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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Unusual electrical conductivity driven by localized stoichiometry modification at vertical epitaxial interfaces

Precise control of lattice mismatch accommodation and interfacial cation diffusion is critical to modulate correlated functionalities in epitaxial heterostructures, particularly when the interface composition is positioned near a compositional phase transition boundary. Here we select La 1-x Sr x MnO 3 (LSMO) as a prototype phase transition oxide and establish vertical epitaxial interfaces with NiO for exploring the strong interplay between strain accommodation, stoichiometry modification, and localized electron transport across the interface. It is found that localized stoichiometry modification overcomes the dead layer problem plaguing LSMO and leads to strongly directional conductivity, as manifested by a more than three orders of magnitude difference between out-of-plane and in-plane conductivity. Comprehensive atomic scale structural characterization and transport measurements reveal that this emerging behavior is created by a compositional change produced by preferential cation diffusion that pushes the LSMO phase transition from insulating into metallic within an ultrathin interface region. This study explores the nature of unusual electrical conductivity at vertical epitaxial interfaces and highlights the beneficial role of controllable cation diffusion that enables emerging functionalities for a broad range of potential applications such as memristors, spintronic devices, and novel nanoelectronic devices using strongly correlated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Lessons learned from first-principles calculations of transition metal oxides

Transition metal oxides (TMOs) are an important class of materials with diverse applications, ranging from memristors to photoelectrochemical cells. First-principles calculations are critical for understanding these complex materials at an atomic level and establishing relationships between atomic and electronic structures, particularly for probing quantities difficult or inaccessible to experiment. Here, we discuss computational strategies used to understand TMOs by focusing on two examples, a photoanode material, BiVO4, and an oxide for low-power electronics, La1−xSrxCoO3. We highlight key aspects required for the modeling of TMOs, namely, the descriptions of how oxygen vacancies, extrinsic doping, the magnetic state, and polaron formation impact their electronic and atomic structures and, consequently, many of the observed properties.

Vo, Hien (ORCID:0000000158315599)↗
DOE OSTI2024

Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque

Abstract Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNGs) can consume orders of magnitude less energy per bit than CMOS pseudo-RNGs. Here, we numerically investigate with a macrospin Landau–Lifshitz-Gilbert equation solver the use of pMTJs driven by spin–orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called ‘coinflips’, via an additional applied spin-transfer-torque current. Specifically, using a single, ideal pMTJ device we successfully draw integer samples on the interval [0, 255] from an exponential distribution based on p -value distribution analysis. In order to investigate device-to-device variations, the thermal stability of the pMTJs are varied based on manufactured device data. It is found that while repeatedly using a varied device inhibits ability to recover the probability distribution, the device variations average out when considering the entire set of devices as a ‘bucket’ to agnostically draw random numbers from. Further, it is noted that the device variations most significantly impact the highest level of the probability tree, with diminishing errors at lower levels. The devices are then used to draw both uniformly and exponentially distributed numbers for the Monte Carlo computation of a problem from particle transport, showing excellent data fit with the analytical solution. Finally, the devices are benchmarked against CMOS and memristor RNGs, showing faster bit generation and significantly lower energy use.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Metallicity of 2H-MoS 2 induced by Au hybridization

Recent studies have shown that MoS 2 can undergo a structural phase transition from the 2H to 1T' phase on Au substrates at moderate temperatures. In this study, we use ultraviolet and x-ray photoelectron spectroscopy, Raman and photoluminescence spectroscopy, and scanning tunneling microscopy/spectroscopy to probe the impact of annealing exfoliated, monolayer MoS 2 on Au. Our results across multiple length scales indicate that 2H-MoS 2 becomes hybridized with Au upon thermal annealing without inducing the 1T' structural phase and the bandgap can be modulated to zero width depending on the degree of hybridization. These results can be used to control the resistance of metal-MoS 2 contacts at the atomic scale without introduction of defects or structural phase transitions and are important for devices such as memristors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Network analysis of memristive device circuits: dynamics, stability and correlations

Abstract Networks with memristive devices are a potential basis for the next generation of computing devices. They are also an important model system for basic science, from modeling nanoscale conductivity to providing insight into the information-processing of neurons. The resistance in a memristive device depends on the history of the applied bias and thus displays a type of memory. The interplay of this memory with the dynamic properties of the network can give rise to new behavior, offering many fascinating theoretical challenges. But methods to analyze general memristive circuits are not well described in the literature. In this paper we develop a general circuit analysis for networks that combine memristive devices alongside resistors, capacitors and inductors and under various types of control. We derive equations of motion for the memory parameters of these circuits and describe the conditions for which a network should display properties characteristic of a resonator system. For the case of a purely memresistive network, we derive Lyapunov functions, which can be used to study the stability of the network dynamics. Surprisingly, analysis of the Lyapunov functions show that these circuits do not always have a stable equilibrium in the case of nonlinear resistance and window functions. The Lyapunov function allows us to study circuit invariances, wherein different circuits give rise to similar equations of motion, which manifest through a gauge freedom and node permutations. Finally, we identify the relation between the graph Laplacian and the operators governing the dynamics of memristor networks operators, and we use these tools to study the correlations between distant memristive devices through the effective resistance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Associative memories using complex-valued Hopfield networks based on spin-torque oscillator arrays

Abstract Simulations of complex-valued Hopfield networks based on spin-torque oscillators can recover phase-encoded images. Sequences of memristor-augmented inverters provide tunable delay elements that implement complex weights by phase shifting the oscillatory output of the oscillators. Pseudo-inverse training suffices to store at least 12 images in a set of 192 oscillators, representing 16 × 12 pixel images. The energy required to recover an image depends on the desired error level. For the oscillators and circuitry considered here, 5% root mean square deviations from the ideal image require approximately 5 μ s and consume roughly 130 nJ. Simulations show that the network functions well when the resonant frequency of the oscillators can be tuned to have a fractional spread less than 10 −3 , depending on the strength of the feedback.

Prasad, Nitin (ORCID:0000000180880164)↗
DOE OSTI2025

How Interface Evolution at the Nanoscale Dictates Protonic Ceramic Cell Behavior

Understanding the effects of heterointerfaces in nanodevices remains a challenge in interface engineering for systems applied in solid oxide fuel cells, catalysis, sensors, and memristors [1]. Previous studies have shown that the presence of interfaces in ceramic oxide nanodevices affects their impedance properties compared to bulk systems [2]. In this work, we analyze a thin-film model system designed to study the interfaces between representative electrode and electrolyte materials employed in protonic ceramic electrochemical cells for the reversible conversion of hydrogen o electricity. The investigated device features a 21-layer architecture that alternates between an electrode material PBSCF (PrBa 0.5 Sr 0.5 Co 1.5 Fe 0.5 O 5+ϵ ) and a proton-conducting electrolyte BZY (BaZr 0.8 Y 0.2 O 3-δ ), with uniform 20 nm thickness for each layer. Here, the device was fabricated using Pulsed Laser Deposition (PLD) and supported on a conductive Nb-doped STO (Nb-SrTiO 3 ) substrate.

dos Santos, Gabriel T. [Northwestern University, E↗
DOE OSTI2020

Dynamic control of nonequilibrium metal-insulator transitions

Here, we demonstrate a first order metal-insulator phase transition in the repulsive, fully frustrated, single-band Hubbard model as a function of the coupling to a fermion bath. Time dependent manipulation of the bath coupling allows switching between metallic and insulating states both across the phase transition and within the coexistence region. We propose a simple nanoelectronic device for experimentally realizing dynamic control of the bath coupling. Analysis of the device characteristics shows that it can act as a two-terminal memristor.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Determination of rutile transition metal oxide (110) surface terminations by scanning tunneling microscopy contrast reversal

The surfaces of rutile transition-metal oxides (TMO 2 ) are widely investigated for catalysis, photoelectrochemical solar cells, memristors, and supercapacitors, but their structures have remained controversial. Here we employ density functional theory to predict that a universal behavior of metallic TMO 2 surfaces, i.e., the stoichiometric TMO 2 surfaces, exhibit a contrast reversal in simulated scanning tunneling microscopy (STM) images at different scanning biases. The predictions are verified by experimental STM imaging of RuO 2 (110) surfaces and this feature is shown to enable accurate determinations of the TMO 2 (110) surface structures under various conditions. This research provides different insights into the electronic properties of TMO 2 (110) surfaces and offers an effective method to directly map the surface structure and point defects using bias-dependent STM.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Virtual Neuron: A Neuromorphic Approach for Encoding Numbers

Neuromorphic computers perform computations by emulating the human brain and are expected to be indispensable for energy-efficient computing in the future. They are primarily used in spiking neural network-based machine learning applications. However, neuromorphic computers are unable to preprocess data for these applications. Currently, data is preprocessed on a CPU or a GPU-this incurs a significant cost of transferring data from the CPU/GPU to the neuromorphic processor and vice versa. This cost can be avoided if preprocessing is done on the neuromorphic processor. To efficiently preprocess data on a neuromorphic processor, we first need an efficient mechanism for encoding data that can lend itself to all general-purpose preprocessing operations. Current encoding approaches have limited applicability and may not be suitable for all preprocessing operations. In this paper, we present the virtual neuron as a mechanism for encoding integers and rational numbers on neuromorphic processors. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware and show that it can perform an addition operation using 23 nJ of energy on average using a mixed-signal, memristor-based neuromorphic processor. The virtual neuron encoding approach is the first step in preprocessing data on a neuromorphic processor.

Date, Prasanna↗
DOE OSTI2024

High energy density picoliter-scale zinc-air microbatteries for colloidal robotics

The recent interest in microscopic autonomous systems, including microrobots, colloidal state machines, and smart dust, has created a need for microscale energy storage and harvesting. However, macroscopic materials for energy storage have noted incompatibilities with microfabrication techniques, creating substantial challenges to realizing microscale energy systems. Here, we photolithographically patterned a microscale zinc/platinum/SU-8 system to generate the highest energy density microbattery at the picoliter (10 −12 liter) scale. The device scavenges ambient or solution-dissolved oxygen for a zinc oxidation reaction, achieving an energy density ranging from 760 to 1070 watt-hours per liter at scales below 100 micrometers lateral and 2 micrometers thickness in size. The parallel nature of photolithography processes allows 10,000 devices per wafer to be released into solution as colloids with energy stored on board. Within a volume of only 2 picoliters each, these primary microbatteries can deliver open circuit voltages of 1.05 ± 0.12 volts, with total energies ranging from 5.5 ± 0.3 to 7.7 ± 1.0 microjoules and a maximum power near 2.7 nanowatts. We demonstrated that such systems can reliably power a micrometer-sized memristor circuit, providing access to nonvolatile memory. We also cycled power to drive the reversible bending of microscale bimorph actuators at 0.05 hertz for mechanical functions of colloidal robots. Additional capabilities, such as powering two distinct nanosensor types and a clock circuit, were also demonstrated. The high energy density, low volume, and simple configuration promise the mass fabrication and adoption of such picoliter zinc-air batteries for micrometer-scale, colloidal robotics with autonomous functions.

Robotics↗
DOE OSTI2023

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

The Abisko project aims to develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation. The SNN architecture explores novel neuromorphic devices that are based on resistive-switching materials, such as memristors and electrochemical RAM. Equally important, Abisko uses a deep codesign approach to pursue this goal by engaging experts from across the entire range of disciplines: materials, devices and circuits, architectures and integration, software, and algorithms. Here, the key objectives of our Abisko project are threefold. First, we are designing an energy-optimized high-performance neuromorphic accelerator based on SNNs. This architecture is being designed as a chiplet that can be deployed in contemporary computer architectures and we are investigating novel neuromorphic materials to improve its design. Second, we are concurrently developing a productive software stack for the neuromorphic accelerator that will also be portable to other architectures, such as field-programmable gate arrays and GPUs. Third, we are creating a new deep codesign methodology and framework for developing clear interfaces, requirements, and metrics between each level of abstraction to enable the system design to be explored and implemented interchangeably with execution, measurement, a model, or simulation. As a motivating application for this codesign effort, we target the use of SNNs for an analog event detector for a high-energy physics sensor.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Redox transistors based on TiO 2 for analogue neuromorphic computing

The ability to train deep neural networks on large data sets have made significant impacts onto artificial intelligence, but consume significant amounts of energy due to the need to move information from memory to logic units. In-memory "neuromorphic" computing presents an alternative framework that processes information directly on memory elements. In-memory computing has been limited by the poor performance of the analogue information storage element, often phase-change memory or memristors. To solve this problem, we developed two types of "redox transistors" using TiO 2 (anatase) which stores analogue information states through the electrochemical concentration of dopants in the crystal. The first type of redox transistor uses lithium as the electrochemical dopant ion, and its key advantage is low operating voltage. The second uses oxygen vacancies as the dopant, which is CMOS compatible and can retain state even when scaled to nanosized dimensions. Both devices offer significant advantages in terms of predictable analogue switching over conventional filamentary-based devices, and provide a significant advance in developing materials and devices for neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Hardware and Software Co-design Framework for Energy Efficient Neuromorphic Systems

Neuromorphic systems can be realized by a variety of algorithms and architectures. A common understanding is that spiking neuromorphic designs, which encode information into spatio-temporal spiking events, are both a biologically-accurate and efficient way of processing information. However, representing the information through timing relationships induces sophisticated circuit designs in traditional CMOS-based implementations. In recent years, high-capacity resistive memory (RRAM, aka, memristor) has demonstrated great potential in mimicking synaptic behaviors. Several RRAM-based spiking neuromorphic designs exist, most of which focus on rate coding schemes. These designs simplify circuit implementations of neuron models and explore challenges such as unsatisfactory speed, resolution, and performance. As an alternative, we will explore temporal coding spiking neuromorphic systems that encode information as the relative timing of neuron activations (spikes), which have been proven to be more adaptive and energy-efficient. Developing a neuromorphic system for spiking neural network (SNN) inference and online training, however, faces some major technical challenges: (1) It lacks circuit implementation support for temporal-coding SNN to achieve satisfying power efficiency and accuracy; (2) Although existing research works have investigated memristive synapse and neuron designs for spike-timing-dependent plasticity, the non-ideal conditions in implementation, such as device variations and signal degradation, degrade online learning accuracy of large scale systems; and (3) Non-optimized, inter-layer data traffic in SNNs, leads to unnecessary data communication costs. In this project, we plan to address these challenges by a hardware and software co-design framework that incorporates solutions at the circuit, architecture, and algorithm levels. At the circuit-level, we will elaborate on the in-situ SNN processing element designs for supporting both inference and online training modes. Variation-aware schemes will be studied to improve reliability. At the architecture level, we propose a pipelined, asynchronous architecture to retain the timing resolution of spikes. At the algorithm level, we will investigate an innovative SNN training algorithm for enabling activation sparsification and reducing unnecessary data communication costs. This neuromorphic system will provide an effective solution to real-life energy-constrained applications and significantly contribute to the exploration of next-generation high-performance computing systems under the DOE context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

2D Amorphous Carbon Dielectric Prepared from Solution Precursor for Nanoelectronics

The preparation of amorphous 2D nanodielectrics represents a major challenge due to the metastable nature of amorphous phases. We describe a scalable and solution-based strategy to prepare wafer-scale 2D amorphous carbon with thickness down to 1–2 atomic layers from coal-derived carbon quantum dots as precursors. The prepared atomically thin 2D amorphous carbon can be suspended over cavities as freestanding membranes with high modulus of 400±100 GPa and demonstrate robust dielectric properties with dielectric strength above 20 MV·cm-1 and leakage current density below 10-4 A·cm-2 through a scaled thickness of three-atomic layers. When implemented as ultrathin gate dielectrics in 2D transistors or ion-transport media in memristors, they enable exceptional device performance and spatiotemporal uniformity, resulting from their amorphous form, intrinsic ultrathinness, and 2D atomic structures.

Pham, Viet Hung↗
DOE OSTI2024

Solution-Based Synthesis of Ultrathin Quasi-2D Amorphous Carbon for Nanoelectronics

The synthesis of ultrathin 2D amorphous dielectric film represents a major challenge due to the metastable nature of amorphous phases. We describe a scalable and solution-based strategy to prepare wafer-scale 2D amorphous carbon with thickness down to 1–2 atomic layers from coal-derived carbon quantum dots as precursors. The prepared atomically thin 2D amorphous carbon can be suspended over cavities as freestanding membranes with high modulus of 400±100 GPa and demonstrate robust dielectric properties with dielectric strength above 20 MV·cm-1 and leakage current density below 10-4 A·cm-2 through a scaled thickness of three-atomic layers. When implemented as ultrathin gate dielectrics in 2D transistors or ion-transport media in memristors, they enable exceptional device performance and spatiotemporal uniformity, resulting from their amorphous form, intrinsic ultrathinness, and 2D atomic structures.

Pham, Viet Hung [NETL Site Support Contractor, Nat↗
DOE OSTI2024

Selective CW Laser Synthesis of MoS2 and Mixture of MoS2 and MoO2 from (NH4)2MoS4 Film

Very recently, the synthesis of 2D MoS2 and WS2 through pulsed laser-directed thermolysis can achieve wafer-scale and large-area structures, in ambient conditions. In this paper, we report the synthesis of MoS2 and MoS2 oxides from (NH4)2MoS4 film using a visible continuous-wave (CW) laser at 532 nm, instead of the infrared pulsed laser for the laser-directed thermolysis. The (NH4)2MoS4 film is prepared by dissolving its crystal powder in DI water, sonicating the solution, and dip-coating onto a glass slide. We observed a laser intensity threshold for the laser synthesis of MoS2, however, it occurred in a narrow laser intensity range. Above that range, a mixture of MoS2 and MoO2 is formed, which can be used for a memristor device, as demonstrated by other research groups. We did not observe a mixture of MoS2 and MoO3 in the laser thermolysis of (NH4)2MoS4. The laser synthesis of MoS2 in a line pattern is also achieved through laser scanning. Due to of the ease of CW beam steering and the fine control of laser intensities, this study can lead toward the CW laser-directed thermolysis of (NH4)2MoS4 film for the fast, non-vacuum, patternable, and wafer-scale synthesis of 2D MoS2.

(NH4)2MoS4↗
DOE OSTI2020

The organic redox transistor for neuromorphic computing

Inspired by the in-memory computing architectures of biological systems, neuromorphic computing using crossbar arrays of artificial synapses based on non-volatile memory (NVM) devices with variable conductances has emerged as a new paradigm to enable massively parallel and ultra-low power computing hardware for data centric applications. Although inference has been demonstrated successfully using crossbars based on a variety of NMV technologies, efficient learning and scaling to large arrays (>10 6 elements) remains a challenge due to the synaptic elements' non-ideal electrical characteristics which degrades ANN accuracy. A further challenge is that in the conductive state memristors draw large currents >μA resulting in significant voltage drops in the interconnect wires and increased probability of failure in scaled arrays. We suggest the organic polymer redox transistor (RT) is an alternate approach that could solve many of these challenges, enabling both inference and parallel outer product updates, as recently demonstrated by Fuller et al. An RT consists of redox-active channel and gate electrodes in contact with a liquid or solid electrolyte. lon insertion through the electrolyte controls the channel electronic conductivity, while electron transfer through an external circuit maintains overall charge neutrality. Unlike a rechargeable battery, in the RT the voltage built-up across the electrolyte is kept to a minimum (typically <100 mV) by using the same material for the gate and channel. Elimination of the voltage offset simplifies integration of the RT into programmable arrays by enabling the use of various selectors. RTs based on inorganic and organic materials have been recently demonstrated with conductance tuning occurring at potentials of just a few mV and hundreds to thousands of linearly and symmetrically programmable conductance states, enabling near ideal accuracy in neural network simulations. Introduced in the 1980's, redox transistors with metallic gate electrodes and organic channel materials, also known as organic electrochemical transistors (OECTs), have been explored for a variety of applications such as chem- and bio-sensing, neural interfaces, and low cost printed circuits. A typical channel material for OECTs is the conducting polymer poly(3,4-ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS). PEDOT is a p-type semiconducting polymer with mobile positively charged polarons that hop chain-to-chain.

Talin, Albert Alec↗