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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Homomorphic data compression for real time photon correlation analysis

The construction of highly coherent X-ray sources, combined with next-generation detectors that are larger and faster, has enabled new research opportunities across the scientific landscape. Among the techniques that benefit most from these advancements is X-ray photon correlation spectroscopy (XPCS), where faster acquisition unlocks the ability to study faster dynamics within samples. However, faster acquisition on larger detectors also introduces unprecedented challenges for online data processing and offline data storage. Such challenges are particularly prominent for XPCS, where real time analyses require simultaneous calculation of all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The offline compression scheme extends storage capacity by a factor of 40 while preserving key features in the lossy compressed data. Meanwhile, the online compression scheme reduces the computational time to below 1 ms, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.

36 MATERIALS SCIENCE↗
NASA NTRS2008

Are Current SEE Test Procedures Adequate for Modern Devices and Electronics Technologies?

Believe it or not, this has been a simplistic look at starting a checklist for SEE testing. Given a memory that has 68 operating modes, when a SEU occurs that changes the mode, just how do you determine what's going on? Laser and microbeam tests can help, but not easily for modern packaged devices. Expanding this approach to other more complex devices such as ADCs or processors as well as analog devices should be considered. The recommendation is to use the existing text standards as the starting point. Just make your own checklist for the device/technology/issues being considered. At HEART 2007, we presented some of the burgeoning challenges associated with single event effect(SEE) testing of modern commercial memories: a) Package, device complexity, test fixture, and data analysis issues were discussed; b) "Complete" SEE Characterization would take 15 years; c) Qualification test costs have a greater than 4 times increase over the last decade. In this talk, we continue to explore the roles of technology with an emphasis on the existing SEE Test Procedures and some of the concerns related to modern devices. The primary objective of the briefing is to provide some overarching guidance concerning the many considerations involved in the formulation of a SEE test plan provided in a " Checklist" format.we note that there is no such thing as a complete check list and that the best approach is to develop a flexible test plan that takes into account the device type and functions, the device technology, circuit and package design, and, of course, test facility and beam characteristics.

LaBel, Kenneth A.↗
NASA NTRS2008

Characterization of a New Phase and Its Effect on the Work Characteristics of a Near-Stoichiometric Ni30Pt20Ti50 High-Temperature Shape Memory Alloy (HTSMA)

A new phase observed in a nominal Ni30Pt20Ti50 (at.%) high temperature shape memory alloy has been characterized using transmission electron microscopy and 3-D atom probe tomography. This phase forms homogeneously in the B2 austenite matrix by a nucleation and growth mechanism and results in a concomitant increase in the martensitic transformation temperature of the base alloy. Although the structure of this phase typically contains a high density of faults making characterization difficult, it appears to be trigonal (-3m point group) with a(sub o) approx. 1.28 nm and c(sub o) approx. 1.4 nm. Precipitation of this phase increases the microhardness of the alloy substantially over that of the solution treated and quenched single-phase material. The effect of precipitation strengthening on the work characteristics of the alloy has been explored through load-biased strain-temperature testing in the solution-treated condition and after aging at 500 C for times ranging from 1 to 256 hours. Work output was found to increase in the aged alloy as a result of an increase in transformation strain, but was not very sensitive to aging time. The amount of permanent deformation that occurred during thermal cycling under load was small but increased with increasing aging time and stress. Nevertheless, the dimensional stability of the alloy at short aging times (1-4 hours) was still very good making it a potentially useful material for high-temperature actuator applications.

Garg, A.↗
DOE OSTI2015

Basics and prospective of magnetic Heusler compounds

Heusler compounds are a remarkable class of materials with more than 1000 members and a wide range of extraordinary multi-functionalities including halfmetallic high-temperature ferri- and ferromagnets, multi-ferroics, shape memory alloys, and tunable topological insulators with a high potential for spintronics, energy technologies, and magneto-caloric applications. The tunability of this class of materials is exceptional and nearly every functionality can be designed. Co{sub 2}-Heusler compounds show high spin polarization in tunnel junction devices and spin-resolved photoemission. Manganese-rich Heusler compounds attract much interest in the context of spin transfer torque, spin Hall effect, and rare earth free hard magnets. Most Mn{sub 2}-Heusler compounds crystallize in the inverse structure and are characterized by antiparallel coupling of magnetic moments on Mn atoms; the ferrimagnetic order and the lack of inversion symmetry lead to the emergence of new properties that are absent in ferromagnetic centrosymmetric Heusler structures, such as non-collinear magnetism, topological Hall effect, and skyrmions. Tetragonal Heusler compounds with large magneto crystalline anisotropy can be easily designed by positioning the Fermi energy at the van Hove singularity in one of the spin channels. Here, we give a comprehensive overview and a prospective on the magnetic properties of Heusler materials.

36 MATERIALS SCIENCE↗
NASA NTRS2003

In-Flight Observations of Long-Term Single Event Effect(SEE)Performance on Orbview-2 and Xray Timing Explorer(XTE)Solid State Recorders (SSR)

This paper presents Single Event Effect (SEE) in-flight data on Solid State Recorders (SSR) that have been collected over a long period of time for two NASA spacecraft: Orbview-2 and XTE. SEE flight data on solid-state memories give an opportunity to study the behavior in space of SEE sensitive commercial devices. The actual Single Event Upset (SEU) rates can be compared with the calculated rates based on environment models and ground test data. The SEE mitigation schemes can also be evaluated in actual implementation. A significant amount of data has already been published concerning observed SEE effects on memories in space. However, most of the data presented cover either a short period of time or a small number of devices. The data presented here has been collected on a large number of devices during several years. This allows statistically significant information about the effect of space weather fluctuations on SEU rates, and the effectiveness of SEE countermeasures used to be analyzed. Only Orbview-2 data is presented in this summary. XTE data will be included in the final paper.

Poivey, Christian↗
NASA NTRS2007

Effects of Stoichiometry on Transformation Temperatures and Actuator-Type Performance of NiTiPd and NiTiPdX High-Temperature Shape Memory Alloys

High-temperature shape memory NiTiPd and NiTiPdX (X=Au, Pt, Hf) alloys were produced with titanium equivalent (Ti+Hf) compositions of 50.5, 50.0, 49.5, and 49.0 at.%. Thermo-mechanical testing in compression was used to evaluate the transformation temperatures, transformation strain, work output, and permanent deformation behavior of each alloy to study the effects of quaternary alloying and stoichiometry on high-temperature shape memory alloy behavior. Microstructural evaluation showed the presence of second phases for all alloy compositions. No load transformation temperatures in the stoichiometric alloys were relatively unchanged by Au and Pt substitutions, while the substitution of Hf for Ti causes a drop in transformation temperatures. The NiTiPd, NiTiPdAu and NiTiPdHf alloys exhibited transformation temperatures that were highest in the Ti-rich compositions, slightly lower at stoichiometry, and significantly reduced when the Ti equivalent composition was less than 50 at.%. For the NiTiPdPt alloy, transformation temperatures were highest for the Ti-rich compositions, lowest at stoichiometry, and slightly higher in the Ni-rich composition. When thermally cycled under constant stresses of up to 300 MPa, all of the alloys had transformation strains, and therefore work outputs, which increased with increasing stress. In each series of alloys, the transformation strain and thus work output was highest for stoichiometric or Ti-rich compositions while permanent strain associated with the constant-load thermal cycling was lowest for alloys with Ni-equivalent-rich compositions. Based on these results, basic rules for optimizing the composition of NiTiPd alloys for actuator performance will be discussed.

Bigelow, Glen S.↗
NASA NTRS2007

Radiation Test Challenges for Scaled Commerical Memories

As sub-100nm CMOS technologies gather interest, the radiation effects performance of these technologies provide a significant challenge. In this talk, we shall discuss the radiation testing challenges as related to commercial memory devices. The focus will be on complex test and failure modes emerging in state-of-the-art Flash non-volatile memories (NVMs) and synchronous dynamic random access memories (SDRAMs), which are volatile. Due to their very high bit density, these device types are highly desirable for use in the natural space environment. In this presentation, we shall discuss these devices with emphasis on considerations for test and qualification methods required.

LaBel, Kenneth A.↗
DOE OSTI2018

Local control of defects and switching properties in ferroelectric thin films

Electric-field switching of polarization is the building block of a wide variety of ferroelectric devices. In turn, understanding the factors affecting ferroelectric switching and developing routes to control it are of great technological significance. This research provides systematic experimental evidence of the role of defects in affecting ferroelectric-polarization switching and utilizes the ability to deterministically create and spatially locate point defects in $PbZr_{0.2}Ti_{0.8}O_3$ thin films via focused-helium-ion bombardment and the subsequent defect-polarization coupling as a knob for on-demand control of ferroelectric switching (e.g., coercivity and imprint). At intermediate ion doses ($0.22-2.2×10^{14}\,ions\,cm^{-2}$), the dominant defects (isolated point defects and small clusters) show a weak interaction with domain walls (pinning potentials from $200-500\,K\,MV\,cm^{-1}$), resulting in small and symmetric changes in the coercive field. At high doses ($0.22-1×10^{15}\,ions\,cm^{-2}$), on the other hand, the dominant defects (larger defect complexes and clusters) strongly pin domain-wall motion (pinning potentials from 500 to $1600\,K\,MV\,cm^{-1}$), resulting in a large increase in the coercivity and imprint, and a reduction in the polarization. This local control of ferroelectric switching provides a route to produce novel functions; namely, tunable multiple polarization states, rewritable pre-determined 180° domain patterns, and multiple zero-field piezoresponse and permittivity states. Such an approach opens up pathways to achieve multilevel data storage and logic, nonvolatile self-sensing shape-memory devices, and nonvolatile ferroelectric field-effect transistors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Persistent optical phenomena in oxide semiconductors

The interaction of transparent oxide semiconductors with light is critically important for a range of applications. Persistent effects could be exploited for holographic memory or optically defined circuits. Conversely, they may also be detrimental to device operation. Large, room-temperature persistent photoconductivity (PPC) was discovered in strontium titanate (SrTiO 3 , STO) after annealing in a hydrogen-containing atmosphere. Barium titanate (BaTiO 3 , BTO), a ferroelectric material, was recently found to also exhibit PPC. Room-temperature photodarkening was observed in Cu-doped gallium oxide (β-Ga 2 O 3 ) after exposure to sub-bandgap light. Hydrogen is believed to play a central role in these persistent phenomena. In the proposed model, a photon excites substitutional hydrogen (a proton inside an oxygen vacancy), making the defect unstable. The proton leaves and binds to a host oxygen atom, forming an O-H bond that is observed with infrared spectroscopy. Here, an oxygen vacancy is left behind. Because oxygen vacancies in STO and BTO are shallow donors, this process results in PPC. In β-Ga 2 O 3 :Cu, however, the oxygen vacancy neighbors a Cu acceptor. In that case, photoexcitation results in the rare Cu 3+ state, which absorbs visible light. The effect can be “erased” by annealing at 300-400°C.

Annealing↗
DOE OSTI2023

Singleton Sieving: Overcoming the Memory/Speed Trade-Off in Exascale k-mer Analysis

Traditional filter data structures, such as Bloom filters, do not offer necessary features that modern high-performance data analytics applications need in order to efficiently perform complex data analysis tasks. For example, MetaHipMer, a de novo metagenome assembler, can use filters to weed out singleton k-mers and reduce memory usage by 30%-70%. However, the filter needs the ability to associate values with k-mers in order to perform the analysis in a single communication pass. Bloom filters do not support value associations and cause the application to perform an extra communication pass, thereby increasing the run time. Therefore, MetaHipMer faces a trade off between memory and speed due to the limited capabilities of traditional filters. In this paper, we overcome the memory and speed trade off in MetaHipMer by integrating a GPU-based feature-rich filter, the Two-Choice filter (TCF), in the MetaHipMer pipeline. The TCF uses key-value association to approximately store k-mers with extensions. This allows MetaHipMer to perform k-mer analysis on the GPUs in a single communication pass. Our empirical analysis shows a 50% reduction in memory usage in k-mer analysis on each node in MetaHipMer without any effect on the overall run time or assembly quality. The memory reduction in turn results in a 43% reduction in the number of nodes required to assemble datasets and enables MetaHipMer to scale to much larger datasets.

McCoy, Hunter↗
DOE OSTI2016

Practice makes imperfect: Working memory training can harm recognition memory performance

There is a great deal of debate concerning the benefits of working memory (WM) training and whether that training can transfer to other tasks. Although a consistent finding is that WM training programs elicit a short-term near-transfer effect (i.e., improvement in WM skills), results are inconsistent when considering persistence of such improvement and far transfer effects. In this study, we compared three groups of participants: a group that received WM training, a group that received training on how to use a mental imagery memory strategy, and a control group that received no training. Although the WM training group improved on the trained task, their posttraining performance on nontrained WM tasks did not differ from that of the other two groups. In addition, although the imagery training group’s performance on a recognition memory task increased after training, the WM training group’s performance on the task decreased after training. Participants’ descriptions of the strategies they used to remember the studied items indicated that WM training may lead people to adopt memory strategies that are less effective for other types of memory tasks. Our results indicate that WM training may have unintended consequences for other types of memory performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Temperature effects in spin-dependent Hall currents in an ideal skyrmion gas

The quest of novel reliable and fast performing logic and memory elements in classical and quantum computing requires discoveries of new effects in novel quantum materials. Skyrmions are such magnetic textures where electron scattering can essentially change the shape and location of the skyrmion, which can be used as a memory element being more efficient than domain walls as memory device in classical computing. Because the skyrmion motion is sensitive even to very small currents, we study electron scattering by skyrmions in an ideal skyrmion gas in ferromagnetic environment. In such systems, the direct and Hall currents become spin-dependent. For applications it is important to consider a Hall effect in the whole range of temperatures under the assumption of the skyrmion existence. In this study we find the nonmonotonic temperature dependence of the direct spin-up conductivity, i. e., the conductivity for the current directed along the applied electric field due to the electrons with the spin parallel to the ferromagnetic moment. Such a behavior contradicts the traditional understanding where the temperature only increases the value of the conductivity. The spin-down Hall conductivity is found to be even more dramatic exhibiting the conductivity sign change (i.e., the change in the current direction) with temperature for small skyrmion sizes. The found effects strongly depend on Fermi energy. The most pronounced dependencies take place if the Fermi energy is slightly below and above the bottom of the upper (spin-down) energy band of an ideal 2D electron gas. In addition, we also find that the direct and Hall resistivities, ρ xx and ρ xy , are independent of the exchange integral J for 2J > ε F .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Large magnetic entropy change and magnetoresistance in a Ni 41 Co 9 Mn 40 Sn 10 magnetic shape memory alloy

A polycrystalline Ni 41 Co 9 Mn 40 Sn 10 (at. %) magnetic shape memory alloy was prepared by arc melting and characterized mainly by magnetic measurements, in-situ high-energy X-ray diffraction (HEXRD), and mechanical testing. A large magnetoresistance of 53.8% (under 5 T) and a large magnetic entropy change of 31.9 J/(kg K) (under 5 T) were simultaneously achieved. Both of these values are among the highest values reported so far in Ni-Mn-Sn-based Heusler alloys. The large magnetic entropy change, closely related to the structural entropy change, is attributed to the large unit cell volume change across martensitic transformation as revealed by our in-situ HEXRD experiment. Furthermore, good compressive properties were also obtained. Lastly, the combination of large magnetoresistance, large magnetic entropy change, and good compressive properties, as well as low cost makes this alloy a promising candidate for multifunctional applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Device Feasibility Analysis of Multi-level FeFETs for Neuromorphic Computing

As an emerging non-volatile memory device technology, Ferroelectric Field-Effect Transistors (FeFETs) can enable low-power, adaptive intelligent system design. However, device dimension and operating voltage dependent reliability issues of scaled FeFETs can ultimately lead to degraded performance in solving machine learning tasks. In this article, detailed experimental characterization of FeFET devices of different dimensions have been carried out to explicitly evaluate the non-ideal behavior in device conductance programming properties like number of programming states, cycle-to-cycle (C2C) variations, device-to-device (D2D) variations, and state retention. A hardware-aware software simulation approach has been adopted to capture the adversarial effects of the non-idealities on recognition accuracy through algorithm-level performance assessment by including them in NeuroSim, a popular neural network hardware simulator, to execute a neural network model considering all other hardware constraints. With the added non-idealities, significant accuracy degradation has been observed compared to the ideal scenarios where D2D variations play the most critical role. Thereafter, feasibility of a variation-aware training method has been evaluated to tackle the accuracy drop.

42 ENGINEERING↗