?uxes from the CombRet product are compared with collocated three-hourly CERES SYN1deg Ed4A satellite retrievals. The ?uxes are correlated in time with correlation coef?cients around 0.9, yet CombRet time-mean OLR is 15 W m-2 larger. Previous work has suggested that CombRet under-samples high clouds, due to attenuation by low-level clouds and reduced instrument sensitivity with altitude. However, mean OLR differs between CombRet and CERES for all values of OLR, not just the lowest values corresponding to widespread high clouds. The discrepancy peaks for mid-range OLR, sugges-tive of precipitating, towering cumulus convective clouds, rather than strati-form cirrus clouds. Biases in the cloud-top height of thick clouds substantially contribute to the overestimate of OLR by CombRet. CombRet data is used to generate composite shortwave and longwave atmospheric heating rate pro-?les as a function of the local OLR. Though there is considerable variability in CombRet not directly related to OLR, the time-height structure of mean heating rate composites generated using OLR as the interpolant is broadly representative of tropical convective variability on intraseasonal time scales.

Shell, Karen M.↗
DOE OSTI2020

ECP Report: Update on Proxy Applications and Vendor Interactions

The ExaLearn miniGAN team (Ellis and Rajamanickam) have released miniGAN, a generative adversarial network(GAN) proxy application, through the ECP proxy application suite. miniGAN is the first machine learning proxy application in the suite (note: the ECP CANDLE project did previously release some benchmarks) and models the performance for training generator and discriminator networks. The GAN's generator and discriminator generate plausible 2D/3D maps and identify fake maps, respectively. miniGAN aims to be a proxy application for related applications in cosmology (CosmoFlow, ExaGAN) and wind energy (ExaWind). miniGAN has been developed so that optimized mathematical kernels (e.g., kernels provided by Kokkos Kernels) can be plugged into to the proxy application to explore potential performance improvements. miniGAN has been released as open source software and is available through the ECP proxy application website (https://proxyapps.exascaleproject.ordecp-proxy-appssuite/) and on GitHub (https://github.com/SandiaMLMiniApps/miniGAN). As part of this release, a generator is provided to generate a data set (series of images) that are inputs to the proxy application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

miniGAN: A Generative Adversarial Network proxy application WBS 2.2.6.08 ECP-2.1.3 (Q3 FY2020 Milestone Report) (V.1.0)

In order to support the machine learning co-design needs of ECP applications in current and future DOE HPC hardware, we have developed a generative adversarial network (GAN) proxy application, miniGAN, that has been released through the ECP proxy application suite. The proxy application is representative of the needs of ExaLearn's target applications, specifically the Cosmoflow and ExaGAN cosmology applications and the ExaWind energy application. The proxy application also demonstrates the first use of performance portable kernels within widely-used machine learning frameworks: PyTorch (Facebook) and Horovod (Uber). We provide performance scaling results for similar workloads to ExaGAN and a profile of individual GAN training components.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Analysis of Defects in Metal Additive Manufacturing with Augmented Data Generation

Laser powder bed fusion (LPBF) is a method of additive manufacturing (AM) that selectively melts and fuses together microscopic metallic powder. LPBF offers the benefit of producing custom structures out of high strength metals that can be difficult to fabricate with conventional methods. The challenge of LPBF is that 3D printed structures often have internal pores due to process flaws. Pulsed thermal tomography (PTT) is a method for reconstructing the depth profile of materials, allowing the visualization internal voids in solids. In prior work, we developed a convolutional neural network (CNN) which, having been trained on simulated 2D PTT images of subsurface elliptical defects, was able to classify the semi-major radii, semi-minor radii, and angular orientation of the best-fit ellipses in previously unseen PTT images. The unseen PTT images contained subsurface irregular defect shapes imported from scanning electron microscopy (SEM) images of metallic LPBF-printed specimens. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Gallium Nitride Superjunction Transistor (Continued Funding Report)

The initial goal of this project is to develop the gallium vacancy assisted diffusion (GAID) method in GaN and apply it to demonstrate 2-terminal, 3-terminal, and large area Superjunction-enhanced modules. These devices will find use in high efficiency power conversion applications with the capability of operating at higher voltages, higher frequency, and less loss than comparable stat-ofthe-art devices. The approach of this project has been to utilize simulation tools to drive diffusion experiments and inform semiconductor device fabrication processes. VASP has been relied upon to simulate first principles diffusion mechanisms while SILVACO TCAD has been utilized to understand tradeoffs of the the critical dimensions in GaN devices.

42 ENGINEERING↗
DOE OSTI2022

Emulation Modeling for Development of Cyber-Defense Capabilities for Satellite Systems

The objective of this project was to develop a novel capability to generate synthetic data sets for the purpose of training Machine Learning (ML) algorithms for the detection of malicious activities on satellite systems. The approach experimented with was to a) generate sparse data sets using emulation modeling and b) enlarge the sparse data using Generative Adversarial Networks (GANs). We based our emulation modeling on the Open Source NASA Operational Simulator for Small Satellites (NOS3) developed by the Katherine Johnson Independent Verification and Validation (IV&V) program in West Virginia. Significant new capabilities on NOS3 had to be developed for our data set generation needs. To expand these data sets for the purpose of training ML, we experimented with a) Extreme Learning Machines (ELMs) and b) Wasserstein-GANs (WGAN-GP).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗