Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.

Memristive devices for computing

In this paper, recent progress of binary metal-oxide resistive switching random access memory (RRAM) is reviewed. The physical mechanism, material properties, and electrical characteristics of a variety of binary metal-oxide RRAM are discussed, with a focus on the use of RRAM for nonvolatile memory application. A review of recent development of large-scale RRAM arrays is given. Issues such as uniformity, endurance, retention, multibit operation, and scaling trends are discussed.

Metal–Oxide RRAM

Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 10(14) synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks based on circuits combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one or several crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits, including first demonstrations of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks. Very recently, such experiments have been extended to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors, whose nonlinear current-voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.

Training and operation of an integrated neuromorphic network based on metal-oxide memristors

The authors of the International Technology Roadmap for Semiconductors-the industry consensus set of goals established for advancing silicon integrated circuit technology-have challenged the computing research community to find new physical state variables (other than charge or voltage), new devices, and new architectures that offer memory and logic functions beyond those available with standard transistors. Recently, ultra-dense resistive memory arrays built from various two-terminal semiconductor or insulator thin film devices have been demonstrated. Among these, bipolar voltage-actuated switches have been identified as physical realizations of 'memristors' or memristive devices, combining the electrical properties of a memory element and a resistor. Such devices were first hypothesized by Chua in 1971 (ref. 15), and are characterized by one or more state variables that define the resistance of the switch depending upon its voltage history. Here we show that this family of nonlinear dynamical memory devices can also be used for logic operations: we demonstrate that they can execute material implication (IMP), which is a fundamental Boolean logic operation on two variables p and q such that pIMPq is equivalent to (NOTp)ORq. Incorporated within an appropriate circuit, memristive switches can thus perform 'stateful' logic operations for which the same devices serve simultaneously as gates (logic) and latches (memory) that use resistance instead of voltage or charge as the physical state variable.

‘Memristive’ switches enable ‘stateful’ logic operations via material implication

A memristor is a resistive device with an inherent memory. The theoretical concept of a memristor was connected to physically measured devices in 2008 and since then there has been rapid progress in the development of such devices, leading to a series of recent demonstrations of memristor-based neuromorphic hardware systems. Here, we evaluate the state of the art in memristor-based electronics and explore where the future of the field lies. We highlight three areas of potential technological impact: on-chip memory and storage, biologically inspired computing and general-purpose in-memory computing. We analyse the challenges, and possible solutions, associated with scaling the systems up for practical applications, and consider the benefits of scaling the devices down in terms of geometry and also in terms of obtaining fundamental control of the atomic-level dynamics. Finally, we discuss the ways we believe biology will continue to provide guiding principles for device innovation and system optimization in the field. This Perspective evaluates the state of the art in memristor-based electronics and explores the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing.

The future of electronics based on memristive systems

Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges

Ionic conduction in zirconia films of nanometer thickness

The electrochemical changes induced by an electric field in Fe-doped SrTiO3 have been investigated by X-ray absorption spectroscopy (XANES and EXAFS), electron paramagnetic resonance (EPR) and Raman spectroscopy. A detailed study of the Fe dopant in the regions around the anode and cathode reveals new insights into the local structure and valence state of Fe in SrTiO3 single crystals. The ab initio full multiple-scattering XANES calculations give an evidence of the oxygen vacancy presence in the first coordination shell of iron. Differences in the length and disorder of the Fe–O bonds as extracted from EXAFS are correlated to the unequivocal identification of the defect type by complementary spectroscopical techniques to identify the valence state of the Fe-dopant and the presence of the Fe − VO complexes. Through this combinatorial approach, novel structural information on Fe − VO complexes is provided by X-ray absorption spectroscopy, and the relation of Fe–O bond length, doping level and oxidation state in SrTi1−xFexO3 is briefly discussed.

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Spectroscopic study of the electric field induced valence change of Fe-defect centers in SrTiO3.

Studies on dislocations in prototypic binary and ternary oxides (here TiO2 and SrTiO3) using modern TEM and scanning probe microscopy (SPM) techniques, combined with classical etch pits methods, are reviewed. Our review focuses on the important role of dislocations in the insulator-to-metal transition and for redox processes, which can be preferentially induced along dislocations using chemical and electrical gradients. It is surprising that, independently of the growth techniques, the density of dislocations in the surface layers of both prototypical oxides is high (109/cm2 for epipolished surfaces and up to 1012/cm2 for the rough surface). The TEM and locally-conducting atomic force microscopy (LCAFM) measurements show that the dislocations create a network with the character of a hierarchical tree. The distribution of the dislocations in the plane of the surface is, in principle, inhomogeneous, namely a strong tendency for the bundling and creation of arrays or bands in the crystallographic and directions can be observed. The analysis of the core of dislocations using scanning transmission electron microscopy (STEM) techniques (such as EDX with atomic resolution, electron-energy loss spectroscopy (EELS)) shows unequivocally that the core of dislocations possesses a different crystallographic structure, electronic structure and chemical composition relative to the matrix. Because the Burgers vector of dislocations is per se invariant, the network of dislocations (with additional d1 electrons) causes an electrical short-circuit of the matrix. This behavior is confirmed by LCAFM measurements for the stoichiometric crystals, moreover a similar dominant role of dislocations in channeling of the current after thermal reduction of the crystals or during resistive switching can be observed. In our opinion, the easy transformation of the chemical composition of the surface layers of both model oxides should be associated with the high concentration of extended defects in this region. Another important insight for the analysis of the physical properties in real oxide crystals (matrix + dislocations) comes from the studies of the nucleation of dislocations via in situ STEM indentation, namely that the dislocations can be simply nucleated under mechanical stimulus and can be easily moved at room temperature.

Influence of Dislocations in Transition Metal Oxides on Selected Physical and Chemical Properties

We present an X-ray absorption spectroscopy study on Fe-doped SrTiO3 thin films grown by pulsed laser deposition. The Fe L2,3 edge spectra are recorded for doping concentrations from 0–5% after several annealing steps at moderate temperatures. The Fe valence state is determined by comparison with an ilmenite reference sample and calculations according to the charge transfer multiplet model. We found clear evidence of Fe2+ and Fe3+ oxidation states independently of the doping concentration. The Fe2+ signal is enhanced at the surface and increases after annealing. The Fe2+ configuration is in contrast to the mixed Fe3+/Fe4+ valence state in bulk material and must be explained by the specific defect structure of the thin films due to the kinetically limited growth which induces a high concentration of oxygen vacancies.

Kristof Szot

Papers

Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges

Ionic conduction in zirconia films of nanometer thickness

Spectroscopic study of the electric field induced valence change of Fe-defect centers in SrTiO3.

Influence of Dislocations in Transition Metal Oxides on Selected Physical and Chemical Properties

Detection of Fe2+ valence states in Fe doped SrTiO3 epitaxial thin films grown by pulsed laser deposition