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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Eigenvalue-Based Micromagnetic Analysis of Switching in Spin-Torque-Driven Structures

We present an eigenvalue-based approach for studying the magnetization dynamics in magnetic nanostructures driven by spintronic excitations, such as spin-transfer torque and spin-orbit torque. The approach represents the system dynamics in terms of normal oscillation modes (eigenstates) with corresponding complex eigenfrequencies. The dynamics is driven by a small number of active eigenstates and often considering just a single eigenstate is sufficient. We develop a perturbation theory that provides semianalytical dynamic solutions by using eigenstates for the case in the absence of damping and spintronic excitations as a basis. The approach provides useful insights into dynamics in such systems and allows solving several difficulties in their modeling, such as extracting the switching current in magnetic random-access memories and understanding switching mechanisms. We show that the presented approach directly predicts the critical switching current, i.e., switching current for an infinite time. The approach also provides solutions for the switching dynamics allowing the switching current to be obtained for a finite switching time, provided that the system symmetry is broken, e.g., by tilting the polarizer, so that switching by a finite pulse is possible.

42 ENGINEERING↗
DOE OSTI2022

Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions

The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann bottleneck, include in-memory and near-memory architectures, as well as algorithmic approaches. In this report we leverage the low-power and the inherently binary operation of magnetic tunnel junctions (MTJs) to demonstrate neural network hardware inference based on passive arrays of MTJs. In general, transferring a trained network model to hardware for inference is confronted by degradation in performance due to device-to-device variations, write errors, parasitic resistance, and nonidealities in the substrate. To quantify the effect of these hardware realities, we benchmark 300 unique weight matrix solutions of a two-layer perceptron to classify the Wine dataset for both classification accuracy and write fidelity. Despite device imperfections, we achieve software-equivalent accuracy of up to 95.3% with proper tuning of network parameters in 15 x 15 MTJ arrays having a range of device sizes. The success of this tuning process shows that new metrics are needed to characterize the performance and quality of networks reproduced in mixed signal hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Harnessing ionic complexity: A modeling approach for hierarchical ionic circuit design

Since the 1950s, soft ionic devices have evolved from individual components to an expanding library of sensors, actuators, signal transmitters, and processors. However, integrating these components into complex, multifunctional systems remains challenging due to the nonintuitive and nonlinear interactions between ionic elements. In this work, we address these fundamental challenges by developing a lumped element model that enables interrogation of the physics that governs ionic circuits, as well as rapid design and optimization. Our model captures features specific to ionic charge carriers, while preserving the hierarchical design flexibility and computational efficiency of traditional circuit modeling. We demonstrate that our model can not only fit individual device behavior but also accurately predict the behavior of larger circuits formed by combining those devices. Additionally, we show how our tool utilizes the intrinsic nonlinearities of ionic systems to enable extended functionality, revealing how factors such as ion enrichment, ion leakage, and polymer charge density influence performance. Lastly, we present a fully ionic power supply, sensor, control system, and actuator for a soft robot that adapts its motion in response to environmental salt, illustrating the tool’s potential to accelerate advancements in chemical sensing, biointerfacing, biomimetic systems, and adaptive materials.

42 ENGINEERING↗
DOE OSTI2021

OPERATING EXPERIENCE AND ITS RELATIONSHIP TO EMBEDDED DIGITAL DEVICES IN SAFETY SYSTEMS

Interest in embedded digital devices (EDDs) for monitoring and providing control of components is increasing and the added functionality digital brings such as diagnostics, displays, control functions is welcomed in both the nuclear and other industries. As a result, pure analog-based components continue to disappear from the industrial I&C marketplace. Equipment with EDDs, also known by related terms such as smart devices, intelligent devices, and digital devices of limited functionality, can include sensors, breakers, priority logic modules, time delay relays, pumps, valve actuators, motor control centers, and uninterruptible power supplies. Not only is equipment consisting of analog and older digital technology being replaced with commercial grade products containing EDDs, but EDDs can also be retrofitted to work with existing components. EDDs may be installed physically within a component, such as in a relay. There are also external devices that some would not consider an EDD, but that operate in almost all technical ways identically to one internal to a component such as in a valve controller. In fact, the digital portions may be located hundreds of feet from the actual component.Due to the potential of being used in an identical manner and without alteration from their application in other industries, operating experience (OpE) for EDDs may be much more applicable than in the case of other more system level devices. Clear separation and independence of functionality provided with EDDs may also allow greater applicability of existing OpE from industry for changes that can be shown not to affect its safety function. However, OpE also demonstrates issues with different conditions between other industries and nuclear power plants, such as issues with undeclared changes or undeclared digital devices and the new failure modes they may introduce, issues with the reporting of OpE in industry, and a lack of transparency into OpE for EDDs.Understanding and addressing these issues will be key in the future use of EDDs in nuclear power plants, but failure rate data is difficult to obtain for EDDs, and lessons learned from the use of EDDs are difficult to collect and identify.Key words: embedded digital device, smart device, operating experience, failure modes

Muhlheim, Michael↗
DOE OSTI2022

Programmable soft valves for digital and analog control

In soft devices, complex actuation sequences and precise force control typically require hard electronic valves and microcontrollers. Existing designs for entirely soft pneumatic control systems are capable of either digital or analog operation, but not both, and are limited by speed of actuation, range of pressure, time required for fabrication, or loss of power through pull-down resistors. Using the nonlinear mechanics intrinsic to structures composed of soft materials—in this case, by leveraging membrane inversion and tube kinking—two modular soft components are developed: a piston actuator and a bistable pneumatic switch. These two components combine to create valves capable of analog pressure regulation, simplified digital logic, controlled oscillation, nonvolatile memory storage, linear actuation, and interfacing with human users in both digital and analog formats. Three demonstrations showcase the capabilities of systems constructed from these valves: 1) a wearable glove capable of analog control of a soft artificial robotic hand based on input from a human user’s fingers, 2) a human-controlled cushion matrix designed for use in medical care, and 3) an untethered robot which travels a distance dynamically programmed at the time of operation to retrieve an object. This work illustrates pathways for complementary digital and analog control of soft robots using a unified valve design.

42 ENGINEERING↗
DOE OSTI2025

A Digital Three Level Space Vector Modulator for High Frequency Vector Sequence Generation

This letter proposes a digital high-speed three-level space vector pulse width modulator (3L-SVPWM). A conventional 3L-SVPWM is typically computation-based, involving a sequential execution of sub-tasks on a digital signal processor (DSP) based controller. The resulting high computation time of 5.4 μs limits the implementation of additional control blocks for switching frequencies greater than 100 kHz. This is overcome by transforming sub-tasks into digital blocks with 1-0 decisions and simpler arithmetic operations. The sub-task blocks are executed concurrently on a programmable logic device (PLD). Hence, a fast 3L-SVPWM execution in 140 ns is achieved. The proposed digital 3L-SVPWM enables high switching frequency operation of wide bandgap (WBG) device-based 3 L inverters to generate high fundamental frequency waveforms. A finite state machine is an integral part of the proposed implementation with the ability to generate any vector sequence, maximizing the usage of redundant vector states in 3L-SVPWM. Here, the proposed digital 3L-SVPWM operation is demonstrated with a GaN-based 3 L active neutral point clamped (3L-ANPC) inverter. Experimental results are presented at 250 kHz switching frequency to generate vector sequences for center-aligned SVPWM (CA-SVPWM) and common mode voltage reduced SVPWM (CMVR-SVPWM). The results also showcase a high fundamental frequency generation capability of 10 kHz.

active neutral point clamped inverter↗
DOE OSTI2022

Radiation-Hardened GaN HEMT and Cell Design, Modeling, and Fabrication for Nuclear Instrumentation Applications

Recent advances in nuclear power generation technologies show great promise for efficient, safe generation of carbon-free power in the near future. Consequently, many new generation reactor designs are being pursued by government and industry groups, including both fission- and fusion-based technologies. Radiation- and temperature-tolerant sensor technology continues to advance, yet development of the electronics/instrumentation technologies suitable for these environments have fallen behind. Industry has adequately addressed harsh environment electronics needs for low earth orbit satellites, but the much more extreme conditions associated with electronics placed near or in a reactor core remain unaddressed. This project investigates the use of gallium nitride (GaN) circuit technology to address the unique needs for sensor interface electronics and communications in reactor environments. This report provides a summary of the first year’s project activities related to developing and optimizing devices and circuits for this GaN high electronic mobility transistor (HEMT) process. Activities reported include multiple analog and digital circuit designs simulated using custom Verilog-A models generated from measured GaN devices fabricated in the target process. Digital circuit designs included fundamental logic circuits such as an inverter, NAND, NOR, AND and OR gates, as well as a 5-stage ring oscillator. Each design was simulated using an open-source SPICE simulator, and an integrated circuit layout was produced of each design for use in future fabrication. Analog circuit designs focused on the fundamental building blocks to be used to construct sensor interface and communications circuits. These included current mirrors, matched differential pairs, and a Gilbert cell mixer design, each with an associated integrated circuit layout. Layout tools used for these designs included two open-source packages—KLayout and Magic—which were customized for specific use with the OSU GaN process layers. Design rules and preliminary device extraction capabilities were built for the Magic tool to enable device extraction and subsequent netlist generation for SPICE simulation. Finally, future research directions for year two are summarized. When the year-two directions are implemented, the project will be in position to advance the state of the art in electronics for near- or in-reactor sensor interfacing and communications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A nanocryotron memory and logic family

The development of superconducting electronics based on nanocryotrons has been limited so far to few device circuits, in part due to the lack of standard and robust logic cells. Here, we introduce and experimentally demonstrate designs for a set of nanocryotron-based building blocks that can be configured and combined to implement memory and logic functions. The devices were fabricated by patterning a single superconducting layer of niobium nitride and measured in liquid helium on a wide range of operating points. The tests show 10 - 4 bit error rates with above ± 20 % margins up to 50 MHz and the possibility of operating under the effect of an out-of-plane 36 mT magnetic field, with ± 30 % margins at 10 MHz. Additionally, we designed and measured an equivalent delay-flip-flop made of two memory cells to show the possibility of combining multiple building blocks to make larger circuits. These blocks may constitute a solid foundation for the development of nanocryotron logic circuits and finite-state machines with potential applications in the integrated processing and control of superconducting nanowire single-photon detectors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Analog-to-digital converter based on voltage-controlled superconducting devices

The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra-energy-efficient data conversion architectures that remain functional at deep cryogenic temperatures. Here, in this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a voltage-controlled quantum-enhanced Josephson junction field-effect transistor (JJFET). Exploiting its strong gate tunability and transistor-like behavior, the JJFET offers a scalable alternative to conventional current-controlled superconducting devices while aligning naturally with CMOS-style design methodologies. Building on our previously developed Verilog-A compact model calibrated to experimental data, we design and simulate a three-bit JJFET-based flash ADC targeted for integration within cryogenic control and readout circuitry in quantum computing. The core comparator block is realized through careful bias current selection and augmented with a three-terminal nanocryotron to precisely define reference voltages. Cascaded JJFET comparators ensure robust voltage gain, cascadability, and logic-level restoration across stages. Simulation results demonstrate accurate quantization behavior with ultra-low power dissipation, underscoring the feasibility of voltage-driven superconducting mixed-signal circuits. This work establishes a critical step toward unifying superconducting logic and data conversion, paving the way for scalable cryogenic architectures in quantum–classical co-processors, low-power artificial intelligence accelerators, and next-generation energy-constrained computing platforms.

Analog-to-digital converter↗
DOE OSTI2024

Nonvolatile electrochemical memory at 600°C enabled by composition phase separation

Silicon-based microelectronics are limited to ~150°C and therefore not suitable for the extremely high temperatures in aerospace, energy, and space applications. While wide-band-gap semiconductors can provide high-temperature logic, nonvolatile memory devices at high temperatures have been challenging. In this work, we develop a nonvolatile electrochemical memory cell that stores and retains analog and digital information at temperatures as high as 600°C. Through correlative scanning transmission electron microscopy, we show that this high-temperature information retention is a result of composition phase separation between the oxidized and reduced forms of amorphous tantalum oxide. This result demonstrates a memory concept that is resilient at extreme temperatures and reveals phase separation as the principal mechanism that enables nonvolatile information storage in these electrochemical memory cells.

42 ENGINEERING↗
DOE OSTI2021

In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Controllable Reset Behavior in Domain Wall–Magnetic Tunnel Junction Artificial Neurons for Task-Adaptable Computation

Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall–magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial neuronal functionality when executing repeated tasks. In this letter, we demonstrate that this behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms: shape anisotropy, magnetic field, and current-driven soft reset. Using micromagnetics and analytical device modeling to classify the Optdigits handwritten digit dataset, we show that edgy-relaxed behavior improves both classification accuracy and classification rate for ordered datasets while sacrificing little to no accuracy for a randomized dataset. This letter establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.

42 ENGINEERING↗
DOE OSTI2023

Creating a Digital Twin for a Hydropower System through 3D Object Modeling: Alder Dam FY23

Hydropower stands as the world's leading source of renewable electricity, with hydropower plants spanning the globe. A typical hydropower plant comprises essential components including the governor, excitation system, generator, thrust bearing, hydraulic turbine, transformer, main lead, metering and control devices, tailwater depression system, and dissolved oxygen monitoring. Each of these components undergoes various measures and monitoring procedures. These measurements are collected through a diverse array of systems, encompassing standalone sensors, programmable logic controllers (PLCs), Supervisory Control and Data Acquisition (SCADA) systems, Internet of Things (IoT) devices, and data acquisition and integration platforms like OSI/PI.

13 HYDRO ENERGY↗
DOE OSTI2024

Neural units with time-dependent functionality

We show that the time-resolved dynamics of an underdamped harmonic oscillator can be used to do multifunctional computation, performing distinct computations at distinct times within a single dynamical trajectory. We consider the amplitude of an oscillator whose inputs influence its frequency. The activity of the oscillator at fixed times is a nonmonotonic function of its inputs, so it can solve problems such as XOR that are not linearly separable. The activity of the oscillator at fixed input is a nonmonotonic function of time, so it is multifunctional in a temporal sense, and able to carry out distinct nonlinear computations at distinct times within the same dynamical trajectory. We show that a single oscillator, observed at different times, can act as all of the elementary logic gates and perform binary addition, the latter usually implemented in hardware using five logic gates. We show that a set of n oscillators, observed at different times, can perform an arbitrary number of analog-to-n-bit digital conversions. We also show that oscillators can be trained by gradient descent to perform distinct classification tasks at distinct times. Computing with time-dependent functionality can be done in or out of equilibrium, and suggests a way of reducing the number of parameters or devices required to do nonlinear computations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Will Stochastic Devices Play Nice With Others in Neuromorphic Hardware?: There’s More to a Probabilistic System Than Noisy Devices

Achieving brain-like efficiency in computing requires a co-design between the development of neural algorithms, brain-inspired circuit design, and careful consideration of how to use emerging devices. The recognition that leveraging device-level noise as a source of controlled stochasticity represents an exciting prospect of achieving brain-like capabilities in probabilistic neural algorithms, but the reality of integrating stochastic devices with deterministic devices in an already-challenging neuromorphic circuit design process is formidable. Here, we explore how the brain combines different signaling modalities into its neural circuits as well as consider the implications of more tightly integrated stochastic, analog, and digital circuits. Further, by acknowledging that a fully CMOS implementation is the appropriate baseline, we conclude that if mixing modalities is going to be successful for neuromorphic computing, it will be critical that device choices consider strengths and limitations at the overall circuit level.

42 ENGINEERING↗
DOE OSTI2024

Entropy Analysis of FPGA Interconnect and Switch Matrices for Physical Unclonable Functions

Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field-programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., look-up tables (LUTs), flip-flops (FFs), etc., using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs is created to fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across-chip routing. Data are collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Signal propagation in reversible digital mechanics

Digital mechanics explores information processing through binary, mechanical circuits. This work demonstrates a flexural, mechanical integrated circuit (m-IC) that achieves reversible, non-reciprocal signal propagation through integrated AND logic and memory. Our approach exploits sequential bistable transitions with symmetric energy wells, tunable stiffness, impedance matching, and AND gate non-linearity, to enable signal propagation, repeatability, and reversibility. We present a generalized model of logic kinematics and energetics, validated experimentally, to study energy flows, quantify energetic limits, and identify operating regimes for reversible logic. Macro-scale experiments confirm propagation dynamics, and new fabrication methods extend the architecture to micro-scale devices. By achieving controlled, reversible signal transmission across interconnected logic and memory, this work establishes a scalable platform for robust mechanical computing and adaptive sensing.

Johnson, Hilary A. [Lawrence Livermore National La↗
DOE OSTI2022

GaN HEMT Fabrication for Radiation-Hardened Sensing and Communications Electronics

Gallium nitride (GaN), a wide bandgap semiconductor, has vast potential to address two environment conditions associated with the application of electronics to nuclear power: elevated temperatures, and high levels of radiation. A process was developed at The Ohio State University (OSU) to enable fabrication of complex digital and analog electronics circuits for application to nuclear power environments such as locations near or in the reactor core or in spent nuclear fuel casks. Radio frequency (RF)-grade GaN high electron mobility transistor (HEMT) devices were fabricated as part of this process. These were fabricated as depletion mode and enhancement mode devices. They have been electrically characterized and have demonstrated the expected performance. Behavioral models (Verilog-A) were developed from these device measurements to enable electrical simulation of GaN HEMT devices and circuits using common electronics simulation tools based on Simulation Program with Integrated Circuit Emphasis (SPICE). A similar set of GaN HEMT devices is being developed to provide lower speed devices for logic and analog functions. These accomplishments position this technology for effective application to sensor interfacing, signal processing, and data communications in nuclear power plants, including operation in or near the reactor core.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗