Neuromorphic one-shot learning utilizing a phase-transition material.

Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.

Subnanometer Scale Mapping of Hydrogen Doping in Vanadium Dioxide.

Hydrogen donor doping of correlated electron systems such as vanadium dioxide (VO2) profoundly modifies the ground state properties. The electrical behavior of HxVO2 is strongly dependent on the hydrogen concentration; hence, atomic scale control of the doping process is necessary. It is however a nontrivial problem to quantitatively probe the hydrogen distribution in a solid matrix. As hydrogen transfers its sole electron to the material, the ionization mechanism is suppressed. In this study, a methodology mapping the doping distribution at subnanometer length scale is demonstrated across a HxVO2 thin film focusing on the oxygen-hydrogen bonds using electron energy loss spectroscopy (EELS) coupled with first-principles EELS calculations. The hydrogen distribution was revealed to be nonuniform along the growth direction and between different VO2 grains, calling for intricate hydrogenation mechanisms. Our study points to a powerful approach to quantitatively map dopant distribution in quantum materials relevant to energy and information sciences.

Phase-Field Model of Electronic Antidoping.

Charge carrier doping usually reduces the resistance of a semiconductor or insulator, but was recently found to dramatically enhance the resistance in certain series of materials. This remarkable antidoping effect has been leveraged to realize synaptic memory trees in nanoscale hydrogenated perovskite nickelates, opening a new direction for neuromorphic computing. To understand these phenomena, we formulate a physical phase-field model of the antidoping effect based on its microscopic mechanism and simulate the voltage-driven resistance change in the prototypical system of hydrogenated perovskite nickelates. Remarkably, the simulations using this model, containing only one adjustable parameter whose magnitude is justified by first-principles calculations, quantitatively reproduce the experimentally observed treelike resistance states, which are shown unambiguously to arise from proton redistribution-induced local band gap enhancement and carrier blockage. Our work lays the foundation for modeling the antidoping phenomenon in strongly correlated materials at the mesoscale, which can provide guidance to the design of novel antidoping-physics-based devices.

Perovskite nickelates as electric-field sensors in salt water.

Designing materials to function in harsh environments, such as conductive aqueous media, is a problem of broad interest to a range of technologies, including energy, ocean monitoring and biological applications. The main challenge is to retain the stability and morphology of the material as it interacts dynamically with the surrounding environment. Materials that respond to mild stimuli through collective phase transitions and amplify signals could open up new avenues for sensing. Here we present the discovery of an electric-field-driven, water-mediated reversible phase change in a perovskite-structured nickelate, SmNiO3. This prototypical strongly correlated quantum material is stable in salt water, does not corrode, and allows exchange of protons with the surrounding water at ambient temperature, with the concurrent modification in electrical resistance and optical properties being capable of multi-modal readout. Besides operating both as thermistors and pH sensors, devices made of this material can detect sub-volt electric potentials in salt water. We postulate that such devices could be used in oceanic environments for monitoring electrical signals from various maritime vessels and sea creatures.

Low-temperature emergent neuromorphic networks with correlated oxide devices.

Neuromorphic computing-which aims to mimic the collective and emergent behavior of the brain's neurons, synapses, axons, and dendrites-offers an intriguing, potentially disruptive solution to society's ever-growing computational needs. Although much progress has been made in designing circuit elements that mimic the behavior of neurons and synapses, challenges remain in designing networks of elements that feature a collective response behavior. We present simulations of networks of circuits and devices based on superconducting and Mott-insulating oxides that display a multiplicity of emergent states that depend on the spatial configuration of the network. Our proposed network designs are based on experimentally known ways of tuning the properties of these oxides using light ions. We show how neuronal and synaptic behavior can be achieved with arrays of superconducting Josephson junction loops, all within the same device. We also show how a multiplicity of synaptic states could be achieved by designing arrays of devices based on hydrogenated rare earth nickelates. Together, our results demonstrate a research platform that utilizes the collective macroscopic properties of quantum materials to mimic the emergent behavior found in biological systems.

Neuromorphic learning with Mott insulator NiO.

Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. Here, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia, habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability-plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.

Spatial Interactions in Hydrogenated Perovskite Nickelate Synaptic Networks.

A key aspect of how the brain learns and enables decision-making processes is through synaptic interactions. Electrical transmission and communication in a network of synapses are modulated by extracellular fields generated by ionic chemical gradients. Emulating such spatial interactions in synthetic networks can be of potential use for neuromorphic learning and the hardware implementation of artificial intelligence. Here, we demonstrate that in a network of hydrogen-doped perovskite nickelate devices, electric bias across a single junction can tune the coupling strength between the neighboring cells. Electrical transport measurements and spatially resolved diffraction and nanoprobe X-ray and scanning microwave impedance spectroscopic studies suggest that graded proton distribution in the inhomogeneous medium of hydrogen-doped nickelate film enables this behavior. We further demonstrate signal integration through the coupling of various junctions.

Efficient Probabilistic Computing with Stochastic Perovskite Nickelates.

Probabilistic computing has emerged as a viable approach to solve hard optimization problems. Devices with inherent stochasticity can greatly simplify their implementation in electronic hardware. Here, we demonstrate intrinsic stochastic resistance switching controlled via electric fields in perovskite nickelates doped with hydrogen. The ability of hydrogen ions to reside in various metastable configurations in the lattice leads to a distribution of transport gaps. With experimentally characterized p-bits, a shared-synapse p-bit architecture demonstrates highly parallelized and energy-efficient solutions to optimization problems such as integer factorization and Boolean satisfiability. The results introduce perovskite nickelates as scalable potential candidates for probabilistic computing and showcase the potential of light-element dopants in next-generation correlated semiconductors.

Strongly correlated perovskite fuel cells.

Fuel cells convert chemical energy directly into electrical energy with high efficiencies and environmental benefits, as compared with traditional heat engines. Yttria-stabilized zirconia is perhaps the material with the most potential as an electrolyte in solid oxide fuel cells (SOFCs), owing to its stability and near-unity ionic transference number. Although there exist materials with superior ionic conductivity, they are often limited by their ability to suppress electronic leakage when exposed to the reducing environment at the fuel interface. Such electronic leakage reduces fuel cell power output and the associated chemo-mechanical stresses can also lead to catastrophic fracture of electrolyte membranes. Here we depart from traditional electrolyte design that relies on cation substitution to sustain ionic conduction. Instead, we use a perovskite nickelate as an electrolyte with high initial ionic and electronic conductivity. Since many such oxides are also correlated electron systems, we can suppress the electronic conduction through a filling-controlled Mott transition induced by spontaneous hydrogen incorporation. Using such a nickelate as the electrolyte in free-standing membrane geometry, we demonstrate a low-temperature micro-fabricated SOFC with high performance. The ionic conductivity of the nickelate perovskite is comparable to the best-performing solid electrolytes in the same temperature range, with a very low activation energy. The results present a design strategy for high-performance materials exhibiting emergent properties arising from strong electron correlations.

Temperature-independent thermal radiation.

Thermal emission is the process by which all objects at nonzero temperatures emit light and is well described by the Planck, Kirchhoff, and Stefan-Boltzmann laws. For most solids, the thermally emitted power increases monotonically with temperature in a one-to-one relationship that enables applications such as infrared imaging and noncontact thermometry. Here, we demonstrated ultrathin thermal emitters that violate this one-to-one relationship via the use of samarium nickel oxide (SmNiO3), a strongly correlated quantum material that undergoes a fully reversible, temperature-driven solid-state phase transition. The smooth and hysteresis-free nature of this unique insulator-to-metal phase transition enabled us to engineer the temperature dependence of emissivity to precisely cancel out the intrinsic blackbody profile described by the Stefan-Boltzmann law, for both heating and cooling. Our design results in temperature-independent thermally emitted power within the long-wave atmospheric transparency window (wavelengths of 8 to 14 µm), across a broad temperature range of ∼30 °C, centered around ∼120 °C. The ability to decouple temperature and thermal emission opens a gateway for controlling the visibility of objects to infrared cameras and, more broadly, opportunities for quantum materials in controlling heat transfer.

Carrier localization in perovskite nickelates from oxygen vacancies.

Point defects, such as oxygen vacancies, control the physical properties of complex oxides, relevant in active areas of research from superconductivity to resistive memory to catalysis. In most oxide semiconductors, electrons that are associated with oxygen vacancies occupy the conduction band, leading to an increase in the electrical conductivity. Here we demonstrate, in contrast, that in the correlated-electron perovskite rare-earth nickelates, RNiO3 (R is a rare-earth element such as Sm or Nd), electrons associated with oxygen vacancies strongly localize, leading to a dramatic decrease in the electrical conductivity by several orders of magnitude. This unusual behavior is found to stem from the combination of crystal field splitting and filling-controlled Mott-Hubbard electron-electron correlations in the Ni 3d orbitals. Furthermore, we show the distribution of oxygen vacancies in NdNiO3 can be controlled via an electric field, leading to analog resistance switching behavior. This study demonstrates the potential of nickelates as testbeds to better understand emergent physics in oxide heterostructures as well as candidate systems in the emerging fields of artificial intelligence.

Sudden Collapse of Magnetic Order in Oxygen-Deficient Nickelate Films.

Antiferromagnetic order is a common and robust ground state in the parent (undoped) phase of several strongly correlated electron systems. The progressive weakening of antiferromagnetic correlations upon doping paves the way for a variety of emergent many-electron phenomena including unconventional superconductivity, colossal magnetoresistance, and collective charge-spin-orbital ordering. In this study, we explored the use of oxygen stoichiometry as an alternative pathway to modify the coupled magnetic and electronic ground state in the family of rare earth nickelates (RENiO_{3-x}). Using a combination of x-ray spectroscopy and resonant soft x-ray magnetic scattering, we find that, while oxygen vacancies rapidly alter the electronic configuration within the Ni and O orbital manifolds, antiferromagnetic order is remarkably robust to substantial levels of carrier doping, only to suddenly collapse beyond 0.21 e^{-}/Ni without an accompanying structural transition. Our work demonstrates that ordered magnetism in RENiO_{3-x} is mostly insensitive to carrier doping up to significant levels unseen in other transition-metal oxides. The sudden collapse of ordered magnetism upon oxygen removal may provide a new mechanism for solid-state magnetoionic switching and new applications in antiferromagnetic spintronics.

First demonstration of robust tri-gateß-Ga2O3nano-membrane field-effect transistors.

Nano-membrane tri-gateß-gallium oxide (ß-Ga2O3) field-effect transistors (FETs) on SiO2/Si substrate fabricated via exfoliation have been demonstrated for the first time. By employing electron beam lithography, the minimum-sized features can be defined with the footprint channel width of 50 nm. For high-quality interface betweenß-Ga2O3and gate dielectric, atomic layer-deposited 15 nm thick aluminum oxide (Al2O3) was utilized with tri-methyl-aluminum (TMA) self-cleaning surface treatment. The fabricated devices demonstrate extremely low subthreshold slope (SS) of 61 mV dec-1, high drain current (IDS) ON/OFF ratio of 1.5 × 109, and negligible transfer characteristic hysteresis. We also experimentally demonstrated robustness of these devices with current-voltage (I-V) characteristics measured at temperatures up to 400 °C.

Strongly correlated perovskite lithium ion shuttles.

Solid-state ion shuttles are of broad interest in electrochemical devices, nonvolatile memory, neuromorphic computing, and biomimicry utilizing synthetic membranes. Traditional design approaches are primarily based on substitutional doping of dissimilar valent cations in a solid lattice, which has inherent limits on dopant concentration and thereby ionic conductivity. Here, we demonstrate perovskite nickelates as Li-ion shuttles with simultaneous suppression of electronic transport via Mott transition. Electrochemically lithiated SmNiO3 (Li-SNO) contains a large amount of mobile Li+ located in interstitial sites of the perovskite approaching one dopant ion per unit cell. A significant lattice expansion associated with interstitial doping allows for fast Li+ conduction with reduced activation energy. We further present a generalization of this approach with results on other rare-earth perovskite nickelates as well as dopants such as Na+ The results highlight the potential of quantum materials and emergent physics in design of ion conductors.

Active Optical Metasurfaces Based on Defect-Engineered Phase-Transition Materials.

Active, widely tunable optical materials have enabled rapid advances in photonics and optoelectronics, especially in the emerging field of meta-devices. Here, we demonstrate that spatially selective defect engineering on the nanometer scale can transform phase-transition materials into optical metasurfaces. Using ion irradiation through nanometer-scale masks, we selectively defect-engineered the insulator-metal transition of vanadium dioxide, a prototypical correlated phase-transition material whose optical properties change dramatically depending on its state. Using this robust technique, we demonstrated several optical metasurfaces, including tunable absorbers with artificially induced phase coexistence and tunable polarizers based on thermally triggered dichroism. Spatially selective nanoscale defect engineering represents a new paradigm for active photonic structures and devices.

Control of emergent properties at a correlated oxide interface with graphene.

Electrolyte gating of complex oxides enables investigation of electronic phase boundaries and collective response to strong electric fields. The origin of large conductance modulations and associated emergent properties in such field effect structures is a matter of intense study due to competing contributions from electrostatic (charge accumulation) and electrochemical (crystal chemistry changes) effects. Vanadium dioxide (VO2) is a prototypical correlated insulator that shows an insulator-to-metal transition at ∼67 °C and recent studies have noted a vast range of electronic effects in electric double-layer transistors (EDLT). In this study, we demonstrate that the response of electrolyte gated VO2 devices can be deterministically controlled by inserting a monolayer of graphene at the oxide-electrolyte interface. Several electrolytes as well as dopants (such as lithium ions and protons) were employed in EDL transistors to show that graphene serves as an inert barrier that successfully protects the oxide surface from chemical reactions. This monolayer interface has a striking effect on resistance modulation in the vanadium dioxide transistor channel up to several orders of magnitude and enables retention of the insulating phase. The studies allow new insights into the response of correlated insulators in EDLTs and inform design of correlated oxide-2D heterostructures for electronics and sensors.

Origin of discrete resistive switching in chemically heterogeneous vanadium oxide crystals.

Phase changes in oxide materials such as VO2 offer a foundational platform for designing novel solid-state devices. Tuning the V : O stoichiometry offers a vast electronic phase space with non-trivial collective properties. Here, we report the observation of discrete threshold switching voltages (Vth) with constant ΔVth between cycles in vanadium oxide crystals. The observed threshold fields over 10 000 cycles are ∼100× lower than that noted for stoichiometric VO2 and show unique discrete behaviour with constant ΔVth. We correlate the observed discrete memristor behaviour with the valence change mechanism and fluctuations in the chemical composition of spatially distributed VO2-VnO2n-1 complex oxide phases that can synergistically co-operate with the insulator-metal transition resulting in sharp current jumps. The design of chemical heterogeneity in oxide crystals, therefore, offers an intriguing path to realizing low-energy neuromorphic devices.

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-NdNiO3, 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.