Perovskite neural trees

Proton Conducting Neuromorphic Materials and Devices

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

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.

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.

Complex Oxides for Brain-Inspired Computing: A Review

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.

Perspective and Prospects for Ordered Functional Materials

Many functional materials are approaching their performance limits due to inherent trade-offs between essential physical properties. Such trade-offs can be overcome by engineering a material that has an ordered arrangement of structural units, including constituent components/phases, grains, and domains. By rationally manipulating the ordering with abundant structural units at multiple length scales, the structural ordering opens up unprecedented opportunities to create transformative functional materials, as amplified properties or disruptive functionalities can be realized. In this perspective article, a brief overview of recent advances in the emerging ordered functional materials across catalytic, thermoelectric, and magnetic materials regarding the fabrication, structure, and property is presented. Then the possibility of applying this structural ordering strategy to highly efficient neuromorphic computing devices and durable battery materials is discussed. Finally, remaining scientific challenges are highlighted, and the prospects for ordered functional materials are made. This perspective aims to draw the attention of the scientific community to the emerging ordered functional materials and trigger intense studies on this topic.

Dual-gate manipulation of a HfZrOx-based MoS2 field-effect transistor towards enhanced neural network applications

Artificial neural networks (ANNs) have strong learning and computing capabilities, and alleviate the problem of high power consumption of traditional von Neumann architectures, providing a solid basis for advanced image recognition, information processing, and low-power detection. Recently, a two-dimensional (2D) MoS₂ field-effect transistor (FET) integrating a Zr-doped HfO₂ (HZO) ferroelectric layer has shown potential for both logic and memory applications with low power consumption, which is promising for parallel processing of massive data. However, the long-term potentiation (LTP) characteristics of such devices are usually non-linear, which will affect the replacement of ANN weight values and degrade the ANN recognition rate. Here, we propose a dual-gate-controlled 2D MoS₂ FET employing HZO gate stack with a crested symmetric structure to reduce power consumption. Improved nonlinearity of the LTP properties has been achieved through the electrical control of the dual gates. A recognition rate reaching 100% is obtained after 60 training epochs, and is 7.89% higher than that obtained from single-gate devices. Our proposed device structure and experimental results provide an attractive pathway towards high-efficiency data processing and image classification in the advanced artificial intelligence field.

Revealing the Role of Hydrogen in Electron-Doping Mottronics for Strongly Correlated Vanadium Dioxide

Hydrogen-associated electron-doping Mottronics for d-band correlated oxides (e.g., VO₂) opens up a new paradigm to regulate the electronic functionality via directly manipulating the orbital configuration and occupancy. Nevertheless, the role of hydrogen in the Mottronic transition of VO₂ is yet unclear because opposite orbital reconfigurations toward either the metallic or highly insulating states were both reported. Herein, we demonstrate the root cause for such hydrogen-induced multiple electronic phase transitions by ¹H quantification using nuclear reaction analysis. A low hydrogenation temperature is demonstrated to be vital in achieving a large hydrogen concentration (n_H ≈ 10²² cm^-3) that further enhances the t_2g orbital occupancy to trigger electron localizations. In contrast, elevating the hydrogenation temperatures surprisingly reduces n_H to ∼10²¹ cm^-3 but forms more stable metallic H_0.06VO₂. This leads to the recognition of a weaker hydrogen interaction that triggers electron localization within VO₂ via Mottronically enhancing the orbital occupancies.

On-Chip Integrated Atomically Thin 2D Material Heater as a Training Accelerator for an Electrochemical Random-Access Memory Synapse for Neuromorphic Computing Application

An artificial synapse based on oxygen-ion-driven electrochemical random-access memory (O-ECRAM) devices is a promising candidate for building neural networks embodied in neuromorphic hardware. However, achieving commercial-level learning accuracy in O-ECRAM synapses, analog conductance tuning at fast speed, and multibit storage capacity is challenging because of the lack of Joule heating, which restricts O^2- ionic transport. Here, we propose the use of an atomically thin heater of monolayer graphene as a low-power heating source for O-ECRAM to increase thermally activated O^2- migration within channel-electrolyte layers. Heating from graphene manipulates the electrolyte activation energy to establish and maintain discrete analog states in the O-ECRAM channel. Benefiting from the integrated graphene heater, the O-ECRAM features long retention (>10⁴ s), good stability (switching accuracy <98% for >10³ training pulses), multilevel analog states for 6-bit analog weight storage with near-ideal linear switching, and 95% pattern-identification accuracy. The findings demonstrate the usefulness of 2D materials as integrated heating elements in artificial synapse chips to accelerate neuromorphic computation.

Superconducting disordered neural networks for neuromorphic processing with fluxons

In superconductors, magnetic fields are quantized into discrete fluxons (flux quanta Φ₀), made of microscopic circulating supercurrents. We introduce a multiterminal synapse network comprising a disordered array of superconducting loops with Josephson junctions. The loops can trap fluxons defining memory, while the junctions allow their movement between loops. Dynamics of fluxons through such a disordered system through a complex reconfigurable energy landscape represents brain-like spiking information flow. In this work, we experimentally demonstrate a three-loop network using YBa₂Cu₃O_{7 - δ}-based superconducting loops and Josephson junctions, which exhibit stable memory configurations of trapped flux in loops that determine the rate of flow of fluxons through synaptic connections. The memory states are, in turn, affected by the applied input signals but can also be externally configured electrically through control current/feedback terminals. These results establish a previously unexplored, biologically similar architectural approach to neuromorphic computing that is scalable while dissipating energy of atto Joules/spike.

Dynamic Self-Rectifying Liquid Metal-Semiconductor Heterointerfaces: A Platform for Development of Bioinspired Afferent Systems

The assembly of geometrically complex and dynamically active liquid metal/semiconductor heterointerfaces has drawn extensive attention in multidimensional electronic systems. In this study the chemovoltaic driven reactions have enabled the microfluidity of hydrophobic galinstan into a three-dimensional (3D) semiconductor matrix. A dynamic heterointerface is developed between the atomically thin surface oxide of galinstan and the TiO₂-Ni interface. Upon the growth of Ga₂O₃ film at the Ga₂O₃-TiO₂ heterointerface, the partial reduction of the TiO₂ film was confirmed by material characterization techniques. The conductance imaging spectroscopy and electrical measurements are used to investigate the charge transfer at heterointerfaces. Concurrently, the dynamic conductance in artificial synaptic junctions is modulated to mimic the biofunctional communication characteristics of multipolar neurons, including slow and fast inhibitory and excitatory postsynaptic responses. The self-rectifying characteristics, femtojoule energy processing, tunable synaptic events, and notably the coordinated signal recognition are the main characteristics of this multisynaptic device. This novel 3D design of liquid metal-semiconductor structure opens up new opportunities for the development of bioinspired afferent systems. It further facilitates the realization of physical phenomena at liquid metal-semiconductor heterointerfaces.

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.

Direct Electrochemical Protonation of Metal Oxide Particles

Metal oxides with surface protonation exhibit versatile physical and chemical properties suitable for use in many fields. Here, we develop an electrochemical route to directly protonize the physically assembled oxide particles, such as TiO₂, Nb₂O₅, and WO₃, in a Na₂SO₄ neutral electrolyte, which is a result of electrochemically induced oxygen vacancies reacting with water molecules. With no need of electric connection among particles or between particles and conductive substrate, the electrochemical protonation follows a bottom-up particle-by-particle surface protonation mechanism due to the fact that the protonation inducing high surface conductivity creates an efficient electron transfer pathway among particles. Our results show that electrochemical protonation of particles provides a chance to finely functionalize the surface of a single particle by only adjusting electrode potentials. Such a facile, cost-efficient, and green route is easy to run for a large-scale production and unlocks the potential of semiconductor oxides for various applications.

Doping-Enabled Reconfigurable Strongly Correlated Phase in a Quasi-2D Perovskite

Highlighted by the discovery of high-temperature superconductivity, strongly correlated oxides with highly distorted perovskite structures serve as intriguing model systems for pursuing emerging materials physics and testing technological concepts. While 3d correlated oxides with a distorted perovskite structure are not uncommon, their 4d counterparts are unfortunately rare. In this work, we report the tuning of the electrical and optical properties of a quasi-2D perovskite niobate CsBiNb₂O₇ via hydrogenation. It is observed that hydrogenation induces drastic changes of lattice dynamics, optical transmission, and conductance. It is suggested that changing the orbital occupancy of Nb d orbitals could trigger the on-site Coulomb interaction in the NbO₆ octahedron. The observed hydrogen doping-induced electrical plasticity is implemented for simulating neural synaptic activity. Our finding sheds light on the role of hydrogen in 4d transition metal oxides and suggests a new avenue for the design and development of novel electronic phases.

Unusual Role of Point Defects in Perovskite Nickelate Electrocatalysts

Low-cost transition-metal oxide is regarded as a promising electrocatalyst family for an oxygen evolution reaction (OER). The classic design principle for an oxide electrocatalyst believes that point defect engineering, such as oxygen vacancies (V_O^..) or heteroatom doping, offers the opportunities to manipulate the electronic structure of material toward optimal OER activity. Oppositely, in this work, we discover a counterintuitive phenomenon that both V_O^.. and an aliovalent dopant (i.e., proton (H⁺)) in perovskite nickelate (i.e., NdNiO₃ (NNO)) have a considerably detrimental effect on intrinsic OER performance. Detailed characterizations unveil that the introduction of these point defects leads to a decrease in the oxidative state of Ni and weakens Ni-O orbital hybridization, which triggers the local electron-electron correlation and a more insulating state. Evidenced by first-principles calculation using the density functional theory (DFT) method, the OER on nickelate electrocatalysts follows the lattice oxygen mechanism (LOM). The incorporation of point defect increases the energy barrier of transformation from OO*(V_O) to OH*(V_O) intermediates, which is regarded as the rate-determining step (RDS). This work offers a new and significant perspective of the role that lattice defects play in the OER process.