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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Effect of tunneling layers on the performances of floating-gate based organic thin-film transistor nonvolatile memories

Two types of floating-gate based organic thin-film transistor nonvolatile memories (FG-OTFT-NVMs) were demonstrated, with poly(methyl methacrylate co glycidyl methacrylate) (P(MMA-GMA)) and tetratetracontane (TTC) as the tunneling layer, respectively. Their device performances were measured and compared. In the memory with a P(MMA-GMA) tunneling layer, typical unipolar hole transport was obtained with a relatively small mobility of 0.16 cm{sup 2}/V s. The unidirectional shift of turn-on voltage (V{sub on}) due to only holes trapped/detrapped in/from the floating gate resulted in a small memory window of 12.5 V at programming/erasing voltages (V{sub P}/V{sub E}) of ±100 V and a nonzero reading voltage. Benefited from the well-ordered molecule orientation and the trap-free surface of TTC layer, a considerably high hole mobility of 1.7 cm{sup 2}/V s and a visible feature of electrons accumulated in channel and trapped in floating-gate were achieved in the memory with a TTC tunneling layer. High hole mobility resulted in a high on current and a large memory on/off ratio of 600 at the V{sub P}/V{sub E} of ±100 V. Both holes and electrons were injected into floating-gate and overwritten each other, which resulted in a bidirectional V{sub on} shift. As a result, an enlarged memory window of 28.6 V at the V{sub P}/V{sub E} of ±100 V and a zero reading voltage were achieved. Based on our results, a strategy is proposed to optimize FG-OTFT-NVMs by choosing a right tunneling layer to improve the majority carrier mobility and realize ambipolar carriers injecting and trapping in the floating-gate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Near-field NanoThermoMechanical memory

In this letter, we introduce the concept of NanoThermoMechanical Memory. Unlike electronic memory, a NanoThermoMechanical memory device uses heat instead of electricity to record, store, and recover data. Memory function is achieved through the coupling of near-field thermal radiation and thermal expansion resulting in negative differential thermal resistance and thermal latching. Here, we demonstrate theoretically via numerical modeling the concept of near-field thermal radiation enabled negative differential thermal resistance that achieves bistable states. Design and implementation of a practical silicon based NanoThermoMechanical memory device are proposed along with a study of its dynamic response under write/read cycles. With more than 50% of the world's energy losses being in the form of heat along with the ever increasing need to develop computer technologies which can operate in harsh environments (e.g., very high temperatures), NanoThermoMechanical memory and logic devices may hold the answer.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Persistent Octrees for Parallel Mesh Refinement Through Non-Volatile Byte-Addressable Memory

We report that octree-based mesh adaptation has enabled simulations of complex physical phenomena. Existing meshing algorithms were proposed with the assumption that computer memory is volatile. Consequently, for failure recovery, the in-core algorithms need to save memory states as snapshots with slow file I/Os. The out-of-core algorithms store octants on disks for persistence. However, neither of them was designed to leverage unique characteristics of non-volatile byte-addressable memory (NVBM). In this paper, we propose a novel data structure Distributed Persistent Merged octree (DPM-octree) for both meshing and in-memory storage of persistent octrees using NVBM. It is a multi-version data structure and can recover from failures using its earlier persistent version stored in NVBM. In addition, we design a feature-directed sampling approach to help dynamically transform the DPM-octree layout for reducing NVBM-induced memory write latency. DPM-octree uses parity trees which are created using erasure coding and stored in NVBM to support low-latency in-memory octant recovery after data loss. DPM-octree has been successfully integrated with Gerris software for simulation of fluid dynamics. Finally, our experimental results with real-world scientific workloads show that DPM-octree scales up to 1.1 billion mesh elements with 1000 processors on the Titan supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Data Movement Dominates: Advanced Memory Technology to Address the Real Exascale Power Problem

Energy is the fundamental barrier to Exascale supercomputing and is dominated by the cost of moving data from one point to another, not computation. Similarly, performance is dominated by data movement, not computation. The solution to this problem requires three critical technologies: 3D integration, optical chip-to-chip communication, and a new communication model. The central goal of the Sandia led "Data Movement Dominates" project aimed to develop memory systems and new architectures based on these technologies that have the potential to lower the cost of local memory accesses by orders of magnitude and provide substantially more bandwidth. Only through these transformational advances can future systems reach the goals of Exascale computing with a manageable power budgets. The Sandia led team included co-PIs from Columbia University, Lawrence Berkeley Lab, and the University of Maryland. The Columbia effort of Data Movement Dominates focused on developing a physically accurate simulation environment and experimental verification for optically-connected memory (OCM) systems that can enable continued performance scaling through high-bandwidth capacity, energy-efficient bit-rate transparency, and time-of-flight latency. With OCM, memory device parallelism and total capacity can scale to match future high-performance computing requirements without sacrificing data-movement efficiency. When we consider systems with integrated photonics, links to memory can be seamlessly integrated with the interconnection network-in a sense, memory becomes a primary aspect of the interconnection network. At the core of the Columbia effort, toward expanding our understanding of OCM enabled computing we have created an integrated modeling and simulation environment that uniquely integrates the physical behavior of the optical layer. The PhoenxSim suite of design and software tools developed under this effort has enabled the co-design of and performance evaluation photonics-enabled OCM architectures on Exascale computing systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Working memory, situation models, and synesthesia

Research on language comprehension suggests a strong relationship between working memory span measures and language comprehension. However, there is also evidence that this relationship weakens at higher levels of comprehension, such as the situation model level. The current study explored this relationship by comparing 10 grapheme–color synesthetes who have additional color experiences when they read words that begin with different letters and 48 normal controls on a number of tests of complex working memory capacity and processing at the situation model level. On all tests of working memory capacity, the synesthetes outperformed the controls. Importantly, there was no carryover benefit for the synesthetes for processing at the situation model level. This reinforces the idea that although some aspects of language comprehension are related to working memory span scores, this applies less directly to situation model levels. As a result, this suggests that theories of working memory must take into account this limitation, and the working memory processes that are involved in situation model construction and processing must be derived.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Persistence and memory timescales in root‐zone soil moisture dynamics

Abstract The memory timescale that characterizes root‐zone soil moisture remains the dominant measure in seasonal forecasts of land‐climate interactions. This memory is a quasi‐deterministic timescale associated with the losses (e.g., evapotranspiration) from the soil column and is often interpreted as persistence in soil moisture states. Persistence, however, represents a distribution of time periods where soil moisture resides above or below some prescribed threshold and is therefore inherently probabilistic. Using multiple soil moisture data sets collected at high resolution (subhourly) across different biomes and climates, this paper explores the differences, underlying dynamics, and relative importance of memory and persistence timescales in root‐zone soil moisture. A first‐order Markov process, commonly used to interpret soil moisture fluctuations derived from climate simulations, is also used as a reference model. Persistence durations of soil moisture below the plant water‐stress level (chosen as the threshold), and the temporal spectrum of upcrossings and downcrossings of this threshold, are compared to the memory timescale and spectrum of the full time series, respectively. The results indicate that despite the differences between meteorological drivers, the spectrum of threshold‐crossings is similar across sites, and follows a unique relation with that of the full soil moisture series. The distribution of persistence times exhibits an approximate stretched exponential type and reflects a likelihood of exceeding the memory at all sites. However, the rainfall counterpart of these distributions shows that persistence of dry atmospheric periods is less likely at sites with long soil moisture memory. The cluster exponent, a measure of the density of threshold‐crossings in a time frame, reveals that the clustering tendency in rainfall events (on‐off switches) does not translate directly to clustering in soil moisture. This is particularly the case in climates where rainfall and evapotranspiration are out of phase, resulting in less ordered (more independent) persistence in soil moisture than in rainfall.

Ghannam, Khaled↗
DOE OSTI2015

Origin of “memory glass” effect in pressure-amorphized rare-earth molybdate single crystals

The memory glass effect (MGE) describes the ability of some materials to recover the initial structure and crystallographic orientation after pressure-induced amorphization (PIA). In spite of numerous studies the nature and underlying mechanisms of this phenomenon are still not clear. Here we report investigations of MGE in β′-Eu{sub 2}(MoO{sub 4}){sub 3} single crystal samples subjected to high pressure amorphization. Using the XRD and TEM techniques we carried out detailed analysis of the structural state of high pressure treated single crystal samples as well as structural transformations due to subsequent annealing at atmospheric pressure. The structure of the sample has been found to be complex, mainly amorphous, however, the amorphous medium contains evenly distributed nanosize inclusions of a paracrystalline phase. The inclusions are highly correlated in orientation and act as “memory units” in the MGE. - Graphical abstract: Schematic representation of pressure-induced amorphization and “memory glass” effect in rare-earth molybdate single crystals. The XRD and TEM measurements have revealed the presence of the residual identically oriented paracrystalline nanodomains in the pressure-amorphized state. These domains preserve the information about initial structure and orientation of the sample. They act as memory units and crystalline seeds during transformation of the amorphous phase back to the starting single crystalline one. - Highlights: • Pressure-amorphized Eu{sub 2}(MoO4){sub 3} single crystals were studied ex-situ by XRD and TEM. • Tiny residual crystalline inclusions were found in amorphous matrix of sample. • The inclusions keep in memory the parent crystal structure and orientation. • The inclusions account for “memory glass” effect in rare-earth molibdates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Multiple-stable anisotropic magnetoresistance memory in antiferromagnetic MnTe

Commercial magnetic memories rely on the bistability of ordered spins in ferromagnetic materials. Recently, experimental bistable memories have been realized using fully compensated antiferromagnetic metals. Here we demonstrate a multiple-stable memory device in epitaxial MnTe, an antiferromagnetic counterpart of common II–VI semiconductors. Favourable micromagnetic characteristics of MnTe allow us to demonstrate a smoothly varying zero-field antiferromagnetic anisotropic magnetoresistance (AMR) with a harmonic angular dependence on the writing magnetic field angle, analogous to ferromagnets. The continuously varying AMR provides means for the electrical read-out of multiple-stable antiferromagnetic memory states, which we set by heat-assisted magneto-recording and by changing the writing field direction. The multiple stability in our memory is ascribed to different distributions of domains with the Ne´el vector aligned along one of the three magnetic easy axes. The robustness against strong magnetic field perturbations combined with the multiple stability of the magnetic memory states are unique properties of antiferromagnets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Robust resistive memory devices using solution-processable metal-coordinated azo aromatics

Non-volatile memories will play a decisive role in the next generation of digital technology. Flash memories are currently the key player in the field, yet they fail to meet the commercial demands of scalability and endurance. Resistive memory devices, and in particular memories based on low-cost, solution-processable and chemically tunable organic materials, are promising alternatives explored by the industry. However, to date, they have been lacking the performance and mechanistic understanding required for commercial translation. Here we report a resistive memory device based on a spin-coated active layer of a transition-metal complex, which shows high reproducibility (~350 devices), fast switching (≤30 ns), excellent endurance (~10 12 cycles), stability (>10 6 s) and scalability (down to ~60 nm 2 ). In situ Raman and ultraviolet–visible spectroscopy alongside spectroelectrochemistry and quantum chemical calculations demonstrate that the redox state of the ligands determines the switching states of the device whereas the counterions control the hysteresis. This insight may accelerate the technological deployment of organic resistive memories.

Goswami, Sreetosh↗
DOE OSTI2019

Dynamical nonlinear memory capacitance in biomimetic membranes

Two-terminal memory elements, or memelements, capable of co-locating signal processing and memory via history-dependent reconfigurability at the nanoscale are vital for next-generation computing materials striving to match the brain’s efficiency and flexible cognitive capabilities. While memory resistors, or memristors, have been widely reported, other types of memelements remain underexplored or undiscovered. Here we report the first example of a volatile, voltage-controlled memcapacitor in which capacitive memory arises from reversible and hysteretic geometrical changes in a lipid bilayer that mimics the composition and structure of biomembranes. We demonstrate that the nonlinear dynamics and memory are governed by two implicitly-coupled, voltage-dependent state variables—membrane radius and thickness. Further, our system is capable of tuneable signal processing and learning via synapse-like, short-term capacitive plasticity. These findings will accelerate the development of low-energy, biomolecular neuromorphic memelements, which, in turn, could also serve as models to study capacitive memory and signal processing in neuronal membranes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Embedding security into ferroelectric FET array via in situ memory operation

Non-volatile memories (NVMs) have the potential to reshape next-generation memory systems because of their promising properties of near-zero leakage power consumption, high density and non-volatility. However, NVMs also face critical security threats that exploit the non-volatile property. Compared to volatile memory, the capability of retaining data even after power down makes NVM more vulnerable. Existing solutions to address the security issues of NVMs are mainly based on Advanced Encryption Standard (AES), which incurs significant performance and power overhead. In this paper, we propose a lightweight memory encryption/decryption scheme by exploiting in-situ memory operations with negligible overhead. To validate the feasibility of the encryption/decryption scheme, device-level and array-level experiments are performed using ferroelectric field effect transistor (FeFET) as an example NVM without loss of generality. Besides, a comprehensive evaluation is performed on a 128 × 128 FeFET AND-type memory array in terms of area, latency, power and throughput. Compared with the AES-based scheme, our scheme shows ~22.6×/~14.1× increase in encryption/decryption throughput with negligible power penalty. Furthermore, we evaluate the performance of our scheme over the AES-based scheme when deploying different neural network workloads. Our scheme yields significant latency reduction by 90% on average for encryption and decryption processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

A scalable superconducting nanowire memory array with row–column addressing

Scalable superconducting memory is required for the development of low-energy superconducting computers and fault-tolerant quantum computers. Conventional superconducting logic-based memory cells possess a large footprint that limits scaling; nanowire-based superconducting memory cells, although more compact, have high error rates, which hinders integration into large arrays. Here we report a 4 × 4 superconducting nanowire memory array that is designed for scalable row–column operations and has a functional density of 2.6 Mbit cm −2 . Each memory cell is based on a nanowire loop consisting of two temperature-dependent superconducting switches and a variable kinetic inductor. The arrays operate at 1.3 K, where we implement and characterize multiflux quanta state storage and destructive read-out. By optimizing the write- and read-pulse sequences, we minimize bit errors and maximize operating margins. We achieve a minimum bit error rate of 10 −5 . Here, we also use circuit-level simulations to understand the memory cell’s dynamics, performance limits and stability under varying pulse amplitudes.

Electrical and electronic engineering↗
DOE OSTI2016

Dopaminergic inputs in the dentate gyrus direct the choice of memory encoding

Rewarding experiences are often well remembered, and such memory formation is known to be dependent on dopamine modulation of the neural substrates engaged in learning and memory; however, it is unknown how and where in the brain dopamine signals bias episodic memory toward preceding rather than subsequent events. Here we found that photostimulation of channelrhodopsin-2–expressing dopaminergic fibers in the dentate gyrus induced a long-term depression of cortical inputs, diminished theta oscillations, and impaired subsequent contextual learning. Computational modeling based on this dopamine modulation indicated an asymmetric association of events occurring before and after reward in memory tasks. In subsequent behavioral experiments, preexposure to a natural reward suppressed hippocampus-dependent memory formation, with an effective time window consistent with the duration of dopamine-induced changes of dentate activity. Altogether, our results suggest a mechanism by which dopamine enables the hippocampus to encode memory with reduced interference from subsequent experience.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

GRAVITATIONAL MEMORY IN BINARY BLACK HOLE MERGERS

In addition to the dominant oscillatory gravitational wave signals produced during binary inspirals, a non-oscillatory component arises from the nonlinear 'memory' effect, sourced by the emitted gravitational radiation. The memory grows significantly during the late-inspiral and merger, modifying the signal by an almost step-function profile, and making it difficult to model by approximate methods. We use numerical evolutions of binary black holes (BHs) to evaluate the nonlinear memory during late-inspiral, merger, and ringdown. We identify two main components of the signal: the monotonically growing portion corresponding to the memory, and an oscillatory part which sets in roughly at the time of merger and is due to the BH ringdown. Counterintuitively, the ringdown is most prominent for models with the lowest total spin. Thus, the case of maximally spinning BHs anti-aligned to the orbital angular momentum exhibits the highest signal-to-noise ratio (S/N) for interferometric detectors. The largest memory offset, however, occurs for highly spinning BHs, with an estimated value of h {sup tot}{sub 20} {approx_equal} 0.24 in the maximally spinning case. These results are central to determining the detectability of nonlinear memory through pulsar timing array measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Differential Age Effects of Transcranial Direct Current Stimulation on Associative Memory

Abstract Objectives Older adults experience associative memory deficits relative to younger adults (Old & Naveh-Benjamin, 2008). The aim of this study was to test the effect of transcranial direct current stimulation (tDCS) on face-name associative memory in older and younger adults. Method Experimenters applied active (1.5 mA) or sham (0.1 mA) stimulation with the anode placed over the left dorsolateral prefrontal cortex (dlPFC) during a face-name encoding task, and measured both cued recall and recognition performance. Participants completed memory tests immediately after stimulation and after a 24-h delay to examine both immediate and delayed stimulation effects on memory. Results Results showed improved face-name associative memory performance for both recall and recognition measures, but only for younger adults, whereas there was no difference between active and sham stimulation for older adults. For younger adults, stimulation-induced memory improvements persisted after a 24-h delay, suggesting delayed effects of tDCS after a consolidation period. Discussion Although effective in younger adults, these results suggest that older adults may be resistant to this intervention, at least under the stimulation parameters used in the current study. This finding is inconsistent with a commonly seen trend, where tDCS effects on cognition are larger in older than younger adults.

Leach, Ryan C.↗
DOE OSTI2026

A Survey on the Expanding Scope and Interdisciplinary Opportunities for Processing-in-Memory Techniques

Processing-in-Memory (PIM) is emerging as a practical path to overcome the limitations of traditional von Neumann architectures. At its core, PIM systems implement computing primitives such as logic operations and multiply-accumulate acceleration through compute-in-memory, near-memory processing, or hybrid designs. The role of memory cells varies widely across technologies, acting as inputs, outputs, or analog accumulators through bit-lines and sense amplifiers. This diversity creates trade-offs in precision, bandwidth, latency, and programmability, making it difficult to build a unified understanding on the progress of the field. In this survey, we organize recent advances of PIM into three areas. First, we discuss the progress on the architectural optimizations of PIM and its integration with both DRAM and emerging non-volatile memories. Second, we examine how PIM is being used to accelerate key computing domains, including generative AI workloads and high-performance kernels, along with new approaches. Third, we highlight the growing adoption of PIM in computational sciences, where it is being applied to solve interdisciplinary problems such as genome analysis, mRNA quantification, mass spectrometry, quantum circuit simulation, wave modeling, and secure computation. Finally, we synthesize the major challenges that continue to slow PIM adoption, including manufacturing constraints, power delivery, thermal reliability, data consistency, runtime and memory-management coordination, and the difficulty of building portable software abstractions without sacrificing commercial viability. This work provides an updated, structured perspective on PIM’s potential across computing and computational sciences and the barriers that must be solved for it to reach its full impact.

Asifuzzaman, Kazi [Oak Ridge National Laboratory (↗
DOE OSTI2017

Processing-in-Memory Enabled Graphics Processors for 3D Rendering

The performance of 3D rendering of Graphics Processing Unit that convents 3D vector stream into 2D frame with 3D image effects significantly impact users’ gaming experience on modern computer systems. Due to the high texture throughput in 3D rendering, main memory bandwidth becomes a critical obstacle for improving the overall rendering performance. 3D stacked memory systems such as Hybrid Memory Cube (HMC) provide opportunities to significantly overcome the memory wall by directly connecting logic controllers to DRAM dies. Based on the observation that texel fetches significantly impact off-chip memory traffic, we propose two architectural designs to enable Processing-In-Memory based GPU for efficient 3D rendering.

Innovative hardware/software co-design↗
DOE OSTI2024

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗