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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Detrimental effect of interfacial Dzyaloshinskii-Moriya interaction on perpendicular spin-transfer-torque magnetic random access memory

Interfacial Dzyaloshinskii-Moriya interaction in ferromagnet/heavy metal bilayers is recently of considerable interest as it offers an efficient control of domain walls and the stabilization of magnetic skyrmions. However, its effect on the performance of perpendicular spin transfer torque memory has not been explored yet. We show based on numerical studies that the interfacial Dzyaloshinskii-Moriya interaction decreases the thermal energy barrier while increases the switching current. As high thermal energy barrier as well as low switching current is required for the commercialization of spin torque memory, our results suggest that the interfacial Dzyaloshinskii-Moriya interaction should be minimized for spin torque memory applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Ultralow bias power all-optical photonic crystal memory realized with systematically tuned L3 nanocavity

An InP photonic crystal nanocavity with an embedded InGaAsP active region is a unique technology that has realized an all-optical memory with a sub-micro-watt operating power and limitless storage time. In this study, we employed an L3 design with systematic multi-hole tuning, which realized a higher loaded Q factor (>40 000) and a lower mode volume (0.9 μm{sup 3}) than a line-defect-based buried-heterostructure nanocavity (16 000 and 2.2 μm{sup 3}). Excluding the active region realized a record loaded Q factor (210 000) in all for InP-based nanocavities. The minimum bias power for bistable memory operation was reduced to 2.3 ± 0.3 nW, which is about 1/10 of the previous record of 30 nW. This work further established the capability of a bistable nanocavity memory for use in future ultralow-power-consumption on-chip integrated photonics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

A silicon-nanowire memory driven by optical gradient force induced bistability

In this paper, a bistable optical-driven silicon-nanowire memory is demonstrated, which employs ring resonator to generate optical gradient force over a doubly clamped silicon-nanowire. Two stable deformation positions of a doubly clamped silicon-nanowire represent two memory states (“0” and “1”) and can be set/reset by modulating the light intensity (<3 mW) based on the optical force induced bistability. The time response of the optical-driven memory is less than 250 ns. It has applications in the fields of all optical communication, quantum computing, and optomechanical circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Study of self-compliance behaviors and internal filament characteristics in intrinsic SiO{sub x}-based resistive switching memory

Self-compliance characteristics and reliability optimization are investigated in intrinsic unipolar silicon oxide (SiO{sub x})-based resistive switching (RS) memory using TiW/SiO{sub x}/TiW device structures. The program window (difference between SET voltage and RESET voltage) is dependent on external series resistance, demonstrating that the SET process is due to a voltage-triggered mechanism. The program window has been optimized for program/erase disturbance immunity and reliability for circuit-level applications. The SET and RESET transitions have also been characterized using a dynamic conductivity method, which distinguishes the self-compliance behavior due to an internal series resistance effect (filament) in SiO{sub x}-based RS memory. By using a conceptual “filament/resistive gap (GAP)” model of the conductive filament and a proton exchange model with appropriate assumptions, the internal filament resistance and GAP resistance can be estimated for high- and low-resistance states (HRS and LRS), and are found to be independent of external series resistance. Our experimental results not only provide insights into potential reliability issues but also help to clarify the switching mechanisms and device operating characteristics of SiO{sub x}-based RS memory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Operation of a quantum dot in the finite-state machine mode: Single-electron dynamic memory

A single electron dynamic memory is designed based on the non-equilibrium dynamics of charge states in electrostatically defined metallic quantum dots. Using the orthodox theory for computing the transfer rates and a master equation, we model the dynamical response of devices consisting of a charge sensor coupled to either a single and or a double quantum dot subjected to a pulsed gate voltage. We show that transition rates between charge states in metallic quantum dots are characterized by an asymmetry that can be controlled by the gate voltage. This effect is more pronounced when the switching between charge states corresponds to a Markovian process involving electron transport through a chain of several quantum dots. By simulating the dynamics of electron transport we demonstrate that the quantum box operates as a finite-state machine that can be addressed by choosing suitable shapes and switching rates of the gate pulses. We further show that writing times in the ns range and retention memory times six orders of magnitude longer, in the ms range, can be achieved on the double quantum dot system using experimentally feasible parameters, thereby demonstrating that the device can operate as a dynamic single electron memory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

On the memory kernel and the reduced system propagator

We relate the memory kernel in the Nakajima–Zwanzig–Mori time-convolution approach to the reduced system propagator which is often used to obtain the kernel in the Tokuyama–Mori time-convolutionless approach. The connection provides a robust and simple formalism to compute the memory kernel for a generalized system-bath model circumventing the need to compute high order system-bath observables, thus streamlining the use of numerically exact solvers for calculating the memory kernel. We illustrate this for a model system with electron-electron and electron-phonon couplings, driven away from equilibrium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Analog architectures for neural network acceleration based on non-volatile memory

Analog hardware accelerators, which perform computation within a dense memory array, have the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. Exploiting the intrinsic computational advantages of memory arrays, however, has proven to be challenging principally due to the overhead imposed by the peripheral circuitry and due to the non-ideal properties of memory devices that play the role of the synapse. We review the existing implementations of these accelerators for deep supervised learning, organizing our discussion around the different levels of the accelerator design hierarchy, with an emphasis on circuits and architecture. We explore and consolidate the various approaches that have been proposed to address the critical challenges faced by analog accelerators, for both neural network inference and training, and highlight the key design trade-offs underlying these techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Memory-based parameterization with differentiable solver: Application to Lorenz ’96

Physical parameterizations (or closures) are used as representations of unresolved subgrid processes within weather and global climate models or coarse-scale turbulent models, whose resolutions are too coarse to resolve small-scale processes. These parameterizations are typically grounded on physically based, yet empirical, representations of the underlying small-scale processes. Machine learning-based parameterizations have recently been proposed as an alternative solution and have shown great promise to reduce uncertainties associated with the parameterization of small-scale processes. Yet, those approaches still show some important mismatches that are often attributed to the stochasticity of the considered process. This stochasticity can be due to coarse temporal resolution, unresolved variables, or simply to the inherent chaotic nature of the process. To address these issues, we propose a new type of parameterization (closure), which is built using memory-based neural networks, to account for the non-instantaneous response of the closure and to enhance its stability and prediction accuracy. We apply the proposed memory-based parameterization, with differentiable solver, to the Lorenz ’96 model in the presence of a coarse temporal resolution and show its capacity to predict skillful forecasts over a long time horizon of the resolved variables compared to instantaneous parameterizations. This approach paves the way for the use of memory-based parameterizations for closure problems.

Mathematics↗
DOE OSTI2023

Distributed memory, GPU accelerated Fock construction for hybrid, Gaussian basis density functional theory

With the growing reliance of modern supercomputers on accelerator-based architecture such a graphics processing units (GPUs), the development and optimization of electronic structure methods to exploit these massively parallel resources has become a recent priority. While significant strides have been made in the development GPU accelerated, distributed memory algorithms for many modern electronic structure methods, the primary focus of GPU development for Gaussian basis atomic orbital methods has been for shared memory systems with only a handful of examples pursing massive parallelism. Here in this work, we present a set of distributed memory algorithms for the evaluation of the Coulomb and exact exchange matrices for hybrid Kohn–Sham DFT with Gaussian basis sets via direct density-fitted (DF-J-Engine) and seminumerical (sn-K) methods, respectively. The absolute performance and strong scalability of the developed methods are demonstrated on systems ranging from a few hundred to over one thousand atoms using up to 128 NVIDIA A100 GPUs on the Perlmutter supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Evaluation of the capacity of communication lines with nonlinear finite memory

We propose a method for calculating the capacity of a finite-memory channel up to the square of the nonlinear memory parameter. A comparison with a regular Gaussian model is performed, in which the Kerr nonlinearity is considered as an additional Gaussian noise. The estimate by the regular Gaussian model is shown to yield a greater capacity as compared with the estimate obtained by the nonlinear memory model. The optimal signal powers, providing the maximum mutual information, are found to be equal. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Quantum memory node based on a semiconductor double quantum dot in a laser-controlled optical resonator

The concept of a quantum node consisting of a memory qubit and a frequency convertor is proposed and analysed. The memory qubit is presented by a semiconductor four-level double quantum dot (DQD) placed in an optical microresonator (MR). The DQD contains an electron in the quantised part of the conduction band and the MR can be populated by a certain number of photons. The DQD and MR states are controlled be applying the laser and electrostatic fields. The difference between the telecommunication frequency of the photon (transport qubit) supplied to the system through a waveguide and the frequency of the electronic transition in the DQD is compensated for using an auxiliary element, i.e. a frequency convertor based on a single quantum dot (QD). This design allows the electron – photon state of the hybrid system to be controlled by an appropriate variation of the field parameters and the switching between resonance and nonresonance DQD and MR interaction regimes. As an example, a GaAs DQD placed in a microdisk MR is studied. A numerical technique for modelling an optical spectrum of a microdisk MR with an additional layer (AL) deposited on its surface is developed. Using this technique, the effect of the AL on the MR eigenmode properties is investigated and the possibility of tuning its frequency to the QD electronic transition frequency by depositing an AL on the disk surface is demonstrated. (quantum memory)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Microwave quantum memory on a controlled frequency comb

We consider a protocol of broadband quantum memory on a controlled frequency comb of spectral lines of ring microwave resonators connected to a common strip waveguide. A prototype of this memory is manufactured, on which the room-temperature preservation of microwave pulses with an efficiency of about 3 % is demonstrated. The experimentally obtained efficiency can be enhanced to a level above 90 % using modern microwave technologies and conducting experiments at helium temperatures, which opens a new way for the development of integrated microwave quantum memory. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Tunable shear thickening, aging, and rejuvenation in suspensions of shape-memory-endowed liquid crystalline particles

The morphological features of particles, notably shape anisotropy, critically influence the rheological properties of dense suspensions, spanning both natural and engineered systems. This work explores the potential of using shape memory particles to dynamically regulate suspension fluid flow through controllable shape transformations. First, we synthesize shape-memory particles with programmable anisotropy from liquid crystal elastomers, such that the stiffness and shapes of the particles can be tuned by manipulating temperature. Our findings reveal that suspensions from such particles exhibit significant tunability in shear thickening behavior, transitioning from discontinuous shear thickening to a Newtonian-like response within a narrow temperature range of 60 ° C. This capability to modulate rheological responses in situ presents an approach for addressing processing challenges in many applications where control over flow behavior is paramount. Furthermore, we also show that suspensions composed of these anisotropic particles can undergo physical aging, and evolve into a glassy state. This state can be escaped upon activation of the shape memory effect. This reversibility underscores the potential for using such materials to engineer systems that can enter or come out of kinetic arrest by leveraging internal mechanical responses to external stimuli. The insights gained here not only broaden our understanding of the interplay between particle geometry and suspension dynamics but also pave the way for leveraging ensembles of stimuli-responsive objects to precisely control collective behaviors in many-body systems.

Science & Technology - Other Topics↗
DOE OSTI2015

NANOGrav constraints on gravitational wave bursts with memory

Among efforts to detect gravitational radiation, pulsar timing arrays are uniquely poised to detect “memory” signatures, permanent perturbations in spacetime from highly energetic astrophysical events such as mergers of supermassive black hole binaries. The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) observes dozens of the most stable millisecond pulsars using the Arecibo and Green Bank radio telescopes in an effort to study, among other things, gravitational wave memory. We herein present the results of a search for gravitational wave bursts with memory (BWMs) using the first five years of NANOGrav observations. We develop original methods for dramatically speeding up searches for BWM signals. In the directions of the sky where our sensitivity to BWMs is best, we would detect mergers of binaries with reduced masses of 10{sup 9} M{sub ⊙} out to distances of 30 Mpc; such massive mergers in the Virgo cluster would be marginally detectable. We find no evidence for BWMs. However, with our non-detection, we set upper limits on the rate at which BWMs of various amplitudes could have occurred during the time spanned by our data—e.g., BWMs with amplitudes greater than 10{sup −13} must encounter the Earth at a rate less than 1.5 yr{sup −1}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Long-time memory effects in a localizable central spin problem

We study the properties of the Nakajima–Zwanzig memory kernel for a qubit immersed in a many-body localized (i.e. disordered and interacting) bath. We argue that the memory kernel decays as a power law in both the localized and ergodic regimes, and show how this can be leveraged to extract t → ∞ populations for the qubit from finite time (Jt ≤ 10 2 ) data in the thermalizing phase. This allows us to quantify how the long-time values of the populations approach the expected thermalized state as the bath approaches the thermodynamic limit. This approach should provide a good complement to state-of-the-art numerical methods, for which the long-time dynamics with large baths are impossible to simulate in this phase. Additionally, our numerics on finite baths reveal the possibility for unbounded exponential growth in the memory kernel, a phenomenon rooted in the appearance of exceptional points in the projected Liouvillian governing the reduced dynamics. In small systems amenable to exact numerics, we find that these pathologies may have some correlation with delocalization.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Optimal control of large quantum systems: assessing memory and runtime performance of GRAPE

Abstract Gradient Ascent Pulse Engineering (GRAPE) is a popular technique in quantum optimal control, and can be combined with automatic differentiation (AD) to facilitate on-the-fly evaluation of cost-function gradients. We illustrate that the convenience of AD comes at a significant memory cost due to the cumulative storage of a large number of states and propagators. For quantum systems of increasing Hilbert space size, this imposes a significant bottleneck. We revisit the strategy of hard-coding gradients in a scheme that fully avoids propagator storage and significantly reduces memory requirements. Separately, we present improvements to numerical state propagation to enhance runtime performance. We benchmark runtime and memory usage and compare this approach to AD-based implementations, with a focus on pushing towards larger Hilbert space sizes. The results confirm that the AD-free approach facilitates the application of optimal control for large quantum systems which would otherwise be difficult to tackle.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Shape-memory transformations of NiTi: Minimum-energy pathways between austenite, martensites, and kinetically limited intermediate states

NiTi is the most used shape-memory alloy, nonetheless, a lack of understanding remains regarding the associated structures and transitions, including their barriers. Using a generalized solid-state nudge elastic band (GSSNEB) method implemented via density-functional theory, we detail the structural transformations in NiTi relevant to shape memory: those between body-centered orthorhombic (BCO) groundstate and a newly identified stable austenite (“glassy” B2-like) structure, including energy barriers (hysteresis) and intermediate structures (observed as a kinetically limited R-phase), and between martensite variants (BCO orientations). All results are in good agreement with available experiment. We contrast the austenite results to those from the often-assumed, but unstable B2. Furthermore, these high- and low-temperature structures and structural transformations provide much needed atomic-scale detail for transitions responsible for NiTi shape-memory effects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Diagnostics of Mixed-State Topological Order and Breakdown of Quantum Memory

Topological quantum memory can protect information against local errors up to finite error thresholds. Such thresholds are usually determined based on the success of decoding algorithms rather than the intrinsic properties of the mixed states describing corrupted memories. Here we provide an intrinsic characterization of the breakdown of topological quantum memory, which both gives a bound on the performance of decoding algorithms and provides examples of topologically distinct mixed states. We employ three information-theoretical quantities that can be regarded as generalizations of the diagnostics of ground-state topological order, and serve as a definition for topological order in error-corrupted mixed states. We consider the topological contribution to entanglement negativity and two other metrics based on quantum relative entropy and coherent information. In the concrete example of the two-dimensional (2D) Toric code with local bit-flip and phase errors, we map three quantities to observables in 2D classical spin models and analytically show they all undergo a transition at the same error threshold. This threshold is an upper bound on that achieved in any decoding algorithm and is indeed saturated by that in the optimal decoding algorithm for the Toric code. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗