Imaging the itinerant-to-localized transmutation of electrons across the metal-to-insulator transition in V2O3.

In solids, strong repulsion between electrons can inhibit their movement and result in a "Mott" metal-to-insulator transition (MIT), a fundamental phenomenon whose understanding has remained a challenge for over 50 years. A key issue is how the wave-like itinerant electrons change into a localized-like state due to increased interactions. However, observing the MIT in terms of the energy- and momentum-resolved electronic structure of the system, the only direct way to probe both itinerant and localized states, has been elusive. Here we show, using angle-resolved photoemission spectroscopy (ARPES), that in V2O3, the temperature-induced MIT is characterized by the progressive disappearance of its itinerant conduction band, without any change in its energy-momentum dispersion, and the simultaneous shift to larger binding energies of a quasi-localized state initially located near the Fermi level.

Spatiotemporal characterization of the field-induced insulator-to-metal transition.

Many correlated systems feature an insulator-to-metal transition that can be triggered by an electric field. Although it is known that metallization takes place through filament formation, the details of how this process initiates and evolves remain elusive. We use in-operando optical reflectivity to capture the growth dynamics of the metallic phase with space and time resolution. We demonstrate that filament formation is triggered by nucleation at hotspots, with a subsequent expansion over several decades in time. By comparing three case studies (VO2, V3O5, and V2O3), we identify the resistivity change across the transition as the crucial parameter governing this process. Our results provide a spatiotemporal characterization of volatile resistive switching in Mott insulators, which is important for emerging technologies, such as optoelectronics and neuromorphic computing.

A Functional Spiking Neural Network of Ultra Compact Neurons.

We demonstrate that recently introduced ultra-compact neurons (UCN) with a minimal number of components can be interconnected to implement a functional spiking neural network. For concreteness we focus on the Jeffress model, which is a classic neuro-computational model proposed in the 40's to explain the sound directionality detection by animals and humans. In addition, we introduce a long-axon neuron, whose architecture is inspired by the Hodgkin-Huxley axon delay-line and where the UCNs implement the nodes of Ranvier. We then interconnect two of those neurons to an output layer of UCNs, which detect coincidences between spikes propagating down the long-axons. This functional spiking neural neuron circuit with biological relevance is built from identical UCN blocks, which are simple enough to be made with off-the-shelf electronic components. Our work realizes a new, accessible and affordable physical model platform, where neuroscientists can construct arbitrary mid-size spiking neuronal networks in a lego-block like fashion that work in continuous time. This should enable them to address in a novel experimental manner fundamental questions about the nature of the neural code and to test predictions from mathematical models and algorithms of basic neurobiology research. The present work aims at opening a new experimental field of basic research in Spiking Neural Networks to a potentially large community, which is at the crossroads of neurobiology, dynamical systems, theoretical neuroscience, condensed matter physics, neuromorphic engineering, artificial intelligence, and complex systems.

Operando characterization of conductive filaments during resistive switching in Mott VO2.

Vanadium dioxide (VO2) has attracted much attention owing to its metal-insulator transition near room temperature and the ability to induce volatile resistive switching, a key feature for developing novel hardware for neuromorphic computing. Despite this interest, the mechanisms for nonvolatile switching functioning as synapse in this oxide remain not understood. In this work, we use in situ transmission electron microscopy, electrical transport measurements, and numerical simulations on Au/VO2/Ge vertical devices to study the electroforming process. We have observed the formation of V5O9 conductive filaments with a pronounced metal-insulator transition and that vacancy diffusion can erase the filament, allowing for the system to "forget." Thus, both volatile and nonvolatile switching can be achieved in VO2, useful to emulate neuronal and synaptic behaviors, respectively. Our systematic operando study of the filament provides a more comprehensive understanding of resistive switching, key in the development of resistive switching-based neuromorphic computing.

Disordered Mott-Hubbard Physics in Nanoparticle Solids: Transitions Driven by Disorder, Interactions, and Their Interplay.

We show that adapting the knowledge developed for the disordered Mott-Hubbard model to nanoparticle (NP) solids can deliver many very helpful new insights. We developed a hierarchical nanoparticle transport simulator (HINTS), which builds from localized states to describe the disorder-localized and Mott-localized phases of NP solids and the transitions out of these localized phases. We also studied the interplay between correlations and disorder in the corresponding multiorbital Hubbard model at and away from integer filling by dynamical mean field theory. This DMFT approach is complementary to HINTS, as it builds from the metallic phase of the NP solid. The mobility scenarios produced by the two methods are strikingly similar and account for the mobilities measured in NP solids. We conclude this work by constructing the comprehensive phase diagram of PbSe NP solids on the disorder-filling plane.

Biologically Relevant Dynamical Behaviors Realized in an Ultra-Compact Neuron Model.

We demonstrate a variety of biologically relevant dynamical behaviors building on a recently introduced ultra-compact neuron (UCN) model. We provide the detailed circuits which all share a common basic block that realizes the leaky-integrate-and-fire (LIF) spiking behavior. All circuits have a small number of active components and the basic block has only three, two transistors and a silicon controlled rectifier (SCR). We also demonstrate that numerical simulations can faithfully represent the variety of spiking behavior and can be used for further exploration of dynamical behaviors. Taking Izhikevich's set of biologically relevant behaviors as a reference, our work demonstrates that a circuit of a LIF neuron model can be used as a basis to implement a large variety of relevant spiking patterns. These behaviors may be useful to construct neural networks that can capture complex brain dynamics or may also be useful for artificial intelligence applications. Our UCN model can therefore be considered the electronic circuit counterpart of Izhikevich's (2003) mathematical neuron model, sharing its two seemingly contradicting features, extreme simplicity and rich dynamical behavior.

Subthreshold firing in Mott nanodevices.

Resistive switching, a phenomenon in which the resistance of a device can be modified by applying an electric field1-5, is at the core of emerging technologies such as neuromorphic computing and resistive memories6-9. Among the different types of resistive switching, threshold firing10-14 is one of the most promising, as it may enable the implementation of artificial spiking neurons7,13,14. Threshold firing is observed in Mott insulators featuring an insulator-to-metal transition15,16, which can be triggered by applying an external voltage: the material becomes conducting ('fires') if a threshold voltage is exceeded7,10-12. The dynamics of this induced transition have been thoroughly studied, and its underlying mechanism and characteristic time are well documented10,12,17,18. By contrast, there is little knowledge regarding the opposite transition: the process by which the system returns to the insulating state after the voltage is removed. Here we show that Mott nanodevices retain a memory of previous resistive switching events long after the insulating resistance has recovered. We demonstrate that, although the device returns to its insulating state within 50 to 150 nanoseconds, it is possible to re-trigger the insulator-to-metal transition by using subthreshold voltages for a much longer time (up to several milliseconds). We find that the intrinsic metastability of first-order phase transitions is the origin of this phenomenon, and so it is potentially present in all Mott systems. This effect constitutes a new type of volatile memory in Mott-based devices, with potential applications in resistive memories, solid-state frequency discriminators and neuromorphic circuits.

Direct Evidence of Lithium Ion Migration in Resistive Switching of Lithium Cobalt Oxide Nanobatteries.

Lithium cobalt oxide nanobatteries offer exciting prospects in the field of nonvolatile memories and neuromorphic circuits. However, the precise underlying resistive switching (RS) mechanism remains a matter of debate in two-terminal cells. Herein, intriguing results, obtained by secondary ion mass spectroscopy (SIMS) 3D imaging, clearly demonstrate that the RS mechanism corresponds to lithium migration toward the outside of the Lix CoO2 layer. These observations are very well correlated with the observed insulator-to-metal transition of the oxide. Besides, smaller device area experimentally yields much faster switching kinetics, which is qualitatively well accounted for by a simple numerical simulation. Write/erase endurance is also highly improved with downscaling - much further than the present cycling life of usual lithium-ion batteries. Hence very attractive possibilities can be envisaged for this class of materials in nanoelectronics.

Magnetism, Spin Texture, and In-Gap States: Atomic Specialization at the Surface of Oxygen-Deficient SrTiO_{3}.

Motivated by recent spin- and angular-resolved photoemission (SARPES) measurements of the two-dimensional electronic states confined near the (001) surface of oxygen-deficient SrTiO_{3}, we explore their spin structure by means of ab initio density functional theory (DFT) calculations of slabs. Relativistic nonmagnetic DFT calculations display Rashba-like spin winding with a splitting of a few meV and when surface magnetism on the Ti ions is included, bands become spin-split with an energy difference ∼100 meV at the Γ point, consistent with SARPES findings. While magnetism tends to suppress the effects of the relativistic Rashba interaction, signatures of it are still clearly visible in terms of complex spin textures. Furthermore, we observe an atomic specialization phenomenon, namely, two types of electronic contributions: one is from Ti atoms neighboring the oxygen vacancies that acquire rather large magnetic moments and mostly create in-gap states; another comes from the partly polarized t_{2g} itinerant electrons of Ti atoms lying further away from the oxygen vacancy, which form the two-dimensional electron system and are responsible for the Rashba spin winding and the spin splitting at the Fermi surface.

Quantum magnets with anisotropic infinite range random interactions.

Using exact diagonalization techniques, we study the dynamical response of the anisotropic disordered Heisenberg model for systems of S=1/2 spins with infinite range random exchange interactions at temperature T=0. The model can be considered as a generalization, to the quantum case, of the well-known Sherrington-Kirkpatrick classical spin glass model. We also compute and study the behavior of the Edwards Anderson order parameter and energy per spin as the anisotropy evolves from the Ising to the Heisenberg limits.

Dynamical mean field theory with the density matrix renormalization group.

A new numerical method for the solution of the dynamical mean field theory's self-consistent equations is introduced. The method uses the density matrix renormalization group technique to solve the associated impurity problem. The new algorithm makes no a priori approximations and is only limited by the number of sites that can be considered. We obtain accurate estimates of the critical values of the metal-insulator transitions and provide evidence of substructure in the Hubbard bands of the correlated metal. With this algorithm, more complex models having a larger number of degrees of freedom can be considered and finite-size effects can be minimized.

Quantum and thermal fluctuations in the SU(N) Heisenberg spin-glass model near the quantum critical point.

We solve for the SU(N) Heisenberg spin glass in the limit of large N focusing on small S and T. We study the effect of quantum and thermal fluctuations in the frequency dependent response function and observe interesting transfers of spectral weight. We compute the T dependence of the order parameter and the specific heat and find an unusual T2 behavior for the latter at low temperatures in the spin-glass phase. We find remarkable qualitative agreement with various experiments on the quantum frustrated magnet SrCr(9p)Ga(12-9p)O19.