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Oxygen-ion conductors have traditionally been studied in the context of high temperature (≈ 873 to 1773 K) energy conversion and sensor technologies. However, there is growing interest in exploring ion-based electronics for harsh environments (400 to 573 K) that represents an emerging field. Here, we utilize a blocking electrode to modify the interface properties of oxygen-ion conducting yttria-stabilized zirconia (YSZ) thin film electrochemical cells. The modified YSZ cell exhibits negative differential resistance (NDR) in the current-voltage curves at 543 K in the air. A double-sweep method and analysis of the scan-rate dependence of the

Both the magnitude and spectrum of the blackbody-radiation distribution change with temperature. Here, we designed the temperature-dependent spectral emissivity of a coating to counteract all the changes in the blackbody-radiation distribution over a certain temperature range, enabled by the nonhysteretic insulator-to-metal phase transition of SmNiO3. At each wavelength within the long-wave infrared atmospheric-transparency window, the thermal radiance of our coating remains nearly constant over a temperature range of at least 20 {\deg}C. Our approach can conceal thermal gradients and transient temperature changes from infrared imaging systems, including those that discriminate by wavelength, such as multispectral and hyperspectral cameras., Comment: Main text + supplementary

We theoretically and experimentally demonstrate a strong and tunable optical anisotropy in epitaxially-grown VO2 thin films. Using a combination of temperature-dependent X-ray diffraction, spectroscopic ellipsometry measurements and first-principle calculations, we reveal that these VO2 thin films present an ultra-large birefringence (Δn > 0.9). Furthermore, leveraging the insulator-to-metal transition of VO2, we demonstrate a dynamic reconfiguration of optical properties from birefringent to hyperbolic, which are two distinctive regimes of anisotropy. Such a naturally birefringent and dynamically switchable platform paves the way for multi-functional devices exploiting tunable anisotropy and hyperbolic dispersion.

Reconfigurable devices offer the ability to program electronic circuits on demand. In this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO3devices that can be simply reconfigured for a specific purpose by single-shot electric pulses. The sensitivity of electronic properties of perovskite nickelates to the local distribution of hydrogen ions enabled these results. With experimental data from our memory capacitors, simulation results of a reservoir computing framework showed excellent performance for tasks such as digit recognition and classification of electrocardiogram heartbeat activity. Using our reconfigurable artificial neurons and synapses, simulated dynamic networks outperformed static networks for incremental learning scenarios. The ability to fashion the building blocks of brain-inspired computers on demand opens up new directions in adaptive networks.

The cointegration of artificial neuronal and synaptic devices with homotypic materials and structures can greatly simplify the fabrication of neuromorphic hardware. We demonstrate experimental realization of vanadium dioxide (VO 2 ) artificial neurons and synapses on the same substrate through selective area carrier doping. By locally configuring pairs of catalytic and inert electrodes that enable nanoscale control over carrier density, volatility or nonvolatility can be appropriately assigned to each two-terminal Mott memory device per lithographic design, and both neuron- and synapse-like devices are successfully integrated on a single chip. Feedforward excitation and inhibition neural motifs are demonstrated at hardware level, followed by simulation of network-level handwritten digit and fashion product recognition tasks with experimental characteristics. Spatially selective electron doping opens up previously unidentified avenues for integration of emerging correlated semiconductors in electronic device technologies.

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.

A transient metal Vanadium dioxide is known to have a coupled structural and electronic transition that can be accessed through light, thermal, or electrical excitation. Ultrafast optical studies of this insulator-to-metal transition indicate that it is mediated by the formation of a transient metallic phase that retains the structure of the original insulating phase. Sood et al. show that a similar sequence occurs when the material is electrically excited with a series of voltage pulses. Using ultrafast electron diffraction, the researchers monitored the structure of a vanadium dioxide sample after excitation and found evidence of a metastable metallic phase that appears during the transition. Science , abc0652, this issue p. 352

Micro-robotics is a burgeoning field, with potential applications in search and rescue, smart dust, advanced additive manufacturing and micro-surgery. A critical component enabling micro-robotic systems is efficient actuator materials. In this work we introduce thin films of NdNiO3, a prototypical perovskite nickelate, as a potential material for MEMS actuation, providing up to 6.0% tensile volumetric strain via hydrogen-ion intercalation, resulting in an experimentally demonstrated output work density of 6.7 J/cm3 and stress output in excess of 1.1 GPa via a unique hydrogen doping mechanism. Separately, a stiffened cantilever array is fabricated to measure experimental tip deflection, which matches expected deflections from a developed finite element analysis model. [2020-0330]

Semiconductors with electrically tunable band gaps are of great interest in controlling transparency to electromagnetic radiation. Thin films of perovskite nickelate NdNiO3 (NNO), a class of correl...

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

The electric-field-assisted hydrogenation and corresponding resistance modulation of NdNiO3 (NNO) thin-film resistors were systematically studied as a function of temperature and dc electric bias. Catalytic Pt electrodes serve as triple-phase boundaries for hydrogen incorporation into a perovskite lattice. A kinetic model describing the relationship between resistance modulation and proton diffusion was proposed by considering the effect of the electric field during hydrogenation. An electric field, in addition to thermal activation, is demonstrated to effectively control the proton distribution along its gradient with an efficiency of ∼22% at 2 × 105 V/m. The combination of an electric field and gas-phase annealing is shown to enable the elegant control of the diffusional doping of complex oxides.

Glutamate, one of the main neurotransmitters in the brain, plays a critical role in communication between neurons, neuronal development, and various neurological disorders. Extracellular measurement of neurotransmitters such as glutamate in the brain is important for understanding these processes and developing a new generation of brain-machine interfaces. Here, we demonstrate the use of a perovskite nickelate-Nafion heterostructure as a promising glutamate sensor with a low detection limit of 16 nM and a response time of 1.2 s via amperometric sensing. We have designed and successfully tested novel perovskite nickelate-Nafion electrodes for recording of glutamate release ex vivo in electrically stimulated brain slices and in vivo from the primary visual cortex (V1) of awake mice exposed to visual stimuli. These results demonstrate the potential of perovskite nickelates as sensing media for brain-machine interfaces.

On-chip protection from electrostatic discharge (ESD) and electrical overstress (EOS) is a continuous challenge in the semiconductor industry, requiring significant design costs and die space. Insulator-metal transition (IMT) materials like vanadium dioxide (VO2) could be used as bidirectional, compact voltage snapback devices for ESD protection that are not required to be in the front-end silicon. However, the reliability and response of these materials to ESD is not yet well studied. Here, we perform transmission line pulse (TLP) tests on thin film (50– 150 nm) VO2 devices. These can repeatedly sustain high currents in their metallic state, but still return to insulating once an ESD event is over. Devices with widths from 5 to 50 $\mu \text{m}$ can carry a maximum current ( ${I} _{{\text {t}2}}$ ) from ~1 to over 10 A, equivalent to ~1 to 15 kV of ESD protection. Furthermore, the snapback voltage can be engineered by varying the device length. These results suggest that IMT materials could be promising for use in on-chip ESD/EOS protection.

We demonstrate spatial modification of the optical properties of thin-film metal oxides, zinc oxide and vanadium dioxide as representatives, using a commercial focused ion beam (FIB) system. Using a Ga+ FIB and thermal annealing, we demonstrated variable doping of a band semiconductor, zinc oxide (ZnO), achieving carrier concentrations from 10^18 cm-3 to 10^20 cm-3. Using the same FIB without subsequent thermal annealing, we defect-engineered a correlated semiconductor, vanadium dioxide (VO2), locally modifying its insulator-to-metal transition (IMT) temperature by range of ~25 degrees C. Such area-selective modification of metal oxides by direct writing using a FIB provides a simple, mask-less route to the fabrication of optical structures, especially when multiple or continuous levels of doping or defect density are required., Main text + supplementary. Updated manuscript with some more science

Using ion implantation, we engineered tunable thresholds of optical switches that incorporated frequency-selective surfaces (FSSs) with thin-film VO2. We also used a focused ion beam to selectively control any passband of an FSS-VO2 optical switch.

Polaritons enable subwavelength confinement and highly anisotropic flows of light over a wide spectral range, holding the promise for applications in modern nanophotonic and optoelectronic devices. However, to fully realize their practical application potential, facile methods enabling nanoscale active control of polaritons are needed. Here, we introduce a hybrid polaritonic-oxide heterostructure platform consisting of van der Waals crystals, such as hexagonal boron nitride (hBN) or alpha-phase molybdenum trioxide (α-MoO3), transferred on nanoscale oxygen vacancy patterns on the surface of prototypical correlated perovskite oxide SmNiO3 (SNO). Using a combination of scanning probe microscopy and infrared nanoimaging techniques, we demonstrate nanoscale real-time reconfigurability of complex hyperbolic phonon polaritons patterned at the nanoscale with unmatched resolution. Hydrogenation and temperature modulation allow spatially localized conductivity modulation of SNO nanoscale patterns, enabling robust real-time modulation and nanoscale reconfiguration of hyperbolic polaritons. Our work paves the way towards nanoscale programmable metasurface engineering as a new paradigm for reconfigurable nanophotonic applications facilitated by a hybrid material platform exploiting extreme light-matter interactions in polaritonic systems.

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.

Nano-membrane tri-gate