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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The Effects of Gamma Ray Integrated Dose on a Commercial 65-nm SRAM Device

Here this work shows that the static random access memory (SRAM) error rate for a commercial 65-nm device in a dose rate environment can be highly dependent upon the integrated dose (dose rate × pulse duration). While the typical metric for such testing is dose rate upset (DRU) level in rad(Si)/s, a series of dose rate experiments at Little Mountain Test Facility (LMTF) shows dependence on the integrated dose. The error rate is also found to be dependent on the core voltage, and the preradiation value of the bits. We believe that these effects are explained by a well charge depletion caused by gamma ray photocurrent.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Dynamic Testing of a Commercial FRAM Device Under Gamma Ray Dose and Neutron Beam

Here, this work presents dose rate and neutron testing on a ferroelectric random-access memory (FRAM) device in dynamic operation during radiation pulses. Radiation failure modes are shown to be unique. Dose rate failure is shown to be based primarily on integrated dose in the radiation pulse and terminates the FRAM operation and can alter up to 8 bytes of data in storage. Neutron failures are attributed to a shifting of data like a clocking error.

Harris, Nathan↗
DOE OSTI2019

Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP

With ever-increasing volumes of scientific data produced by high-performance computing applications, significantly reducing data size is critical because of limited capacity of storage space and potential bottlenecks on I/O or networks in writing/reading or transferring data. SZ and ZFP are two leading BSD licensed open source C/C++ libraries for compressed floating-point arrays that support high throughput read and write random access. However, their performance is not consistent across different data sets and across different fields of some data sets, which raises the need for an automatic online (during compression) selection between SZ and ZFP, with minimal overhead. In this paper, the automatic selection optimizes the rate-distortion, an important statistical quality metric based on the signal-to-noise ratio. To optimize for rate-distortion, we investigate the principles of SZ and ZFP. We then propose an efficient online, low-overhead selection algorithm that predicts the compression quality accurately for two compressors in early processing stages and selects the best-fit compressor for each data field. Here, we implement the selection algorithm into an open-source library, and we evaluate the effectiveness of our proposed solution against plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results on three data sets representing about 100 fields show that our selection algorithm improves the compression ratio up to 70 percent with the same level of data distortion because of very accurate selection (around 99 percent) of the bestfit compressor, with little overhead (less than 7 percent in the experiments).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Progressive Tree-Based Compression of Large-Scale Particle Data

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Compressive Neural Representations of Volumetric Scalar Fields

Here, we present an approach for compressing volumetric scalar fields using implicit neural representations. Our approach represents a scalar field as a learned function, wherein a neural network maps a point in the domain to an output scalar value. By setting the number of weights of the neural network to be smaller than the input size, we achieve compressed representations of scalar fields, thus framing compression as a type of function approximation. Combined with carefully quantizing network weights, we show that this approach yields highly compact representations that outperform state-of-the-art volume compression approaches. The conceptual simplicity of our approach enables a number of benefits, such as support for time-varying scalar fields, optimizing to preserve spatial gradients, and random-access field evaluation. We study the impact of network design choices on compression performance, highlighting how simple network architectures are effective for a broad range of volumes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Suboxide/subnitride formation on Ta masks during magnetic material etching by reactive plasmas

Etching characteristics of tantalum (Ta) masks used in magnetoresistive random-access memory etching processes by carbon monoxide and ammonium (CO/NH{sub 3}) or methanol (CH{sub 3}OH) plasmas have been examined by mass-selected ion beam experiments with in-situ surface analyses. It has been suggested in earlier studies that etching of magnetic materials, i.e., Fe, Ni, Co, and their alloys, by such plasmas is mostly due to physical sputtering and etch selectivity of the process arises from etch resistance (i.e., low-sputtering yield) of the hard mask materials such as Ta. In this study, it is shown that, during Ta etching by energetic CO{sup +} or N{sup +} ions, suboxides or subnitrides are formed on the Ta surface, which reduces the apparent sputtering yield of Ta. It is also shown that the sputtering yield of Ta by energetic CO{sup +} or N{sup +} ions has a strong dependence on the angle of ion incidence, which suggests a correlation between the sputtering yield and the oxidation states of Ta in the suboxide or subnitride; the higher the oxidation state of Ta, the lower is the sputtering yield. These data account for the observed etch selectivity by CO/NH{sub 3} and CH{sub 3}OH plasmas.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Low-temperature atomic layer deposition of TiO{sub 2} thin layers for the processing of memristive devices

Atomic layer deposition (ALD) represents one of the most fundamental techniques capable of satisfying the strict technological requirements imposed by the rapidly evolving electronic components industry. The actual scaling trend is rapidly leading to the fabrication of nanoscaled devices able to overcome limits of the present microelectronic technology, of which the memristor is one of the principal candidates. Since their development in 2008, TiO{sub 2} thin film memristors have been identified as the future technology for resistive random access memories because of their numerous advantages in producing dense, low power-consuming, three-dimensional memory stacks. The typical features of ALD, such as self-limiting and conformal deposition without line-of-sight requirements, are strong assets for fabricating these nanosized devices. This work focuses on the realization of memristors based on low-temperature ALD TiO{sub 2} thin films. In this process, the oxide layer was directly grown on a polymeric photoresist, thus simplifying the fabrication procedure with a direct liftoff patterning instead of a complex dry etching process. The TiO{sub 2} thin films deposited in a temperature range of 120–230 °C were characterized via Raman spectroscopy and x-ray photoelectron spectroscopy, and electrical current–voltage measurements taken in voltage sweep mode were employed to confirm the existence of resistive switching behaviors typical of memristors. These measurements showed that these low-temperature devices exhibit an ON/OFF ratio comparable to that of a high-temperature memristor, thus exhibiting similar performances with respect to memory applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Ab Initio Transport Calculations: From Normal to Superconducting Current

Applying the Bogoliubov-de Gennes equations with density-functional theory, it is possible to formulate first principles description of current-phase relationships in superconducting/normal (magnetic)/superconducting tri-layers. Such structures are the basis for the superconducting analog of Magnetoresistive random access memory devices (JMRAM). In a recent paper1 we presented results from the first attempt to formulate such a theory, applied to the Nb/Ni/Nb trilayers. In the present work we provide computational details, explaining how to construct key ingredient (scattering matrices SN ) in a framework of linear muffin-tin orbitals (LMTO).

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS↗
DOE OSTI2014

SU-E-T-249: Neutron Model Upgrade for Radiotherapy Patients Monitoring Using a New Online Detector

Purpose: The purpose of this work is to improve the existing methodology to estimate neutron equivalent dose in organs during radiotherapy treatments, based on a Static Random Access Memory neutron detector (SRAMnd) [1]. This is possible thanks to the introduction of a new digital detector with improved characteristics, which is able to measure online the neutron fluence rate in the presence of an intense photon background [2]. Its reduced size, allows the direct estimation of doses in specific points inside an anthropomorphic phantom (NORMA) without using passive detectors as TLD or CR-39. This versatility will allow not only to improve the existing models (generic abdomen and H and N [1]) but to generate more specific ones for any technique. Methods: The new Thermal Neutron Rate Detector (TNRD), based on a diode device sensitized to thermal neutrons, have been inserted in 16 points of the phantom. These points are distributed to infer doses to specific organs. Simultaneous measurements of these devices and a reference one, located in front of the gantry, have been performed for the mentioned generic treatments, in order to improve the existing model. Results: These new devices have shown more precise since they agree better with Monte Carlo simulations. The comparison of the thermal neutron fluence, measured with TNRD, and the existing models, converted from events to fluence, shows an average improvement of (3.90±3.37) % for H and N and (12.61±9.43) % for abdomen, normalized to the maximum value. Conclusion: This work indicates the potential of these new devices for more precise neutron equivalent dose estimation in organs, as a consequence of radiotherapy treatments. The simplicity of the process makes possible to establish more specific models that will provide a better dose estimation. References[1] Phys Med Biol 2012; 57:6167–6191.[2] A new active thermal neutron detector. Radiat. Prot. Dosim. (in press)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Picosecond spin-orbit torque–induced coherent magnetization switching in a ferromagnet

Electrically controllable nonvolatile magnetic memories show great potential for the replacement of conventional semiconductor-based memory technologies. Here, we experimentally demonstrate ultrafast spin-orbit torque (SOT)-induced coherent magnetization switching dynamics in a ferromagnet. We use an ultrafast photoconducting switch and a coplanar strip line to generate and guide a ~9-picosecond electrical pulse into a heavy metal/ferromagnet multilayer to induce ultrafast SOT. We then use magneto-optical probing to investigate the magnetization dynamics with sub-picosecond resolution. Ultrafast heating by the approximately 9 picosecond current pulse induces a thermal anisotropy torque which, in combination with the damping-like torque, coherently rotates the magnetization to obtain zero-crossing of magnetization in ~70 picoseconds. A macro-magnetic simulation coupled with an ultrafast heating model agrees well with the experiment and suggests coherent magnetization switching without any incubation delay on an unprecedented time scale. Our work proposes a unique magnetization switching mechanism toward markedly increasing the writing speed of SOT magnetic random-access memory devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Field-free switching of perpendicular magnetization in a ferrimagnetic insulator with spin reorientation transition

Writing magnetic bits through spin-orbit torque (SOT) switching is promising for fast and efficient magnetic random-access memory devices. While SOT switching of out-of-plane (OOP) magnetized states requires lateral symmetry breaking, in-plane (IP) magnetized states suffer from low storage density. Here, we demonstrate a field-free switching scheme using a 5-nanometer europium iron garnet film grown with a (110) orientation that shows a spin reorientation transition from OOP to IP above room temperature. This scheme combines the benefits of high-density storage in the OOP states at room temperature and the efficient field-free SOT switching in the IP states at elevated temperatures. While conventional switching of OOP bits faces the dilemma that high OOP anisotropy is required to improve bit stability and low OOP anisotropy is required to lower switching current density, this scheme disentangles this interdependence, allowing for low switching currents to be possible without sacrificing the bit stability, offering opportunities for future memory devices.

Science & Technology - Other Topics↗
DOE OSTI2026

Multifunctional electrochemical memory stabilized by phase coexistence

Our growing computing needs, especially in applications that heavily rely on artificial intelligence (AI), motivate a search for new components that could substantially augment the performance of general-purpose digital computers. Beyond ON/OFF switching, new components with linear multistate analog resistive tuning, nonlinear volatile switching, spiking, oscillatory, stochastic and other complex functionalities could enable highly efficient neuromorphic computing schemes for AI information processing. Compared to the extreme multifunctionality of biological neurons, realizing all the above characteristics in a single, scalable analog component remains a grand challenge. Here we investigate electrochemical gating combined with localized thermal activation to program and switch a single, vertically integrated and dimensionally scaled electrothermal chemical random access memory (ETCRAM) with a channel and reservoir composed of phase-separated vanadium oxide. Closely related to electrochemical RAM (ECRAM), ETCRAM uses an integrated gate-heater electrode to overcome kinetic barriers that help retain states at ambient temperatures. In addition to synapse-like stable and programmable analog resistance states arising from redox-tunable phase coexistence, a single component exhibits neuron-like nonlinear conductance switching with a tunable threshold and self-driven dynamics owing to the thermally driven metal-insulator phase transition in vanadium dioxide. More broadly, we demonstrate that electrochemically stabilized phase coexistence could unlock analog electronics with novel functionality, stability, reconfigurability, and scalability.

Oh, Sangheon [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2019

Phase-change heterostructure enables ultralow noise and drift for memory operation

Artificial intelligence and other data-intensive applications have escalated the demand for data storage and processing. New computing devices, such as phase-change random access memory (PCRAM)–based neuro-inspired devices, are promising options for breaking the von Neumann barrier by unifying storage with computing in memory cells. However, current PCRAM devices have considerable noise and drift in electrical resistance that erodes the precision and consistency of these devices. We designed a phase-change heterostructure (PCH) that consists of alternately stacked phase-change and confinement nanolayers to suppress the noise and drift, allowing reliable iterative RESET and cumulative SET operations for high-performance neuro-inspired computing. Our PCH architecture is amenable to industrial production as an intrinsic materials solution, without complex manufacturing procedure or much increased fabrication cost.

Science & Technology - Other Topics↗
DOE OSTI2018

Analysis of the Switching Characteristics of MRAM Cells Based on Materials with Uniaxial Anisotropy

Magnetoresistive random access memory (MRAM) has some advantages over other types of memory. However, MRAM has one substantial drawback: the current density and magnetic field that must be applied to switch the spin-valve free layer inside an MRAM cell are too high. The dependence of the current density and switching magnetic field on the magnetic parameters of the material from which the ferromagnetic layers of a spin valve are fabricated is therefore analyzed. A comparison of the critical characteristics of a spin valve with longitudinal anisotropy shows that cobalt, iron, and alloys of them; cobalt ferroborates; and alloys of cobalt with gadolinium, are promising materials for fabricating spin valves. Bifurcation diagrams of equations that describe the valve switching process are presented and analyzed. The four optimum switching modes of a valve are selected, based on an investigation of the dynamics of the magnetization vector. The magnitudes of the external magnetic field and controlling injection current that correspond to stable MRAM cell switching are compared for a variety of materials. The switching time of an MRAM cell is estimated numerically, and the conditions for its optimum speed are determined. It is established that the most promising materials for spin-valve fabrication are Fe{sub 40}Co{sub 40}B{sub 20} and Co{sub 80}Gd{sub 20}, annealed at 300 and 200°C, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Implementing Arbitrary/Common Concurrent Writes of CRCW PRAM

The Parallel Random Access Machines (PRAM) abstraction is the simplest and most elegant algorithmic model for the design and analysis of parallel algorithms. It consists of different models categorized based on the underlying memory access mode used, the most powerful of which is the Concurrent Read Concurrent Write (CRCW) model. A PRAM algorithm describes a series of rounds, each of which consists of a collection of operations that can be executed concurrently within the same time step. However, the lack of support for concurrent memory accesses and the prevalence of asynchronous programming models led to the belief that implementing CRCW PRAM algorithms is unattainable and prompted many to avoid this model except for theoretical studies of optimal performance.In this work, we study the arbitrary and common concurrent writes in the CRCW PRAM model and explore implementation challenges on general-purpose systems. Moreover, we examine current practices for implementing common/arbitrary concurrent writes and propose a new efficient lightweight and thread-safe method to implement concurrent writes through leveraging atomic instructions. To demonstrate the efficacy of our method, we developed OpenMP kernels for classical CRCW PRAM algorithms and provide experimental results and comparisons based on run time performance measured over the x86 multicore architecture. Our results show a performance speedup compared to current practices up to 4.5x across all our benchmarks.

Ghanim, Fady↗
DOE OSTI2023

DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets

Graph neural networks (GNNs) are a class of Deep Learning models used in designing atomistic materials for effective screening of large chemical spaces. To ensure robust prediction, GNN models must be trained on large volumes of atomistic data on leadership class supercomputers. Even with the advent of modern architectures that consist of multiple storage layers that include node-local NVMe devices in addition to device memory for caching large datasets, extreme-scale model training faces I/O challenges at scale.We present DDStore, an in-memory distributed data store designed for GNN training on large-scale graph data. DDStore provides a hierarchical, distributed, data caching technique that combines data chunking, replication, low-latency random access, and high throughput communication. DDStore achieves near-linear scaling for training a GNN model using up to 1000 GPUs on the Summit and Perlmutter supercomputers, and reaches up to a 6.15x reduction in GNN training time compared to state-of-the-art methodologies.

Choi, Jong Youl↗
DOE OSTI2024

Tailor : Altering Skip Connections for Resource-Efficient Inference

Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and off-chip memory utilization and bandwidth requirements. In this article, we show that skip connections can be optimized for hardware when tackled with a hardware-software codesign approach. We argue that while a network’s skip connections are needed for the network to learn, they can later be removed or shortened to provide a more hardware-efficient implementation with minimal to no accuracy loss. We introduceTailor, a codesign tool whose hardware-aware training algorithm gradually removes or shortens a fully trained network’s skip connections to lower the hardware cost.Tailorimproves resource utilization by up to 34% for block random access memories (BRAMs), 13% for flip-flops (FFs), and 16% for look-up tables (LUTs) for on-chip, dataflow-style architectures.Tailorincreases performance by 30% and reduces memory bandwidth by 45% for a two-dimensional processing element array architecture.

Computer Science↗
DOE OSTI2013

A CMOS Compatible, Forming Free TaO x ReRAM

Resistive random access memory (ReRAM) has become a promising candidate for next-generation high-performance non-volatile memory that operates by electrically tuning resistance states via modulating vacancy concentrations. Here, we demonstrate a wafer-scale process for resistive switching in tantalum oxide that is completely CMOS compatible. The resulting devices are forming-free and with greater than 1x10 5 cycle endurance.

97 MATHEMATICS AND COMPUTING↗