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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Dynamics of the stochastic Lorenz chaotic system with long memory effects

Little seems to be known about the ergodic dynamics of stochastic systems with fractional noise. This paper is devoted to discern such long time dynamics through the stochastic Lorenz chaotic system (SLCS) with long memory effects. By a truncation technique, the SLCS is proved to generate a continuous stochastic dynamical system Λ. Based on the Krylov-Bogoliubov criterion, the required Lyapunov function is further established to ensure the existence of the invariant measure of Λ. Meanwhile, the uniqueness of the invariant measure of Λ is proved by examining the strong Feller property, together with an irreducibility argument. Therefore, the SLCS has exactly one adapted stationary solution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

A stochastic simulation method for the assessment of resistive random access memory retention reliability

This study presents an evaluation method for resistive random access memory retention reliability based on the Metropolis Monte Carlo algorithm and Gibbs free energy. The method, which does not rely on a time evolution, provides an extremely efficient way to compare the relative retention properties of metal-insulator-metal structures. It requires a small number of iterations and may be used for statistical analysis. The presented approach is used to compare the relative robustness of a single layer ZrO{sub 2} device with a double layer ZnO/ZrO{sub 2} one, and obtain results which are in good agreement with experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Evidence for thermally assisted threshold switching behavior in nanoscale phase-change memory cells

In spite of decades of research, the details of electrical transport in phase-change materials are still debated. In particular, the so-called threshold switching phenomenon that allows the current density to increase steeply when a sufficiently high voltage is applied is still not well understood, even though there is wide consensus that threshold switching is solely of electronic origin. However, the high thermal efficiency and fast thermal dynamics associated with nanoscale phase-change memory (PCM) devices motivate us to reassess a thermally assisted threshold switching mechanism, at least in these devices. The time/temperature dependence of the threshold switching voltage and current in doped Ge{sub 2}Sb{sub 2}Te{sub 5} nanoscale PCM cells was measured over 6 decades in time at temperatures ranging from 40 °C to 160 °C. We observe a nearly constant threshold switching power across this wide range of operating conditions. We also measured the transient dynamics associated with threshold switching as a function of the applied voltage. By using a field- and temperature-dependent description of the electrical transport combined with a thermal feedback, quantitative agreement with experimental data of the threshold switching dynamics was obtained using realistic physical parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Optically readout write once read many memory with single active organic layer

An optically readable write once read many memory (WORM) in Ag/Poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene] (MEH PPV)/ITO is demonstrated in this work. Utilising characteristics of the organic light emitting diode structure of Ag/MEH PPV/ITO and electrochemical metallization of Ag, a WORM with light emitting capability can be realised. The simple fabrication process and multifunction capability of the device can be useful for future wearable optoelectronics and photomemory applications, where fast and parallel readout can be achieved by photons.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Retention modeling for ultra-thin density of Cu-based conductive bridge random access memory (CBRAM)

We investigate the effect of Cu concentration On-state resistance retention characteristics of W/Cu/Ti/HfO{sub 2}/Pt memory cell. The development of RRAM device for application depends on the understanding of the failure mechanism and the key parameters for device optimization. In this study, we develop analytical expression for cations (Cu{sup +}) diffusion model using Gaussian distribution for detailed analysis of data retention time at high temperature. It is found that the improvement of data retention time depends not only on the conductive filament (CF) size but also on Cu atoms concentration density in the CF. Based on the simulation result, better data retention time is observed for electron wave function associated with Cu{sup +} overlap and an extended state formation. This can be verified by analytical calculation of Cu atom defects inside the filament, based on Cu{sup +} diffusion model. The importance of Cu diffusion for the device reliability and the corresponding local temperature of the filament were analyzed by COMSOL Multiphysics simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Disruptive effect of Dzyaloshinskii-Moriya interaction on the magnetic memory cell performance

In order to increase the thermal stability of a magnetic random access memory cell, materials with high spin-orbit interaction are often introduced in the storage layer. As a side effect, a strong Dzyaloshinskii-Moriya interaction (DMI) may arise in such systems. Here, we investigate the impact of DMI on the magnetic cell performance, using micromagnetic simulations. We find that DMI strongly promotes non-uniform magnetization states and non-uniform switching modes of the magnetic layer. It appears to be detrimental for both the thermal stability of the cell and its switching current, leading to considerable deterioration of the cell performance even for a moderate DMI amplitude.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Switching characteristics for ferroelectric random access memory based on RC model in poly(vinylidene fluoride-trifluoroethylene) ultrathin films

The switching characteristic of the poly(vinylidene fluoride-trifluoroethlene) (P(VDF-TrFE)) films have been studied at different ranges of applied electric field. It is suggest that the increase of the switching speed upon nucleation protocol and the deceleration of switching could be related to the presence of a non-ferroelectric layer. Remarkably, a capacitor and resistor (RC) links model plays significant roles in the polarization switching dynamics of the thin films. For P(VDF-TrFE) ultrathin films with electroactive interlayer, it is found that the switching dynamic characteristics are strongly affected by the contributions of resistor and non-ferroelectric (non-FE) interface factors. A corresponding experiment is designed using poly(3,4-ethylene dioxythiophene):poly(styrene sulfonic) (PEDOT-PSSH) as interlayer with different proton concentrations, and the testing results show that the robust switching is determined by the proton concentration in interlayer and lower leakage current in circuit to reliable applications of such polymer films. These findings provide a new feasible method to enhance the polarization switching for the ferroelectric random access memory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Memory embedded non-intrusive reduced order modeling of non-ergodic flows

Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two, but the novel nonintrusive approach we present in this study is an attempt at taking all three into account effectively compared to their traditional counterparts. Based on dimensionality reduction using proper orthogonal decomposition (POD), we introduce a long short-term memory neural network architecture together with a principal interval decomposition (PID) framework as an enabler to account for localized modal deformation. As an effective partitioning tool for breaking the Kolmogorov barrier, our PID framework, therefore, can be considered a key element in the accurate reduced order modeling of convective flows. Our applications for convection-dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations demonstrate that the proposed approach yields significantly more accurate predictions than the POD-Galerkin method and could be a key enabler toward near real-time predictions of unsteady flows.

42 ENGINEERING↗
DOE OSTI2021

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electrical and structural properties of binary Ga–Sb phase change memory alloys

Material properties of Ga–Sb binary alloy thin films deposited under ultra-high vacuum conditions were studied for analog phase change memory (PCM) applications. Crystallization of this alloy was shown to occur in the temperature range of 180–264 °C, with activation energy >2.5 eV depending on the composition. X-ray diffraction (XRD) studies showed phase separation upon crystallization into two phases, Ga-doped A7 antimony and cubic zinc-blende GaSb. Synchrotron in situ XRD analysis revealed that crystallization into the A7 phase is accompanied by Ga out-diffusion from the grains. X-ray absorption fine structure studies of the local structure of these alloys demonstrated a bond length decrease with a stable coordination number of 4 upon amorphous-to-crystalline phase transformation. Mushroom cell structures built with Ga–Sb alloys on ø110 nm TiN heater show a phase change material resistance switching behavior with resistance ratio >100 under electrical pulse measurements. TEM and Energy Dispersive Spectroscopy (EDS) studies of the Ga–Sb cells after ∼100 switching cycles revealed that partial SET or intermediate resistance states are attained by the variation of the grain size of the material as well as the Ga content in the A7 phase. A mechanism for a reversible composition control is proposed for analog cell performance. These results indicate that Te-free Ga–Sb binary alloys are potential candidates for analog PCM applications.

Ume, Rubab (ORCID:0000000324766900)↗
DOE OSTI2023

Shock-induced transformation of nitinol shape memory alloy: Effect of stress state on transformation

Due to its numerous practical applications and intriguing phase transformation behavior, shape memory alloys (SMAs) have garnered significant research and development interests. In the past, most studies on the mechanical behavior of SMAs have been conducted under uniaxial stress loadings. Limited research on SMAs under shock loading has not provided conclusive results regarding their transformation behavior and transformation stress under such loading. Additionally, there is a lack of comprehensive understanding regarding the effects of different stress states on transformation behavior. The main objectives of this study are to address these issues. To achieve these objectives, a series of shock wave experiments were designed and conducted. Additionally, quasi-static and dynamic uniaxial stress experiments were carried out to establish a baseline for comparison. The results revealed that the transformation stress under dynamic uniaxial strain shock loading was approximately 1.92 GPa in contrast to 0.5 GPa (quasi-static) to 0.8 GPa (dynamic) observed in uniaxial stress loading. The transformation behavior exhibited noticeable rate sensitivity for both types of loading. There appeared to be a critical strain rate above which the austenite phase was driven to a metastable state. This estimated critical axial strain rate along the loading direction was approximately 2 × 10 3 /s–4 × 10 3 /s for uniaxial stress loading and approximately 2 × 10 6 /s for uniaxial strain loading. The apparent high transformation stress for uniaxial strain loading can likely be attributed to a combination of high-pressure confinement and high strain rate. Furthermore, determining their relative contributions remains an open issue.

36 MATERIALS SCIENCE↗
DOE OSTI2024

When in-memory computing meets spiking neural networks—A perspective on device-circuit-system-and-algorithm co-design

This review explores the intersection of bio-plausible artificial intelligence in the form of spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain, highlighting their collective potential for low-power edge computing environments. Through detailed investigation at the device, circuit, and system levels, we highlight the pivotal synergies between SNNs and IMC architectures. Additionally, we emphasize the critical need for comprehensive system-level analyses, considering the inter-dependencies among algorithms, devices, circuit, and system parameters, crucial for optimal performance. An in-depth analysis leads to the identification of key system-level bottlenecks arising from device limitations, which can be addressed using SNN-specific algorithm–hardware co-design techniques. This review underscores the imperative for holistic device to system design-space co-exploration, highlighting the critical aspects of hardware and algorithm research endeavors for low-power neuromorphic solutions.

Physics↗
DOE OSTI2017

Deep melting reveals liquid structural memory and anomalous ferromagnetism in bismuth

As an archetypal semimetal with complex and anisotropic Fermi surface and unusual electric properties (e.g., high electrical resistance, large magnetoresistance, and giant Hall effect), bismuth (Bi) has played a critical role in metal physics. In general, Bi displays diamagnetism with a high volumetric susceptibility (~10 -4 ). Here in this paper, we report unusual ferromagnetism in bulk Bi samples recovered from a molten state at pressures of 1.4–2.5 GPa and temperatures above ~1,250 K. The ferromagnetism is associated with a surprising structural memory effect in the molten state. On heating, low-temperature Bi liquid (L) transforms to a more randomly disordered high-temperature liquid (L') around 1,250 K. By cooling from above 1,250 K, certain structural characteristics of liquid L' are preserved in L. Bi clusters with characteristics of the liquid L' motifs are further preserved through solidification into the Bi-II phase across the pressure-independent melting curve, which may be responsible for the observed ferromagnetism.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model

Here, the urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Field-induced magnetic phase transitions and memory effect in bilayer ruthenate Ca 3 Ru 2 O 7 with Fe substitution

Bilayer ruthenate Ca 3 (Ru 1-x Fe x ) 2 O 7 (x = 0.05) exhibits an incommensurate magnetic soliton lattice driven by the Dzyaloshinskii–Moriya interaction. Here, in this work, we report complex field-induced magnetic phase transitions and memory effect in this system via single-crystal neutron diffraction and magnetotransport measurements. We observe first-order incommensurate-to-commensurate magnetic transitions upon applying the magnetic field both along and perpendicular to the propagation axis of the incommensurate spin structure. Furthermore, we find that the metastable states formed upon decreasing the magnetic field depend on the temperature and the applied field orientation. Lastly, we suggest that the observed field-induced metastability may be ascribable to the quenched kinetics at low temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Thermodynamics of multicaloric effects in multiferroic materials: application to metamagnetic shape-memory alloys and ferrotoroidics

In this paper, we develop a general thermodynamic framework to investigate multicaloric effects in multiferroic materials. This is applied to the study of both magnetostructural and magnetoelectric multiferroics. Landau models with appropriate interplay between the corresponding ferroic properties (order parameters) are proposed for metamagnetic shape-memory and ferrotoroidic materials, which, respectively, belong to the two classes of multiferroics. For each ferroic property, caloric effects are quantified by the isothermal entropy change induced by the application of the corresponding thermodynamically conjugated field. The multicaloric effect is obtained as a function of the two relevant applied fields in each class of multiferroics. It is further shown that multicaloric effects comprise the corresponding contributions from caloric effects associated with each ferroic property and the cross-contribution arising from the interplay between these ferroic properties. Finally, this article is part of the themed issue ‘Taking the temperature of phase transitions in cool materials’.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Optical control of topological memory based on orbital magnetization

Under suitable conditions, some twisted graphene multilayers and transition-metal dichalcogenides become Chern insulators, exhibiting the anomalous quantum Hall effect and orbital magnetization due to spontaneous valley polarization. We study the interaction of a Chern insulator with circularly polarized light. Here, the interaction energy contains an antisymmetric term that couples to the helicity of incident light. For a two-band Chern insulator, this term is expressed as an integral involving the Berry curvature of the system. Taking advantage of this interaction, we propose an experimental protocol for switching topological memory based on orbital magnetization by circularly polarized light. Moreover, two laser beams of opposite circular polarization can nucleate domains of opposite magnetization and thus produce an optically configurable domain wall carrying topologically protected chiral edge modes.

36 MATERIALS SCIENCE↗