A scalable neuristor built with Mott memristors

A fast operation of nanometer-scale metallic memristors: highly transparent conductance channels in Ag2S devices

The nonlinear transport properties of nanometer-scale junctions formed between an inert metallic tip and an Ag film covered by a thin Ag2S layer are investigated. Suitably prepared samples exhibit memristive behavior with technologically optimal ON and OFF state resistances yielding to resistive switching on the nanosecond time scale. Utilizing point contact Andreev reflection spectroscopy, we studied the nature of electron transport in the active volume of memristive junctions showing that both the ON and OFF states correspond to truly nanometer-scale, highly transparent metallic channels. Our results demonstrate the merits of Ag2S nanojunctions as nanometer-scale memory cells which can be switched by nanosecond voltage pulses.

Spin-torque building blocks

The discovery of the spin-torque effect has made magnetic nanodevices realistic candidates for active elements of memory devices and applications. Magnetoresistive effects allow the read-out of increasingly small magnetic bits, and the spin torque provides an efficient tool to manipulate - precisely, rapidly and at low energy cost - the magnetic state, which is in turn the central information medium of spintronic devices. By keeping the same magnetic stack, but by tuning a device's shape and bias conditions, the spin torque can be engineered to build a variety of advanced magnetic nanodevices. Here we show that by assembling these nanodevices as building blocks with different functionalities, novel types of computing architecture can be envisaged. We focus in particular on recent concepts such as magnonics and spintronic neural networks.

Local temperature redistribution and structural transition during joule-heating-driven conductance switching in VO2

Joule-heating induced conductance-switching is studied in VO2 , a Mott insulator. Complementary in situ techniques including optical characterization, blackbody microscopy, scanning transmission X-ray microscopy (STXM) and numerical simulations are used. Abrupt redistribution in local temperature is shown to occur upon conductance-switching along with a structural phase transition, at the same current.

Nanoscale resistive switching devices: mechanisms and modeling

Resistive switching devices (also termed memristive devices or memristors) are two-terminal nonlinear dynamic electronic devices that can have broad applications in the fields of nonvolatile memory, reconfigurable logic, analog circuits, and neuromorphic computing. Current rapid advances in memristive devices in turn demand better understanding of the switching mechanism and the development of physics-based as well as simplified device models to guide future device designs and circuit-level applications. In this article, we review the physical processes behind resistive switching (memristive) phenomena and discuss the experimental and modeling efforts to explain these effects. In this article three categories of devices, in which the resistive switching effects are driven by cation migration, anion migration, and electronic effects, will be discussed. The fundamental driving forces and the stochastic nature of resistive switching will also be discussed.

Synaptic electronics: materials, devices and applications

In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological synaptic plasticity and learning are described. The material properties and electrical switching characteristics of a variety of synaptic devices are discussed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing. Performance metrics desirable for large-scale implementations of synaptic devices are illustrated. A review of recent work on targeted computing applications with synaptic devices is presented.

Phase transitions enable computational universality in neuristor-based cellular automata

We recently demonstrated that Mott memristors, two-terminal devices that exhibit threshold switching via an insulator to conductor phase transition, can serve as the active components necessary to build a neuristor, a biomimetic threshold spiking device. Here we extend those results to demonstrate, in simulation, neuristor-based circuits capable of performing general Boolean logic operations. We additionally show that these components can be used to construct a one-dimensional cellular automaton, rule 137, previously proven to be universal. This proof-of-principle shows that localized phase transitions can perform spiking computation, which is of particular interest for neuromorphic hardware.

Analog neuromorphic module based on carbon nanotube synapses

We report an analog neuromorphic module composed of p-type carbon nanotube (CNT) synapses and an integrate-and-fire (I&F) circuit. The CNT synapse has a field-effect transistor structure with a random CNT network as its channel and an aluminum oxide dielectric layer implanted with indium ions as its gate. A positive voltage pulse (spike) applied on the gate attracts electrons into the defect sites of the gate dielectric layer, and the trapped electrons are gradually released after the pulse is removed. The electrons modify the hole concentration and induce a dynamic postsynaptic current in the CNT channel. Multiple input spikes induce excitatory or inhibitory postsynaptic currents via excitatory or inhibitory CNT synapses, which flow toward an I&F circuit to trigger output spikes. The dynamic transfer function between the input and output spikes of the neuromorphic module is analyzed. The module could potentially be scaled up to emulate biological neural networks and their functions.

Elastic resistance change and action potential generation of non-faradaic Pt/TiO2/Pt capacitors

Electric current in the mixed ionic-electronic conductor TiO2 is hysteretic, i.e. history-dependent, and its use is versatile in electronic devices. Nowadays, biologically inspired, analogue-type computing systems, known as neuromorphic systems, are being actively investigated owing to their new and intriguing physical concepts. The realization of artificial synapses is important for constructing neuromorphic systems. In mammalians' brains, the plasticity of synapses between neighbouring nerve cells arises from action potential firing. Emulating action potential firing via inorganic systems has therefore become important in neuromorphic engineering. In this work, the current-voltage hysteresis of TiO2-based non-faradaic capacitors is investigated to primarily focus on the correlation between the blocking contact and the elasticity, i.e. non-plasticity, of the capacitors' resistance change, in experimental and theoretical methods. The similarity between the action potential firing behaviour in nerve cells and the elasticity of the non-faradaic capacitors is addressed.

Stochastic memristive devices for computing and neuromorphic applications

Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems. However a major challenge is to address the potentially large variations in space and time in these nanoscale devices. Here we show that in metal-filament based memristive devices the switching can be fully stochastic. While individual switching events are random, the distribution and probability of switching can be well predicted and controlled. Rather than trying to force high switching probabilities using excess voltage or time, the inherent stochastic nature of resistive switching allows these binary devices to be used as building blocks for novel error-tolerant computing schemes such as stochastic computing and provides the needed "analog" feature for neuromorphic applications. To verify such potential, we demonstrated memristor-based stochastic bitstreams in both time and space domains, and show that an array of binary memristors can act as a multi-level "analog" device for neuromorphic applications.