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Results for “neuromorphic devices”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Selective modulation of electronic transport in VO 2 induced by 10 keV helium ion irradiation

Vanadium dioxide (VO 2 ) manifests an abrupt metal–insulator transition (MIT) from monoclinic to rutile phases, with potential use for tunable electronic and optical properties and spiking neuromorphic devices. Understanding pathways to modulate electronic transport in VO 2 , as well as its response to irradiation (e.g., for space applications), is critical to better enable these applications. In this work, we investigate the selective modulation of electronic transport in VO 2 films subject to different 10 keV helium ion (He + ) fluences. Under these conditions, the resistivity in the individual monoclinic and rutile phases varied by 50%–200%, while the MIT transformation temperature remains constant within 4 °C independent of irradiation fluence. Importantly, different trends in the resistivity of the monoclinic and rutile phases were observed both as a function of total He fluence as well as in films grown on different substrates (amorphous SiO 2 /Si vs single crystal Al 2 O 3 ). Through a combination of measurements including majority carrier sign via Seebeck, low frequency noise, and TEM, our investigation supports the presence of different kinds of point defects (V in; O in), which may arise due to grain boundary defect interactions. Our work suggests the utility of He irradiation for the selective modulation of VO 2 transport properties for neuromorphic, in contrast to other established but non-selective methods, like doping.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tunable Microwave Conductance of Nanodomains in Ferroelectric PbZr 0.2 Ti 0.8 O 3 Thin Film

Ferroelectric materials exhibit spontaneous polarization that can be switched by electric field. Beyond traditional applications as nonvolatile capacitive elements, the interplay between polarization and electronic transport in ferroelectric thin films has enabled a path to neuromorphic device applications involving resistive switching. A fundamental challenge, however, is that finite electronic conductivity may introduce considerable power dissipation and perhaps destabilize ferroelectricity itself. In this work, tunable microwave frequency electronic response of domain walls injected into ferroelectric lead zirconate titanate (PbZr 0.2 Ti 0.8 O 3 ) on the level of a single nanodomain is revealed. Tunable microwave response is detected through first-order reversal curve spectroscopy combined with scanning microwave impedance microscopy measurements taken near 3 GHz. Contributions of film interfaces to the measured AC conduction through subtractive milling, where the film exhibited improved conduction properties after removal of surface layers, are investigated. Using statistical analysis and finite element modeling, we inferred that the mechanism of tunable microwave conductance is the variable area of the domain wall in the switching volume. These observations open the possibilities for ferroelectric memristors or volatile resistive switches, localized to several tens of nanometers and operating according to well-defined dynamics under an applied field.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Graph Dynamical neural network approach for decoding dynamical states in ferroelectrics.

Ferroelectric materials such as BaTiO 3 show tremendous potential for emerging advances in memory devices, particular neuromorphic type devices. High density of memory can be obtained by stabilising polar domain walls at the nanoscale, regions of discontinuity between the well-defined polarization order parameter, but little is known about what controls their structure and dynamics in real nanoscale materials. Indeed, chiral polar domain walls have been observed in heterogeneous ferroelectrics, such as oxygen-deficient BaTiO 3 , but very little is known about how such polar-domains walls interact with defects. Indeed, a critical understanding of how dynamics of domain-walls depend on point-defects is crucial to create engineered ferroelectric memory devices. For this work, we perform large-scale simulations of nansocale domain-wall dynamics in pristine and defective BaTiO 3 using reactive force-field developed by us earlier (Phys. Chem. Chem. Phys., 2019, 21, 18240–18249), and capture their dynamical dependence on point defects using a graph dynamical neural-network approach, which we adapted to interrogate solids with well-defined order-parameters, and implemented using Pytorch based libraries. Our machine learning (ML) approach goes beyond the traditional post-processing methods to capture both spatial and temporal heterogeneities of large-scale molecular dynamics simulations of complex defective ferroelectric oxide materials. We crucially find that isolated oxygen vacancies introduce very localized spatial regions (~1–2 unit-cell in length) that show slow dipole relaxation due to formation of defect-dipoles, and that these defect-dipoles in turn slow the intrinsic dynamics of domain walls. Further, the roughness of domain walls, also influenced by vacancies, introduce dynamic heterogeneity along the domain-wall. As such we find a novel mechanism by which quenched disorder due to defects introduce dynamic heterogeneity thereby influencing response to external fields (particularly time varying fields) in a ferroelectric. Our study also emphasizes the need for creating digital twins of dynamical quantities to achieve autonomous in operando control of nanoscale switching.

42 ENGINEERING↗
DOE OSTI2022

Determining the Oxygen Stoichiometry of Cobaltite Thin Films

Transition metal oxides (TMO) are promising materials to realize low-power neuromorphic devices. Their physical properties critically depend on their oxygen vacancy concentrations, whose experimental determination remains a challenging task. Here we focus on cobaltites, in particular La 1-x Sr x CoO 3-d (LSCO), and we present a strategy to identify fingerprints of oxygen vacancies in X-ray absorption (XA) spectra. Using a combination of experiment and theory, we show that the variation of the oxygen vacancy concentration in the perovskite phase of LSCO is correlated with the change of the relative peak positions of the O K-edge XA spectra. Furthermore, we also identify an additional geometrical fingerprint that captures both the changes of the Co-O bond length and Co-O-Co bond angle in the material due to the presence of oxygen vacancies. Finally, we predict the oxygen vacancy concentration of experimental samples and show how the resistivity of the oxide material may be tuned as a function of the defect concentration present in the system.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Self-Driving Laboratory for Polymer Electronics

Owing to the chemical pluripotency and viscoelastic nature of electronic polymers, polymer electronics have shown unique advances in many emerging applications such as skin-like electronics, large-area printed energy devices, and neuromorphic computing devices, but their development period is years-long. Recent advancements in automation, robotics, and learning algorithms have led to a growing number of self-driving (autonomous) laboratories that have begun to revolutionize the development and accelerated discovery of materials. In this perspective, we first introduce the current state of autonomous laboratories. Then we analyze why it is challenging to conduct polymer electronics research by an autonomous laboratory and highlight the needs. Here, we further discuss our efforts in building an autonomous laboratory, namely Polybot, for the automated synthesis and characterization of electronic polymers and their processing and fabrication into electronic devices. Finally, we share our vision in using a self-driving laboratory for different types of polymer electronics research.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Correlation control of the Mott transition in LaTiO 3 /SrTiO 3 heterostructures

The Mott metal-insulator transition arises from electron-electron interactions determined by the ratio of Coulomb to kinetic energy scales (U/t). While temperature, pressure, and doping can induce Mott transitions, direct control of U in solid-state systems remains largely unexplored, particularly in the inhomogeneous environments of emerging neuromorphic devices, where conductive filaments create complex gradients of local properties. Here we show that interface-induced screening can continuously tune the electron-electron interaction strength U to drive an isothermal Mott transition. Using thickness-graded LaTiO3/SrTiO3 heterostructures, we used diffraction and photoemission spectroscopy to reveal a continuous, isothermal quantum phase transition from a Fermi liquid quasiparticle with incoherent excitations to a Mott insulator with Hubbard bands. The primary determinant of the transition is the enhanced local screening environment, which directly influences the interaction strength U, driving the system metallic. This demonstrates that interface engineering of the local screening environment provides a promising approach to manipulate Mott physics through correlation control, beyond traditional bandwidth or filling approaches.

Electronic and spintronic devices↗
DOE OSTI2020

Tuning electronic properties in LaNiO 3 thin films by B-site Cu-substitution

Resistors are essential parts of futuristic all-oxide electronic architectures, yet easily overlooked due to their apparent simplicity. In this study, design of thin films with specific resistance spanning six orders of magnitude by partial substitution of Cu 2+ for Ni 3+ in the metallic conductor LaNiO 3 is shown. Substitution is attainable across the whole composition range using atomic layer deposition on LaAlO 3 (100) pc substrates, however with some inclusion of Ruddlesden–Popper RP1 phase (La 2 CuO 4 ) at high levels of Cu-incorporation. The thermal stability of the resistance is based on a metal–insulator transition that evolves from non-existent for LaNiO 3 to above room-temperature for high Cu 2+ substitution. This provides further insight in the metal–insulator transition found for correlated rare-earth nickelates, especially the type of transition seen for ultrathin films of LaNiO 3 that is usually attributed to oxygen vacancy formation. Materials with variable resistivity and metal–insulator-transition temperatures are key in the design of futuristic electronics such as metal–insulator-transition-driven neuromorphic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

In situ synchrotron x-ray studies of epitaxial SrCoO x films during ionic liquid gating

The manipulation of ions in complex oxide materials can be used to mimic brain-like plasticity through changes to the resistivity of a neuromorphic device. Advances in the design of more energy efficient devices require improved understanding of how ions migrate within a material and across its interface. We investigate the exchange of oxygen and hydrogen in a model SrCoO x epitaxial film—a material that transitions between a ferromagnetic metal and antiferromagnetic insulator depending on the oxygen concentration. Changes to the film during ionic liquid gating were measured by in situ synchrotron x-ray techniques as a function of time and gate voltage, examining the reversibility of the oxide over one complete gating cycle. We find that the out-of-plane lattice constant and oxygen vacancy concentration of SrCoO x are largely reversible although changes were observed in the ordered vacancy structure. Our results provide much needed insight into electrolyte-gated phase behavior in the transition metal oxides.

Li, Yan [Argonne National Laboratory (ANL), Argonn↗
DOE OSTI2024

Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks

Abstract Networks composed of nanoscale memristive components, such as nanowire and nanoparticle networks, have recently received considerable attention because of their potential use as neuromorphic devices. In this study, we explore ergodicity in memristive networks, showing that the performance on machine leaning tasks improves when these networks are tuned to operate at the edge between two global stability points. We find this lack of ergodicity is associated with the emergence of memory in the system. We measure the level of ergodicity using the Thirumalai-Mountain metric, and we show that in the absence of ergodicity, two different memristive network systems show improved performance when utilized as reservoir computers (RC). We highlight that it is also important to let the system synchronize to the input signal in order for the performance of the RC to exhibit improvements over the baseline.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

AI-Powered Knowledge Graphs for Neuromorphic and Energy-Efficient Computing

The surge in scientific literature obscures breakthroughs and hinders the discovery of new research paths. We propose an artificial intelligence (AI) powered framework using large language models (LLMs) and knowledge graphs (KGs) to automate parts of scientific discovery, focusing on energy-efficient AI circuits. Our hybrid approach combines LLMs, structured data, and ontology-based reasoning to construct a comprehensive knowledge graph that integrates insights across computational neuroscience, spiking neuron models, learning rules, architectural motifs, and neuromorphic device technologies. This multi-domain representation enables the generation of hypotheses that connect biological function with implementable, energy-efficient hardware architectures. Using KG embeddings and graph neural networks, the framework generates hypotheses for novel circuits, validates them through optimization on exascale HPC systems, and with tools like SuperNeuro and Fugu, the most promising designs will be prototyped in hardware. This open-source system aims to accelerate discoveries and bridging neuroscience with hardware innovation, drive collaboration, and unlock new opportunities in low-power AI computing.

Gautam, Ashish [ORNL]↗
DOE OSTI2024

Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks and nanowire

Networks composed of nanoscale memristive components, such as nanowire and nanoparticle networks, have recently received considerable attention because of their potential use as neuromorphic devices. In this study, we explore ergodicity in memristive networks, showing that the performance on machine leaning tasks improves when these networks are tuned to operate at the edge between two global stability points. We find this lack of ergodicity is associated with the emergence of memory in the system. We measure the level of ergodicity using the Thirumalai-Mountain metric, and we show that in the absence of ergodicity, two different memristive network systems show improved performance when utilized as reservoir computers (RC). We highlight that it is also important to let the system synchronize to the input signal in order for the performance of the RC to exhibit improvements over the baseline.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Heat Transport Hysteresis Generated Through Frequency Switching of a Time-Dependent Temperature Gradient

A stochastic energetics framework is applied to examine how periodically shifting the frequency of a time-dependent oscillating temperature gradient affects heat transport in a nanoscale molecular model. We specifically examine the effects that frequency switching, i.e., instantaneously changing the oscillation frequency of the temperature gradient, has on the shape of the heat transport hysteresis curves generated by a particle connected to two thermal baths, each with a temperature that is oscillating in time. Analytical expressions are derived for the energy fluxes in/out of the system and the baths, with excellent agreement observed between the analytical expressions and the results from nonequilibrium molecular dynamics simulations. We find that the shape of the heat transport hysteresis curves can be significantly altered by shifting the frequency between fast and slow oscillation regimes. We also observe the emergence of features in the hysteresis curves such as pinched loops and complex multi-loop patterns due to the frequency shifting. The presented results have implications in the design of thermal neuromorphic devices such as thermal memristors and thermal memcapacitors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Low-temperature emergent neuromorphic networks with correlated oxide devices

Significance Designing neuromorphic hardware for cryoelectronics is an important area of research as the field of computing paradigms beyond complementary metal-oxide-semiconductor (CMOS) progresses. Superconductivity and metal−insulator transitions are two of the most celebrated emergent, collective properties found in quantum materials such as strongly correlated oxides. Here, we present simulations of artificial neural networks that can be designed by combining superconducting devices (e.g. Josephson junctions) with Mott metal−insulator transition−based tunable resistor devices. Our simulations show that 1) neurons and synapses can be seamlessly created, 2) their functions can be tuned via learning, and 3) controlling disorder by incorporating light ions enables exponential multiplicity of states. The results open up directions for incorporating emergent behavior seen in condensed matter into hardware design for artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Neuromorphic Photonics Based on Phase Change Materials

Neuromorphic photonics devices based on phase change materials (PCMs) and silicon photonics technology have emerged as promising solutions for addressing the limitations of traditional spiking neural networks in terms of scalability, response delay, and energy consumption. In this review, we provide a comprehensive analysis of various PCMs used in neuromorphic devices, comparing their optical properties and discussing their applications. We explore materials such as GST (Ge 2 Sb 2 Te 5 ), GeTe-Sb 2 Te 3 , GSST (Ge 2 Sb 2 Se 4 Te 1 ), Sb 2 S 3 /Sb 2 Se 3 , Sc 0.2 Sb 2 Te 3 (SST), and In 2 Se 3 , highlighting their advantages and challenges in terms of erasure power consumption, response rate, material lifetime, and on-chip insertion loss. By investigating the integration of different PCMs with silicon-based optoelectronics, this review aims to identify potential breakthroughs in computational performance and scalability of photonic spiking neural networks. Further research and development are essential to optimize these materials and overcome their limitations, paving the way for more efficient and high-performance photonic neuromorphic devices in artificial intelligence and high-performance computing applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An Interface–Type Memristive Device for Artificial Synapse and Neuromorphic Computing

Interface-type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament-type (FT) devices for neuromorphic computing because of their uniform, filament-free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface-dominated memristive device is demonstrated using a simple Au/Nb-doped SrTiO 3 (Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface-controlled RS, the proposed device shows low device-to-device, cell-to-cell, and cycle-to-cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next-generation neuromorphic computing.

36 MATERIALS SCIENCE↗
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 OSTI2022

Structural and Dynamic Disorder, Not Ionic Trapping, Controls Charge Transport in Highly Doped Conducting Polymers

Doped organic semiconductors are critical to emerging device applications, including thermoelectrics, bioelectronics, and neuromorphic computing devices. It is commonly assumed that low conductivities in these materials result primarily from charge trapping by the Coulomb potentials of the dopant counter-ions. Here, we present a combined experimental and theoretical study rebutting this belief. Using a newly developed doping technique, we find the conductivity of several classes of high-mobility conjugated polymers to be strongly correlated with paracrystalline disorder but poorly correlated with ionic size, suggesting that Coulomb traps do not limit transport. A general model for interacting electrons in highly doped polymers is proposed and carefully parameterized against atomistic calculations, enabling the calculation of electrical conductivity within the framework of transient localisation theory. Theoretical calculations are in excellent agreement with experimental data, providing insights into the disordered-limited nature of charge transport and suggesting new strategies to further improve conductivities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗