Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “GAN”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Technology Development for 3-D Wide Swath Imaging Supporting ACE

The National Academy of Sciences Decadal Survey (DS) Aerosol-Cloud-Ecosystems Mission (ACE) aims to advance our ability to observe and predict changes to the Earth's hydrological cycle and energy balance in response to climate forcing, especially those changes associated with the effects of aerosol on clouds and precipitation. ACE is focused on obtaining measurements to reduce the uncertainties in current climate models arising from the lack in understanding of aerosol-cloud interactions. As part of the mission instrument suite, a dual-frequency radar comprised of a fixed-beam 94 gigahertz (W-band) radar and a wide-swath 35 gigahertz (Ka-band) imaging radar has been recommended by the ACE Science Working Group.In our 2010 Instrument Incubator Program project, we've developed a radar architecture that addresses the challenge associated with achieving the measurement objectives through an innovative, shared aperture antenna that allows dual-frequency radar operation while achieving wide-swath (100 kilometers) imaging at Ka-band. The antenna system incorporates 2 key technologies; a) a novel dual-band reflectorreflectarray and b) a Ka-band Active Electronically Scanned Array (AESA) feed module. The dual-band antenna is comprised of a primary cylindrical reflectorreflectarray surface illuminated by a point-focus W-band feed (compatible with a quasi-optical beam waveguide feed, such as that employed on CloudSat); the Ka-band AESA line feed provides wide-swath across-track scanning. The benefits of this shared-aperture approach include significant reductions in ACE satellite payload size, weight, and cost, as compared to a two aperture approach. Four objectives were addressed in our project. The first entailed developing the tools for the analysis and design of reflectarray antennas, assessment of candidate reflectarray elements, and validation using test coupons. The second objective was to develop a full-scale aperture design utilizing the reflectarray surface and to detail specific requirements and trades for the Ka-band AESA line feed. As part of the third objective a subscale antenna, similar to the full-scale aperture design, was developed, integrated, and flown with the Cloud Radar System during the 2014 Integrated Precipitation and Hydrology Experiment. The fourth and ongoing objective entails developing a GaN MMIC (Gallium Nitride Monolithic Microwave Integrated Circuits) power amplifier for use in the Ka-band AESA. An overview of the progress made on this project and a look ahead at the 2013 IIP (Instrument Incubator Program) award selection will be presented.

Antenna↗
NASA NTRS2014

Antenna Technology for 3-D Wide Swath Imaging Supporting ACE

A fundamental goal for the National Academy of Sciences Decadal Survey (DS) Aerosol-Cloud-Ecosystems Mission (ACE) is to advance our ability to observe and predict changes to the Earth's hydrological cycle and energy balance in response to climate forcing, especially those changes associated with the effects of aerosol on clouds and precipitation. ACE is focused on obtaining measurements to reduce the uncertainties in current climate models arising from the lack of understanding of aerosol-cloud interactions. As part of the mission instrument suite, the ACE Science Working Group has recommended a dual-frequency radar comprised of a fixed-beam 94 GHz (W-band) radar and a wide-swath 35 GHz (Ka-band) imaging radar. Our ACE radar architecture addresses the challenge associated with achieving the measurement objectives through an innovative, shared aperture antenna that allows dual-frequency radar operation while achieving wide-swath (>100 km) imaging at Ka-band. The antenna system incorporates 2 key technologies; a) a novel dual-band reflector/reflectarray and b) a Ka-band AESA feed module. The benefits of this shared-aperture approach include significant reductions in ACE satellite payload size, weight, and cost while enabling wide-swath imaging at Ka-band. This presentation describes the approach used to develop the reflectarray surface and plans for its TRL maturation.The dual-frequency antenna is comprised of a primary cylindrical reflector/reflectarray surface illuminated by a fixed W-band feed (compatible with a quasi-optical beam waveguide feed, such as that employed on CloudSat) and a Ka-band AESA line feed. The AESA feed design leverages state-of-the-art GaN transmit/receive (T/R) MMICs with high RF output power, high power added efficiency and low noise figure. The innovative reflectarray surface provides beam focusing at W-band, but it is transparent at Ka-band.

RAdar Antenna↗
NASA NTRS2018

Development of High Performance Detector Technology for UV and Near IR Applications

Sensing and imaging for ultraviolet (UV) and nearinfrared (NIR) bands has many applications forNASA, defense, and commercial systems. Recentwork has involved developing UV avalanchephotodiode (UV-APD) arrays with high gain for highresolution imaging. Various GaN/AlGaN p-i-n (PIN)UV-APDs have been fabricated from epitaxialstructures grown by MOCVD on GaN substrates withavalanche gains higher than 5 x 10(exp 5), and significantlyhigher responsivities. Likewise, the SiGe materialsystem allows the demonstration of high-performancedetector array technology that covers the 0.5 to 1.7 mwavelength range for visible and NIR bands ofinterest. We have utilized SiGe fabrication technologyto develop Ge based PIN detector devices on 300 mmSi wafers. We will discuss the theoretical andexperimental results from electrical and opticalcharacterization of the detector devices with variousn+ region doping concentrations to demonstrate low dark currents below 1 uA at -1 V and high photocurrent. Recent results from these detectorarrays for UV and NIR detection will be presented.

IR↗
NASA NTRS2018

GEONEX: Progressive Conditional Generative Adversarial Training Using Transfer learning

Obtaining accurate segmentation on large scale images is an open problem in deep learning. The main problem is the amount of labeled data that exists for large scale images. Traditionally, the common solution to this problem is to crop the large images into smaller images to increase the amount of available data and train a Conditional Generative Adversarial Network (CGAN). CGANs are currently the state of the art in image to image translation and provide better accuracy than the traditional method of training a encoder based conv-net architecture to minimize the loss at each pixel. This method can produce noisy and discontinuous images with inaccurate results. We seek to solve this problem by utilizing the concepts of transfer learning and progressive training to create a CGAN that can segment large scale images with a limited amount of labeled data. In transfer learning we recognize that many learned features are applicable to many classes from multiple domains. This introduces the concept of feature reusability, which is the basis for finetuning. Progressive training got its start in training models on the same images at different resolutions. In this work we instead train a GAN on increasing image scales by transferring the weights from the smaller scales to the larger scales. The learned features at the smaller scales are continually reused and applied to larger scales to create a CGAN that can perform accurate segmentation on large scale images. We apply this method to detect building footprints on very high-resolution overhead imagery (e.g Digital Globe and high resolution airborne platforms).

GEONEX↗
NASA NTRS2019

High Performance GaN/AlGaN Ultraviolet Avalanche Photodiode Detector Technologies

Detection of ultraviolet (UV) bands provides distinct advantages for NASA, defense, and commercial applications, including increased spatial resolution, small pixel sizes, and large format arrays. AlxGa1-xN semiconductor alloys have attracted great interest for detection in the UV spectral region because of their potential for high optical gain, high sensitivity, and low dark current performance in ultraviolet avalanche photodiodes (UV-APDs). We are developing GaN/AlGaN UV-APDs that demonstrate consistent and reliable UV-APD performance and operation. For these UV detectors we have measured gains of above 5×10(exp 6) and high quantum efficiencies at ~350 nm enabled by a strong avalanche multiplication process. These UV-APDs are fabricated through high quality metal organic chemical vapor deposition (MOCVD) growth on lattice-matched, low dislocation density GaN substrates with optimized GaN/AlGaN UV-APD material growth and doping parameters. The high performance, variable-area GaN/AlGaN UV-APD detectors and arrays can be customized to a wide variety of sizes including large-area formats to enable sensing and high-resolution detection over UV bands of interest.

Sood, Ashok K.↗
NASA NTRS2019

Development of High-Performance Detector Technology for UV and IR Applications

Sensing and imaging for ultraviolet (UV) and infrared (IR) bands has many applications for NASA, defense, and commercial systems. Recent work has involved developing UV avalanche photodiode (UVAPD) arrays with high gain for high resolution imaging. Various GaN/AlGaN p-i-n UV-APDs have been fabricated from epitaxial structures grown by metalorganic chemical vapor deposition (MOCVD) on GaN substrates with avalanche gains greater than 5×105, and high responsivities. Similarly, the IR spectral band is useful for measuring ocean temperatures, atmospheric aerosols, forest fires, etc. We are also developing room temperature operating graphene-enhanced PbSe midwave infrared (MWIR) detectors and focal plane arrays (FPAs). These compact and low-cost MWIR sensors can benefit various NASA remote sensing applications. Here we present recent results from these high performance UV- and IR-band detector and FPA technologies.

Sood, Ashok K.↗
NASA NTRS2020

Characterization and Commissioning of a Ka-Band Ground Station for Cognitive Algorithm Development

In 2018, the Cognitive Communications and Propagation projects completed installation and checkout testing of a new Ka-Band ground station at the NASA Glenn Research Center in Cleveland, Ohio. The Cognitive Algorithms Demonstration Testbed (CADeT) was developed to provide a fully characterized and controllable dynamic link environment to researchers looking to demonstrate hardware and software aligned with atmospheric sensing and cognitive algorithms. CADeT integrates a host of precision control and measurement systems in addition to repurposing a 5.5 meter beam-waveguide dish platform previously used with the Advanced Communications Technology Satellite (ACTS). This paper will discuss the laboratory testing of ground station components with a emphasis on elements vital to achieving link budget requirements including characterization of the new Gallium Nitride (GaN) Solid State Power Amplifier (SSPA) and far-field measurements of the new antenna feed. Finally, the paper discusses in-situ tests conducted with CADeT and the Tracking and Data Relay Satellite System (TDRSS) to validate laboratory results and make necessary link budget adjustments before reviewing the lessons learned.

Cameron M Seidl↗
NASA NTRS2019

GaN/AlGaN Avalanche Photodiode Detectors for High Performance Ultraviolet Sensing Applications

The shorter wavelengths of the ultraviolet (UV) band enable detectors to operate with increased spatial resolution, variable pixel sizes, and large format arrays, benefitting a variety of NASA, defense, and commercial applications. AlxGa1-xN semiconductor alloys, which have attracted much interest for detection in the UV spectral region, have been shown to enable high optical gains, high sensitivities with the potential for single photon detection, and low dark current performance in ultraviolet avalanche photodiodes (UV-APDs). We are developing GaN/AlGaN UV-APDs with large pixel sizes that demonstrate consistent and uniform device performance and operation. These UV-APDs are fabricated through high quality metal organic chemical vapor deposition (MOCVD) growth on lattice-matched, low dislocation density GaN substrates with optimized material growth and doping parameters. The use of these low defect density substrates is a critical element to realizing highly sensitive UV-APDs and arrays with suppressed dark current under high electric fields.Optical gains greater than 5X10 (exp 6) with enhanced quantum efficiencies over the 350-400 nm spectral range have been demonstrated, enabled by a strong avalanche multiplication process. Furthermore, we are developing 6X6 arrays of devices to test high gain UV-APD array performance at ~355 nm. These variable-area GaN/AlGaN UV-APD detectors and arrays enable advanced sensing performance over UV bands of interest with high resolution detection for NASA Earth Science applications.

Sood, Ashok K.↗
NASA NTRS2020

Performance Evaluation of Isolated Gate Driver, ADuM4121, Under Exposure to Extreme Temperature

Electronics designed for use in space missions are expected to be exposed to harsh environment including radiation and extreme temperature, to name a few. Severe thermal swings are also encountered depending on planetary exploration, orbital orientation, and mission duration. Most commercial-off-the-shelf (COTS) devices are not designed to function under such extreme conditions and very little data exist on their performance outside their specified range of operation. In this work, the performance of an isolated gate driver for controlling power-level transistors, including gallium nitride (GaN) and silicon carbide (SiC) devices, was evaluated under extreme temperatures and thermal cycling. The investigations were carried out to assess performance for potential use of this device in space exploration missions under extreme temperature conditions.

Boomer, Kristen↗
NASA NTRSDate not supplied

Wide Bandgap Reliability and Application Guidelines

Wide Bandgap (WBG) semiconductors, such as silicon carbide (SiC) and gallium nitride (GaN) have emerged as promising technologies due to the advantages they offer over silicon (Si). Under the NASA Electronic Parts and Packaging (NEPP) Program, Glenn Research Center (GRC) supports a task called "Wide Bandgap Reliability and Application Guidelines." This presentation summarizes the work performed under this task, to be presented at the 2020 NEPP Electronics Technology Workshop in June 2020.

Wide Bandgap Semiconductors↗
NASA NTRS2013

Single-Event Effect Report for EPC Series eGaN FETs: EPC1001, EPC1010, EPC1014, EPC1012

Heavy ion testing of newly available GaN FETs from EPC were tested in March of 2012 at TAM. The EPC1010, EPC1001, EPC1012, and EPC1014 were tested for general radiation response from gold and xenon ions. Overall the devices showed radiation degradation commensurate with breakdown in isolation oxides, and similar testing by EPC and Microsemi agrees with these data. These devices were the first generation production of the device called Gen1. Gen2 parts are scheduled for later in the third quarter of FY2012

Scheick, Leif↗
NASA NTRSDate not supplied

Design of 1 kW Simplified Stirling Controller Using Capacitor-Based Power Factor Correction

Free-piston Stirling convertors hold potential for efficient, reliable thermal to electric power conversion, provided they are paired with a robust controller. This paper outlines work underway on a system-level optimization of a kW controller. Using simplified control and passive power factor correction (PFC) this design explores the possibility of reducing programmatic risk through system simplification. Efficiency is improved through incorporating wide-bandgap gallium nitride (GaN) switches, and PFC volume is minimized through the use of polymer multi-layer capacitors. This work is aimed at exploring new approaches for future Stirling space power concepts.

Stirling↗
NASA NTRSDate not supplied

Optimization, Design, and Demonstration of 1 kW Stirling Controller Using Capacitor-based Power Factor Correction

Free-piston Stirling convertors provide efficient, reliable thermal to electric power conversion, provided they are paired with a reliable controller. This paper outlines work underway on design concepts for kilowatt-class controllers. Using passive power factor correction (PFC) this design explores the possibility of reducing programmatic risk through system simplification. Efficiency is maximized through incorporating wide-bandgap gallium nitride (GaN) switches, and passive PFC volume is minimized through the use of polymer multi-layer capacitors. This work is aimed at exploring new approaches for future Stirling controllers.

Christopher Barth↗
NASA NTRS2019

Design and optimization of radiation-hardened isolated converters for Jovian environments

This paper presents the design and optimization of a class of flexible, high efficiency, multiple output capable, radiation-hardened isolated DC/DC converters for Jovian environments. The proposed converters achieve lower electromagnetic emission, better thermal management and higher efficiency. A new series of radiation-hardened isolated GaN gate driver circuit is proposed for space applications. Experimental results based on two prototype units implemented in the laboratory verify the performance of the proposed isolated converters.

Carr, Gregory↗
NASA NTRS2021

Spectral Synthesis for Geostationary Satellite-to-Satellite Translation

Earth-observing satellites carrying multispectral sensors are widely used to monitor the physical and biological states of the atmosphere, land, and oceans. These satellites have different vantage points above the Earth and different spectral imaging bands resulting in inconsistent imagery from one to another. This presents challenges in building downstream applications. What if we could generate synthetic bands for existing satellites from the union of all domains? We tackle the problem of generating synthetic spectral imagery for multispectral sensors as an unsupervised image-to-image translation problem modeled with a variational autoencoder (VAE) and generative adversarial network (GAN) architecture. Our approach introduces a novel shared spectral reconstruction loss to constrain the high-dimensional feature space of multispectral images. Simulated experiments performed by dropping one or more spectral bands show that cross-domain reconstruction outperforms measurements obtained from a second vantage point. Our proposed approach enables the synchronization of multispectral data and provides a basis for more homogeneous remote sensing datasets.

Geostationary satellites↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRS2021

Infrared Imaging using Sparse Microresonator Arrays and Compressed Sensing

In this work, we present an uncooled infrared (IR) thermal imager featuring a sparse array of resonant pixels that can be monolithically integrated with the readout electronics for long-term operation in high-temperature environments. Each of the detector pixels is made of an IR-sensitive GaN microresonator developed to be operated at up to 500 0C. The pixels are employed in sparse array configuration to allow for routing the signal carrying traces and to ease the routing from pixel to circuit, as well as to reduce the power consumption at the expense of moderate post-processing time and increased complexity to recover the image. Three 128 by 128 sparse resonator arrays with the array densities of 10%, 25% and 37.5% are designed, and used in conjunction with the Compressed Sensing (CS) method to reconstruct complex test images from highly incomplete data. Capabilities and performance metrics of the recovery technique are explored.

Rais-Zadeh, Mina↗