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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Photothermally Activated Artificial Neuromorphic Synapses

Biological nervous systems rely on the coordination of billions of neurons with complex, dynamic connectivity to enable the ability to process information and form memories. In turn, artificial intelligence and neuromorphic computing platforms have sought to mimic biological cognition through software-based neural networks and hardware demonstrations utilizing memristive circuitry with fixed dynamics. Here, to incorporate the advantages of tunable dynamic software implementations of neural networks into hardware, we develop a proof-of-concept artificial synapse with adaptable resistivity. This synapse leverages the photothermally induced local phase transition of VO 2 thin films by temporally modulated laser pulses. Such a process quickly modifies the conductivity of the film site-selectively by a factor of 500 to “activate” these neurons and store “memory” by applying varying bias voltages to induce self-sustained Joule heating between electrodes after activation with a laser. These synapses are demonstrated to undergo a complete heating and cooling cycle in less than 120 ns.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

In Situ Studies of the Swelling by an Electrolyte in Electrochemical Doping of Ethylene Glycol-Substituted Polythiophene

Organic mixed ionic electronic conductors (OMIECs) have the potential to enable diverse new technologies, ranging from biosensors to flexible energy storage devices and neuromorphic computing platforms. However, a study of these materials in their operating state, which convolves both passive and potential-driven solvent, cation, and anion ingress, is extremely difficult, inhibiting rational material design. In this report, we present a novel approach to the in situ studies of the electrochemical switching of a prototypical OMIEC based on oligoethylene glycol (oEG) substitution of semicrystalline regioregular polythiophene via grazing-incidence X-ray scattering. Here by studying the crystal lattice both dry and in contact with the electrolyte while maintaining potential control, we can directly observe the evolution of the crystalline domains and their relationship to film performance in an electrochemically gated transistor. Despite the oEG side-chain enabling bulk electrolyte uptake, we find that the crystalline regions are relatively hydrophobic, exhibiting little (less than one water per thiophene) swelling of the undoped polymer, suggesting that the amorphous regions dominate the reported passive swelling behavior. With applied potential, we observe that the π–π separation in the crystals contracts while the lamella spacing increases in a balanced fashion, resulting in a negligible change in the crystal volume. The potential-induced changes in the crystal structure do not clearly correlate to the electrical performance of the film as an organic electrochemical transistor, suggesting that the transistor performance is strongly influenced by the amorphous regions of the film.

36 MATERIALS SCIENCE↗
DOE OSTI2020

P-Type Electrochemical Doping Can Occur by Cation Expulsion in a High Performing Polymer for Organic Electrochemical Transistors

We investigate the mechanism of ion-dependent charge compensation during electrochemical oxidation (doping) of the model mixed ionic/electronic transporting polythiophene derivative poly(3-{[2-(2-methoxyethoxy)ethoxy]methyl}thiophene-2,5-diyl) (P3MEEMT). Using a combination of electrochemical quartz microbalance gravimetry and glow discharge optical emission spectroscopy, we show that charge compensation during polymer redox processes proceeds via a cation-dependent mechanism. For p-type polymer oxidation in certain electrolytes, charge compensation is achieved by both eventual injection of anions into the film, as well as initial expulsion of cations from the film. We compare doping mechanisms for a variety of electrolyte salts including potassium chloride, tetrabutylammonium chloride, potassium hexafluorophosphate (KPF6), and tetrabutylammonium hexafluorophosphate. For the electrolyte KPF6, both the cations and anions coexist in the water-swelled polymer even prior to application of electrical bias. Our data indicate that electrochemical doping (hole injection into the polymer and ionic charge compensation) proceeds via the following mechanism: (1) hydration of the neutral film by electrolyte (water, cations, anions), (2) cation (K+) expulsion from the film upon initial application of an oxidative bias, and (3) anion injection into the film at higher oxidation/doping levels (>~2 × 1020/cm3). Understanding the mechanism of charge compensation during the doping process should allow for the design of improved mixed ionic/electronic conductors for use in applications ranging from organic supercapacitors and redox flow batteries to bioelectronic sensors, thermoelectrics, and devices for neuromorphic computing.

Flagg, Lucas Q.↗
DOE OSTI2022

van der Waals Semiconductor Empowered Vertical Color Sensor

We report biomimetic artificial vision is receiving significant attention nowadays, particularly for the development of neuromorphic electronic devices, artificial intelligence, and microrobotics. Nevertheless, color recognition, the most critical vision function, is missed in the current research due to the difficulty of downscaling of the prevailing color sensing devices. Conventional color sensors typically adopt a lateral color sensing channel layout and consume a large amount of physical space, whereas compact designs suffer from an unsatisfactory color detection accuracy. In this work, we report a van der Waals semiconductor-empowered vertical color sensing structure with the emphasis on compact device profile and precise color recognition capability. More attractive, we endow color sensor hardware with the function of chromatic aberration correction, which can simplify the design of an optical lens system and, in turn, further downscales the artificial vision systems. Also, the dimension of a multiple pixel prototype device in our study confirms the scalability and practical potentials of our developed device architecture toward the above applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm 2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Metal–insulator transition tuned by oxygen vacancy migration across TiO2/VO2 interface

Abstract Oxygen defects are essential building blocks for designing functional oxides with remarkable properties, ranging from electrical and ionic conductivity to magnetism and ferroelectricity. Oxygen defects, despite being spatially localized, can profoundly alter global properties such as the crystal symmetry and electronic structure, thereby enabling emergent phenomena. In this work, we achieved tunable metal–insulator transitions (MIT) in oxide heterostructures by inducing interfacial oxygen vacancy migration. We chose the non-stoichiometric VO 2-δ as a model system due to its near room temperature MIT temperature. We found that depositing a TiO 2 capping layer on an epitaxial VO 2 thin film can effectively reduce the resistance of the insulating phase in VO 2 , yielding a significantly reduced R OFF /R ON ratio. We systematically studied the TiO 2 /VO 2 heterostructures by structural and transport measurements, X-ray photoelectron spectroscopy, and ab initio calculations and found that oxygen vacancy migration from TiO 2 to VO 2 is responsible for the suppression of the MIT. Our findings underscore the importance of the interfacial oxygen vacancy migration and redistribution in controlling the electronic structure and emergent functionality of the heterostructure, thereby providing a new approach to designing oxide heterostructures for novel ionotronics and neuromorphic-computing devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electrically-driven IMT and volatile memristor behavior in NdNiO 3 films

Abstract Transition metal oxides with insulator-metal transitions (IMTs) are uniquely suited for volatile memristor devices that mimic the spiking of biological neurons. Unlike most non-volatile memristors, which often operate via ion migration into filaments, volatile devices utilize a reversible phase change that returns to a ground state in the absence of applied stimulus. In these devices, Joule heating triggers the IMT and changes the bulk resistivity rather than influencing conduction through defects, as in previous studies. This volatile resistive switching behavior has previous been leveraged in niobium and vanadium oxides, but not in rare-earth nickelates, despite their tunable transition temperatures. This study demonstrates an electrically driven IMT in the prototypical rare-earth nickelate, NdNiO 3 , in large area devices. While previous work examining the electrically-driven IMT in NdNiO 3 suggests defect-dominated conduction, this study shows clear s-type negative differential resistance (NDR) consistent with temperature-dependent resistivity measurements. The NDR peak-to-valley voltage scales linearly with temperature as expected for conductivity pathways dominated by bulk IMT behavior. Unlike other transition metal oxides, which are modeled using the insulator-metal phase fraction as the internal state variable, a thermoelectric model with temperature as the internal state variable is found to more accurately describe the current–voltage characteristic of NdNiO 3 volatile memristors. Overall, we report the synthesis, fabrication, and characterization of NdNiO 3 volatile memristors with resistivity dominated by bulk-like IMT behavior which is scalable and not dependent upon oxygen vacancy migration or defect mediated conduction pathways.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Probing liquid/liquid interfaces at and away from equilibrium using vibrational sum frequency generation

The self-assembly and transport of species at oil/aqueous interfaces represent key mechanistic steps in processes spanning from liquid extraction to soft-matter neuromorphic electrical devices. While technologically and fundamentally important, there is a notable lack of chemical understanding regarding these buried interfaces due to challenges in differentiating species located at the surface from the nearby bulk phases. In this work, we will describe some of the challenges in probing chemistry at these liquid/liquid interfaces and detail the approaches we have developed using vibrational sum frequency generation to enable studies of these complex interfaces at and away from equilibrium. From these spectroscopic developments, new insight into solvation, ion pairing, aggregation, complexation, and transport will be discussed in the context of solvent extraction chemistry. Notably, we show how the presence of both oil and aqueous phases provides an environment to drive fundamentally different phenomena from what is observed at model air/aqueous interfaces. This difference arises from key differences in solvation and bulk populations that feedback onto the surface to drive emergent and often functional molecular assemblies.

Doughty, Benjamin L↗
DOE OSTI2020

Tuning the critical Li intercalation concentrations for MoX 2 bilayer phase transitions using classical and machine learning approaches

Transition metal dichalcogenides (TMDs) such as MoX 2 are known to undergo a structural phase transformation as well as a change in the electronic conductivity upon Li intercalation. These properties make them candidates for charge tunable ion-insertion materials that could be used in electro-chemical devices for neuromorphic computing applications. In this work we study the phase stability and electronic structure of Li-intercalated bilayer MoX 2 with X=S, Se or Te. Using first-principles calculations in combination with classical and machine learning modeling approaches we find that the energy needed to stabilize the conductive phase decreases with increasing atomic mass of the chalcogen atom X. A similar decreasing trend is found in the threshold Li concentration where the structural phase transition takes place. While the electronic conductivity increases with increasing ion concentration at low concentrations, we do not observe a conductivity jump at the phase transition point.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Device Feasibility Analysis of Multi-level FeFETs for Neuromorphic Computing

As an emerging non-volatile memory device technology, Ferroelectric Field-Effect Transistors (FeFETs) can enable low-power, adaptive intelligent system design. However, device dimension and operating voltage dependent reliability issues of scaled FeFETs can ultimately lead to degraded performance in solving machine learning tasks. In this article, detailed experimental characterization of FeFET devices of different dimensions have been carried out to explicitly evaluate the non-ideal behavior in device conductance programming properties like number of programming states, cycle-to-cycle (C2C) variations, device-to-device (D2D) variations, and state retention. A hardware-aware software simulation approach has been adopted to capture the adversarial effects of the non-idealities on recognition accuracy through algorithm-level performance assessment by including them in NeuroSim, a popular neural network hardware simulator, to execute a neural network model considering all other hardware constraints. With the added non-idealities, significant accuracy degradation has been observed compared to the ideal scenarios where D2D variations play the most critical role. Thereafter, feasibility of a variation-aware training method has been evaluated to tackle the accuracy drop.

42 ENGINEERING↗
DOE OSTI2024

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Memristive Devices

Solid-state devices made from correlated oxides, such as perovskite nickelates, are promising for neuromorphic computing by mimicking biological synaptic function. However, comprehending dopant action at the nanoscale poses a formidable challenge to understanding the elementary mechanisms involved. Here, we perform operando infrared nanoimaging of hydrogen-doped correlated perovskite, neodymium nickel oxide (H-NdNiO 3 , H-NNO), devices and reveal how an applied field perturbs dopant distribution at the nanoscale. This perturbation leads to stripe phases of varying conductivity perpendicular to the applied field, which define the macroscale electrical characteristics of the devices. Hyperspectral nano-FTIR imaging in conjunction with density functional theory calculations unveils a real-space map of multiple vibrational states of H-NNO associated with OH stretching modes and their dependence on the dopant concentration. Moreover, the localization of excess charges induces an out-of-plane lattice expansion in NNO which was confirmed by in situ X-ray diffraction and creates a strain that acts as a barrier against further diffusion. Our results and the techniques presented here hold great potential for the rapidly growing field of memristors and neuromorphic devices wherein nanoscale ion motion is fundamentally responsible for function.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Spatiotemporal Thermal Coupling in VO 2 Device Arrays

Correlated oxides such as VO 2 exhibit an electrically driven insulator–metal transition (IMT) that underlies their promise for neuromorphic and memory devices. Yet the IMT is not a uniform bulk process but a spatiotemporal phenomenon in which local heating nucleates filaments, contracts or dissolves them with the electric field, and couples to the environment. In this work, we directly image the VO 2 IMT dynamics by mid-wave infrared, thermography synchronized with electrical transport, resolving device temperature with micrometer spatial and microsecond temporal resolution. At the single-device level, we capture the full cycle of filament nucleation, contraction, and relaxation during current/voltage-driven resistive switching. At the array level, we show that heat propagates across etched gaps with an effective length scale of ∼131 µm, enabling cooperative behaviors among electrically isolated devices. Short-range distanced devices exhibit mutual filament attraction and sequential dissolution, while long-range distanced devices differentiate into distinct roles: drivers that initiate switching, cooperative responders that undergo assisted self-oscillations, and passive reporters that record the thermal field. Furthermore, these results reframe thermal crosstalk, long regarded as parasitic, as an intrinsic coupling channel and design principle for organizing collective switching behaviors, with direct implications for emergent circuit functionality in neuromorphic and unconventional computing architectures.

coupling↗
DOE OSTI2023

The 2022 applied physics by pioneering women: a roadmap

Abstract Women have made significant contributions to applied physics research and development, and their participation is vital to continued progress. Recognizing these contributions is important for encouraging increased involvement and creating an equitable environment in which women can thrive. This Roadmap on Women in Applied Physics, written by women scientists and engineers, is intended to celebrate women’s accomplishments, highlight established and early career researchers enlarging the boundaries in their respective fields, and promote increased visibility for the impact women have on applied physics research. Perspectives cover the topics of plasma materials processing and propulsion, super-resolution microscopy, bioelectronics, spintronics, superconducting quantum interference device technology, quantum materials, 2D materials, catalysis and surface science, fuel cells, batteries, photovoltaics, neuromorphic computing and devices, nanophotonics and nanophononics, and nanomagnetism. Our intent is to inspire more women to enter these fields and encourage an atmosphere of inclusion within the scientific community.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
DOE OSTI2022

Controlled Formation of Conduction Channels in Memristive Devices Observed by X–ray Multimodal Imaging

Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software-based AI. The key to improving accuracy is to reduce the intrinsic randomness of memristive devices, emulating synapses in the brain for neuromorphic computing. Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X-ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. Furthermore, this work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing.

36 MATERIALS SCIENCE↗