Search NASA⌕ Search

SEARCH · Search NASA

Results for “low memory”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Low-power resistive random access memory by confining the formation of conducting filaments

Owing to their small physical size and low power consumption, resistive random access memory (RRAM) devices are potential for future memory and logic applications in microelectronics. In this study, a new resistive switching material structure, TiO{sub x}/silver nanoparticles/TiO{sub x}/AlTiO{sub x}, fabricated between the fluorine-doped tin oxide bottom electrode and the indium tin oxide top electrode is demonstrated. The device exhibits excellent memory performances, such as low operation voltage (<±1 V), low operation power, small variation in resistance, reliable data retention, and a large memory window. The current-voltage measurement shows that the conducting mechanism in the device at the high resistance state is via electron hopping between oxygen vacancies in the resistive switching material. When the device is switched to the low resistance state, conducting filaments are formed in the resistive switching material as a result of accumulation of oxygen vacancies. The bottom AlTiO{sub x} layer in the device structure limits the formation of conducting filaments; therefore, the current and power consumption of device operation are significantly reduced.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2015

Apparatus and Method for Low-Temperature Training of Shape Memory Alloys

An apparatus and method for the low-temperature thermo-mechanical training of shape memory alloys (SMA) has been developed. The experimental SMA materials are being evaluated as prototypes for applicability in novel thermal management systems for future cryogenic applications. Alloys providing two-way actuation at cryogenic temperatures are the chief target. The mechanical training regimen was focused on the controlled movement of rectangular strips, with S-bend configurations, at temperatures as low as 30 K. The custom holding fixture included temperature sensors and a low heat-leak linear actuator with a magnetic coupling. The fixture was mounted to a Gifford-McMahon cryocooler providing up to 25 W of cooling power at 20 K and housed within a custom vacuum chamber. Operations included both training cycles and verification of shape memory movement. The system design and operation are discussed. Results of the training for select prototype alloys are presented.

Training↗
DOE OSTI2014

Spin-transfer torque magnetoresistive random-access memory technologies for normally off computing (invited)

Most parts of present computer systems are made of volatile devices, and the power to supply them to avoid information loss causes huge energy losses. We can eliminate this meaningless energy loss by utilizing the non-volatile function of advanced spin-transfer torque magnetoresistive random-access memory (STT-MRAM) technology and create a new type of computer, i.e., normally off computers. Critical tasks to achieve normally off computers are implementations of STT-MRAM technologies in the main memory and low-level cache memories. STT-MRAM technology for applications to the main memory has been successfully developed by using perpendicular STT-MRAMs, and faster STT-MRAM technologies for applications to the cache memory are now being developed. The present status of STT-MRAMs and challenges that remain for normally off computers are discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2022

Non-Volatile, Low Power, and High Density SiC Memory For Future Venus Missions

Long lived memory would transform Venus landers & missions. Venus durable memory demonstrated in earlier HOTTech with impractically high power and low bit capacity. This project seeks to develop and demonstrate the world’s first Venus-durable low power memory device (Non-Volatile Random Access Memory (NVRAM) to enhance extended Venus surface missions.

silicon carbide↗
DOE OSTI2020

Resistance drift in Ge 2 Sb 2 Te 5 phase change memory line cells at low temperatures and its response to photoexcitation

Resistance drift in amorphous Ge 2 Sb 2 Te 5 is experimentally characterized in melt-quenched line cells in the range of 300 K to 125 K and is observed to follow the previously reported power-law behavior with drift coefficients in the range of 0.07 to 0.11 in the dark, linearly decreasing with 1/kT. While these drift coefficients measured in the dark are similar to commonly observed drift coefficients (~0.1) at and above room temperature, measurements under light show a significantly lower drift coefficient (0.05 under illumination vs 0.09 in the dark at 150 K). Periodic on/off switching of light shows a sudden decrease/increase in resistance, attributed to photo-excited carriers, followed by a very slow response (~30 min at 150 K) attributed to contribution of electron traps and slow trap-to-trap charge exchanges. A device-level electronic model is used to relate these experimental findings to gradual charging of electron traps in amorphous Ge 2 Sb 2 Te 5 , which gives rise to growth of a potential barrier for holes in time and, hence, resistance drift.

42 ENGINEERING↗
DOE OSTI2018

Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices

Resource constrained devices are the building blocks of the internet of things (IoT) era. Since the idea behind IoT is to develop an interconnected environment where the devices are tiny enough to operate with limited resources, several control systems have been built to maintain low energy and area consumption while operating as IoT edge devices. Several researchers have begun work on implementing control systems built from resource constrained devices using machine learning. However, there are many ways such devices can achieve lower power consumption and area utilization while maximizing application efficiency. Spiky neuromorphic computing (SNC) is an emerging paradigm that can be leveraged in resource constrained devices for several emerging applications. While delivering the benefits of machine learning, SNC also helps minimize power consumption. For example, low energy memory devices (memristors) are often used to achieve low power operation and also help in reducing system area. In total, we anticipate SNC will provide computational efficiency approaching that of deep learning while using low power, resource constrained devices.

Chakma, Gangotree↗
NASA NTRS2001

A radiation - tolerant, low - power non - volatile memory based on silicon nanocrystal quantum dots.

Nanocrystal nonvolatile floating-gate memories are a good candidate for space applications - initial results suggest they are fast, more reliable and consume less power than conventional floating gate memories. In the nanocrystal based NVM device, charge is not stored on a continuous polysilicon layer (so-called floating gate), but instead on a layer of discrete nanocrystals. Charge injection and storage in dense arrays of silicon nanocrystals in Si02 is a critical aspect of the performance of potential nanocrystal flash memory structures. The ultimate goal for this class of devices is few- or single-electron storage in a small number of nanocrystal elements. In addition, the nanocrystal layer fabrication technique should be simple, 8-inch wafer compatible and well controlled.

Green, M. L.↗
DOE OSTI2025

FLAMES─Fast, Low-Storage, Accurate, and Memory-Efficient Adaptive Sampling─Approach to Resolve Spatially Dependent Dynamics of Molecular Liquids

Many critical phenomena in soft matter occur at large length scales, necessitating the resolution of their structure and dynamics at low wavenumbers. However, resolving wavenumber-dependent dynamics computationally via molecular dynamics simulations presents significant challenges, as these phenomena span several orders of magnitude in both time and length scales, resulting in high computational costs and memory demands. Here, this work highlights the computational and memory challenges associated with analyzing molecular trajectories in reciprocal space and demonstrates a method to address them. We introduce FLAMESFast, Low-storage, Accurate, and Memory-Efficient adaptive Sampling, which is a direct method for calculation of structure factors, allowing us to select only the required number of wavevectors for binning. We also use wavenumber-dependent time steps to extract dynamics. Our FLAMES approach effectively mitigates computational and memory/storage bottlenecks. We demonstrate the method using simulations of a model system, liquid octane, at various temperatures. Comparisons with experimental data and real space computation show that the FLAMES technique achieves high accuracy in resolving temperature- and spatially dependent dynamics while being significantly more computationally efficient and requiring less memory and storage than methods based on a uniform wavevector grid and fixed temporal spacing.

Chen, Guang [Argonne National Laboratory (ANL), Ar↗
NASA NTRS2000

Implementation of Ferroelectric Memories for Space Applications

Ferroelectric random access semiconductor memories (FeRAMs) are an ideal nonvolatile solution for space applications. These memories have low power performance, high endurance and fast write times. By combining commercial ferroelectric memory technology with radiation hardened CMOS technology, nonvolatile semiconductor memories for space applications can be attained. Of the few radiation hardened semiconductor manufacturers, none have embraced the development of radiation hardened FeRAMs, due a limited commercial space market and funding limitations. Government funding may be necessary to assure the development of radiation hardened ferroelectric memories for space applications.

Philpy, Stephen C.↗
DOE OSTI2023

Runtime extension for neural network training with heterogeneous memory

Systems, apparatuses, and methods for managing buffers in a neural network implementation with heterogeneous memory are disclosed. A system includes a neural network coupled to a first memory and a second memory. The first memory is a relatively low-capacity, high-bandwidth memory while the second memory is a relatively high-capacity, low-bandwidth memory. During a forward propagation pass of the neural network, a run-time manager monitors the usage of the buffers for the various layers of the neural network. During a backward propagation pass of the neural network, the run-time manager determines how to move the buffers between the first and second memories based on the monitored buffer usage during the forward propagation pass. As a result, the run-time manager is able to reduce memory access latency for the layers of the neural network during the backward propagation pass.

Mappouras, Georgios↗
DOE OSTI2015

Electrical control of Co/Ni magnetism adjacent to gate oxides with low oxygen ion mobility

We investigate the electrical manipulation of Co/Ni magnetization through a combination of ionic liquid and oxide gating, where HfO{sub 2} with a low O{sup 2−} ion mobility is employed. A limited oxidation-reduction process at the metal/HfO{sub 2} interface can be induced by large electric field, which can greatly affect the saturated magnetization and Curie temperature of Co/Ni bilayer. Besides the oxidation/reduction process, first-principles calculations show that the variation of d electrons is also responsible for the magnetization variation. Our work discloses the role of gate oxides with a relatively low O{sup 2−} ion mobility in electrical control of magnetism, and might pave the way for the magneto-ionic memory with low power consumption and high endurance performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Integration of lead-free ferroelectric on HfO2/Si (100) for high performance non-volatile memory applications

We introduce a novel lead-free ferroelectric thin film (1-x)BaTiO3-xBa(Cu1/3Nb2/3)O3 (x 5 0.025) (BT-BCN) integrated on to HfO2 buffered Si for non-volatile memory (NVM) applications. Piezoelectric force microscopy (PFM), x-ray diffraction, and high resolution transmission electron microscopy were employed to establish the ferroelectricity in BT-BCN thin films. PFM study reveals that the domains reversal occurs with 1806 phase change by applying external voltage, demonstrating its effectiveness for NVM device applications. X-ray photoelectron microscopy was used to investigate the band alignments between atomic layer deposited HfO2 and pulsed laser deposited BT-BCN films. Programming and erasing operations were explained on the basis of band-alignments. The structure offers large memory window, low leakage current, and high and low capacitance values that were easily distinguishable even after ,106 s, indicating strong charge storage potential. This study explains a new approach towards the realization of ferroelectric based memory devices integrated on Si platform and also opens up a new possibility to embed the system within current complementary metal-oxide-semiconductor processing technology.

42 ENGINEERING↗
DOE OSTI2019

Shape memory alloy engine for high efficiency low-temperature gradient thermal to electrical conversion

More than half of the energy generated worldwide is lost as unused thermal energy because of the lack of efficient methodology for harnessing the low-grade heat. In this paper, we demonstrate that shape-memory alloy can be an effective mechanism for recovering low-grade heat. Shape memory alloys exhibit thermally induced martensite to austenite phase transformation and super-elasticity (stress-induced martensitic transformation). Employing these two characteristics, we demonstrate a thermal engine for harnessing waste energy through all modes of heat transfer: convection, conduction, and radiation. In this work, we performed material and heat transfer analysis for achieving high frequency, sustainable and efficient operation of our engine. An optimized shape memory alloy engine generated 36 W per kilogram or 234 kW of electricity per cubic meter of active material. A continuous three-day operation of several SMA engines could generate 7.2 kWh of electricity when installed on a 500 m long hot pipe network. This generated power can potentially reduce the carbon footprint by 5.1 kg of CO 2 illustrating the promise of this technology for addressing climate change.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Field-free spin-orbit torque switching assisted by in-plane unconventional spin torque in ultrathin [Pt/Co] N

Electrical manipulation of magnetization without an external magnetic field is critical for the development of advanced non-volatile magnetic-memory technology that can achieve high memory density and low energy consumption. Several recent studies have revealed efficient out-of-plane spin-orbit torques (SOTs) in a variety of materials for field-free type-z SOT switching. Here, we report on the corresponding type-x configuration, showing significant in-plane unconventional spin polarizations from sputtered ultrathin [Pt/Co] N , which are either highly textured on single crystalline MgO substrates or randomly textured on SiO 2 coated Si substrates. The unconventional spin currents generated in the low-dimensional Co films result from the strong orbital magnetic moment, which has been observed by X-ray magnetic circular dichroism (XMCD) measurement. The x-polarized spin torque efficiency reaches up to −0.083 and favors complete field-free switching of CoFeB magnetized along the in-plane charge current direction. Micromagnetic simulations additionally demonstrate its lower switching current than type-y switching, especially in narrow current pulses. Our work provides additional pathways for electrical manipulation of spintronic devices in the pursuit of high-speed, high-density, and low-energy non-volatile memory.

42 ENGINEERING↗
DOE OSTI2022

Optical Memory, Switching, and Neuromorphic Functionality in Metal Halide Perovskite Materials and Devices

Metal halide perovskite-based materials have emerged over the past few decades as remarkable solution-processable opto-electronic materials with many intriguing properties and potential applications. Notably, these emerging materials have recently been considered for their promise in low-energy memory and information processing applications. In particular, their large optical cross-sections, high photoconductance contrast, large carrier diffusion lengths, and mixed electronic/ionic transport mechanisms are attractive for enabling memory elements and neuromorphic devices that are written and/or read in the optical domain. Here, we review recent progress towards memory and neuromorphic functionality in metal halide perovskite materials and devices where photons are used as a critical degree of freedom for switching, memory, and neuromorphic functionality.

14 SOLAR ENERGY↗