Reliability effects of lateral filament confinement by nano-scaling the oxide in memristive devices.

Write-variability and resistance instability are major reliability concerns impeding implementation of oxide-based memristive devices in neuromorphic systems. The root cause of the reliability issues is the stochastic nature of conductive filament formation and dissolution, whose impact is particularly critical in the high resistive state (HRS). Optimizing the filament stability requires mitigating diffusive processes within the oxide, but these are unaffected by conventional electrode scaling. Here we propose a device design that laterally confines the switching oxide volume and thus the filament to 10 nm, which yields reliability improvements in our measurements and simulations. We demonstrate a 50% decrease in HRS write-variability for an oxide nano-fin device in our full factorial analysis of modulated current-voltage sweeps. Furthermore, we use ionic noise measurements to quantify the HRS filament stability against diffusive processes. The laterally confined filaments exhibit a change in the signal-to-noise ratio distribution with a shift to higher values. Our complementing kinetic Monte Carlo simulation of oxygen vacancy (re-)distribution for confined filaments shows improved noise behavior and elucidates the underlying physical mechanisms. While lateral oxide volume scaling down to filament sizes is challenging, our efforts motivate further examination and awareness of filament confinement effects in regards to reliability.

Space charge governs the kinetics of metal exsolution.

Nanostructured composite electrode materials play a major role in the fields of catalysis and electrochemistry. The self-assembly of metallic nanoparticles on oxide supports via metal exsolution relies on the transport of reducible dopants towards the perovskite surface to provide accessible catalytic centres at the solid-gas interface. At surfaces and interfaces, however, strong electrostatic gradients and space charges typically control the properties of oxides. Here we reveal that the nature of the surface-dopant interaction is the main determining factor for the exsolution kinetics of nickel in SrTi0.9Nb0.05Ni0.05O3-δ. The electrostatic interaction of dopants with surface space charge regions forming upon thermal oxidation results in strong surface passivation, which manifests in a retarded exsolution response. We furthermore demonstrate the controllability of the exsolution response via engineering of the perovskite surface chemistry. Our findings indicate that tailoring the electrostatic gradients at the perovskite surface is an essential step to improve exsolution-type materials in catalytic converters.

Atomically resolved electronic properties in single layer graphene on α-Al2O3 (0001) by chemical vapor deposition.

Metal-free chemical vapor deposition (CVD) of single-layer graphene (SLG) on c-plane sapphire has recently been demonstrated for wafer diameters of up to 300 mm, and the high quality of the SLG layers is generally characterized by integral methods. By applying a comprehensive analysis approach, distinct interactions at the graphene-sapphire interface and local variations caused by the substrate topography are revealed. Regions near the sapphire step edges show tiny wrinkles with a height of about 0.2 nm, framed by delaminated graphene as identified by the typical Dirac cone of free graphene. In contrast, adsorption of CVD SLG on the hydroxyl-terminated α-Al2O3 (0001) terraces results in a superstructure with a periodicity of (2.66 ± 0.03) nm. Weak hydrogen bonds formed between the hydroxylated sapphire surface and the π-electron system of SLG result in a clean interface. The charge injection induces a band gap in the adsorbed graphene layer of about (73 ± 3) meV at the Dirac point. The good agreement with the predictions of a theoretical analysis underlines the potential of this hybrid system for emerging electronic applications.

A high throughput generative vector autoregression model for stochastic synapses.

By imitating the synaptic connectivity and plasticity of the brain, emerging electronic nanodevices offer new opportunities as the building blocks of neuromorphic systems. One challenge for large-scale simulations of computational architectures based on emerging devices is to accurately capture device response, hysteresis, noise, and the covariance structure in the temporal domain as well as between the different device parameters. We address this challenge with a high throughput generative model for synaptic arrays that is based on a recently available type of electrical measurement data for resistive memory cells. We map this real-world data onto a vector autoregressive stochastic process to accurately reproduce the device parameters and their cross-correlation structure. While closely matching the measured data, our model is still very fast; we provide parallelized implementations for both CPUs and GPUs and demonstrate array sizes above one billion cells and throughputs exceeding one hundred million weight updates per second, above the pixel rate of a 30 frames/s 4K video stream.

Standards for the Characterization of Endurance in Resistive Switching Devices.

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

A Consistent Model for Short-Term Instability and Long-Term Retention in Filamentary Oxide-Based Memristive Devices.

Major challenges concerning the reliability of resistive switching random access memories based on the valence change mechanism (VCM) are short-term instability and long-term retention failure of the programmed resistance state, particularly in the high resistive state. On the one hand, read noise limits the reliability of VCMs via comparatively small current jumps especially when looking at the statistics of millions of cells that are needed for industrial applications. Additionally, shaping algorithms aiming for an enlargement of the read window are observed to have no lasting effect. On the other hand, long-term retention failures limiting the lifetime of the programmed resistance states need to be overcome. The physical origin of these phenomena is still under debate and needs to be understood much better. In this work, we present a three-dimensional kinetic Monte Carlo simulation model where we implemented diffusion-limiting domains to the oxide layer of the VCM cell. We demonstrate that our model can explain both instability and retention failure consistently by the same physical processes. Further, we find that the random diffusion of oxygen vacancies plays an important role regarding the reliability of VCMs and can explain instability phenomena as the shaping failure as well as the long-term retention failure in our model. Additionally, the results of the simulations are compared with experimental data of read noise and retention investigations on ZrO2-based VCM devices.

Utilizing the Switching Stochasticity of HfO2/TiOx-Based ReRAM Devices and the Concept of Multiple Device Synapses for the Classification of Overlapping and Noisy Patterns.

With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data, neuromorphic chip concepts are seen as key solutions for coping with the massive amount of unstructured data streams by moving the computation closer to the sensors, the so-called "edge computing." Augmenting these chips with emerging memory technologies enables these edge devices with non-volatile and adaptive properties which are desirable for low power and online learning operations. However, an energy- and area-efficient realization of these systems requires disruptive hardware changes. Memristor-based solutions for these concepts are in the focus of research and industry due to their low-power and high-density online learning potential. Specifically, the filamentary-type valence change mechanism (VCM memories) have shown to be a promising candidate In consequence, physical models capturing a broad spectrum of experimentally observed features such as the pronounced cycle-to-cycle (c2c) and device-to-device (d2d) variability are required for accurate evaluation of the proposed concepts. In this study, we present an in-depth experimental analysis of d2d and c2c variability of filamentary-type bipolar switching HfO2/TiOx nano-sized crossbar devices and match the experimentally observed variabilities to our physically motivated JART VCM compact model. Based on this approach, we evaluate the concept of parallel operation of devices as a synapse both experimentally and theoretically. These parallel synapses form a synaptic array which is at the core of neuromorphic chips. We exploit the c2c variability of these devices for stochastic online learning which has shown to increase the effective bit precision of the devices. Finally, we demonstrate that stochastic switching features for a pattern classification task that can be employed in an online learning neural network.

Theory and experimental verification of configurable computing with stochastic memristors.

The inevitable variability within electronic devices causes strict constraints on operation, reliability and scalability of the circuit design. However, when a compromise arises among the different performance metrics, area, time and energy, variability then loosens the tight requirements and allows for further savings in an alternative design scope. To that end, unconventional computing approaches are revived in the form of approximate computing, particularly tuned for resource-constrained mobile computing. In this paper, a proof-of-concept of the approximate computing paradigm using memristors is demonstrated. Stochastic memristors are used as the main building block of probabilistic logic gates. As will be shown in this paper, the stochasticity of memristors' switching characteristics is tightly bound to the supply voltage and hence to power consumption. By scaling of the supply voltage to appropriate levels stochasticity gets increased. In order to guide the design process of approximate circuits based on memristors a realistic device model needs to be elaborated with explicit emphasis of the probabilistic switching behavior. Theoretical formulation, probabilistic analysis, and simulation of the underlying logic circuits and operations are introduced. Moreover, the expected output behavior is verified with the experimental measurements of valence change memory cells. Hence, it is shown how the precision of the output is varied for the sake of the attainable gains at different levels of available design metrics. This approach represents the first proposition along with physical verification and mapping to real devices that combines stochastic memristors into unconventional computing approaches.

Exsolution of Embedded Nanoparticles in Defect Engineered Perovskite Layers.

Exsolution phenomena are highly debated as efficient synthesis routes for nanostructured composite electrode materials for the application in solid oxide cells (SOCs) and the development of next-generation electrochemical devices for energy conversion. Utilizing the instability of perovskite oxides, doped with electrocatalytically active elements, highly dispersed nanoparticles can be prepared at the perovskite surface under the influence of a reducing heat treatment. For the systematic study of the mechanistic processes governing metal exsolution, epitaxial SrTi0.9Nb0.05Ni0.05O3-δ thin films of well-defined stoichiometry are synthesized and employed as model systems to investigate the interplay of defect structures and exsolution behavior. Spontaneous phase separation and the formation of dopant-rich features in the as-synthesized thin film material is revealed by high-resolution transmission electron microscopy (HR-TEM) investigations. The resulting nanostructures are enriched by nickel and serve as preformed nuclei for the subsequent exsolution process under reducing conditions, which reflects a so far unconsidered process drastically affecting the understanding of nanoparticle exsolution phenomena. Using an approach of combined morphological, chemical, and structural analysis of the exsolution response, a limitation of the exsolution dynamics for nonstoichiometric thin films is found to be correlated to a distortion of the perovskite host lattice. Consequently, the incorporation of defect structures results in a reduced particle density at the perovskite surface, presumably by trapping of nanoparticles in the oxide bulk.

Tuning electrochemically driven surface transformation in atomically flat LaNiO3 thin films for enhanced water electrolysis.

Structure-activity relationships built on descriptors of bulk and bulk-terminated surfaces are the basis for the rational design of electrocatalysts. However, electrochemically driven surface transformations complicate the identification of such descriptors. Here we demonstrate how the as-prepared surface composition of (001)-terminated LaNiO3 epitaxial thin films dictates the surface transformation and the electrocatalytic activity for the oxygen evolution reaction. Specifically, the Ni termination (in the as-prepared state) is considerably more active than the La termination, with overpotential differences of up to 150 mV. A combined electrochemical, spectroscopic and density-functional theory investigation suggests that this activity trend originates from a thermodynamically stable, disordered NiO2 surface layer that forms during the operation of Ni-terminated surfaces, which is kinetically inaccessible when starting with a La termination. Our work thus demonstrates the tunability of surface transformation pathways by modifying a single atomic layer at the surface and that active surface phases only develop for select as-synthesized surface terminations.

Picosecond multilevel resistive switching in tantalum oxide thin films.

The increasing demand for high-density data storage leads to an increasing interest in novel memory concepts with high scalability and the opportunity of storing multiple bits in one cell. A promising candidate is the redox-based resistive switch repositing the information in form of different resistance states. For reliable programming, the underlying physical parameters need to be understood. We reveal that the programmable resistance states are linked to internal series resistances and the fundamental nonlinear switching kinetics. The switching kinetics of [Formula: see text]-based cells was investigated in a wide range over 15 orders of magnitude from 10[Formula: see text] s to 250 ps. The capacitive charging time of our device limits the direct observation of the set time below 770 ps, however, we found indication for an intrinsic switching speed of 10 ps at a stimulus of 3 V. On all time scales, multi-bit data storage capabilities were demonstrated. The elucidated link between fundamental material properties and multi-bit data storage paves the way for designing resistive switches for memory and neuromorphic applications.

Defect chemistry of donor-doped BaTiO3 with BaO-excess for reduction resistant PTCR thermistor applications - redox-behaviour.

The electrical conductivity of donor-doped BaTiO3 thermistor ceramics with excessive BaO revealing a reduction-persistent PTCR effect has been carefully examined depending on materials' composition and oxygen partial pressure at moderate temperatures between 973 and 1273 K. This thermal regime represents the range which is relevant for the realization of insulating grain boundaries in these electrically inhomogeneous ceramic materials through reoxidation. Based on the experimental results strong evidence for a general correlation between the PTCR characteristics, DC-conductivity and the herewith associated defect chemistry significant to thermistor applications is presented for the system (Ba, La)mTiO3, where m designates the BaO-excess (m≥ 1). Nominal compositions with a relatively low (Ba + La)/Ti ratio m only show a rather poor PTCR effect and an overall donor-type response in conductivity can be observed at all levels of oxygen partial pressure considered in the present study. With increasing (Ba + La)/Ti ratio m the nonlinear resistivity jump with increasing temperature strongly improves and the acceptor-type behaviour seems to dominate the total conductivity at partial pressures of oxygen above approximately 10-6 MPa. This result for compositions with high BaO-excess can be understood by the local formation of point defect associates in the grain boundary regions that consist of both acceptor-type titanium vacancies and donor-type oxygen vacancies. Their origin is attributed to the preferential local incorporation of excessive BaO into the BaTiO3 lattice at the intergranular interfaces.

On the role of the metal oxide/reactive electrode interface during the forming procedure of valence change ReRAM devices.

One of the key issues of resistive switching memory devices is the so called "forming" process, a one time process at a high voltage, which initializes the resistive switching at significantly lower voltages. With this study we identify the influence of the different layers - namely the insulating oxide layer (ZrO2 and Ta2O5) and the reactive ohmic electrode layer (Hf, Ta and Pt) - on the forming voltage and the pristine capacitance of the devices. For this, the forming voltage and pristine capacitance is measured in dependence of the oxide layer thickness with different electrodes. The different slopes of the forming voltage - thickness relation for different top electrodes give an indication that the reactive ohmic electrode is oxidized from the oxide layer underneath and that the degree of the oxidation depends on the thickness of the oxide layer as well as the materials used for the oxide and electrode layer. This finding could be confirmed by X-ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM) measurements. From the electrical measurements and the TEM images the thickness of the oxidized electrode layer could be estimated. The degree of the oxidation depends on the oxygen affinity of the oxide and electrode material. The interface dependent (thickness independent) part of the forming voltage is determined by the material of the electrode. The magnitude of this interface voltage could be correlated to the oxide free energy of the electrode material. These results can support the ongoing research towards resistive switching memory devices with a very low forming voltage or forming free behaviour.

Metallic filamentary conduction in valence change-based resistive switching devices: the case of TaOx thin film with x∼ 1.

The resistive switching in metal-oxide thin films typically occurs via modulation of the oxygen content in nano-sized conductive filaments. For Ta2O5-based resistive switching devices, the two current models consider filaments composed of oxygen vacancies and those containing metallic Ta clusters. The present work tries to resolve this dispute. The filaments in Ta2O5 were formerly shown to exhibit the same electrical transport mechanisms as TaOx thin films with x∼ 1.0. In this paper, sputtered thin films of pure ß-Ta and of TaOx with different oxygen concentrations are studied and compared in terms of their structure and electrical transport. The structural analysis reveals the presence of Ta clusters in the TaOx films. Identical electrical transport characteristics were observed in the TaOx films with x∼ 1.0 and in the ß-Ta film. Both show the same transport mechanism, a carrier concentration on the order of 1022 cm-3 and a positive magnetoresistance associated with weak antilocalization at T < 30 K. It is concluded that the electrical transport in the TaOx films with x∼ 1.0 is dominated by percolation through Ta clusters. This means that the transport in the filaments is also determined by percolation through Ta clusters, strongly supporting the metallic Ta filament model.

Topotactic Phase Transition Driving Memristive Behavior.

Redox-based memristive devices are one of the most attractive candidates for future nonvolatile memory applications and neuromorphic circuits, and their performance is determined by redox processes and the corresponding oxygen-ion dynamics. In this regard, brownmillerite SrFeO2.5 has been recently introduced as a novel material platform due to its exceptional oxygen-ion transport properties for resistive-switching memory devices. However, the underlying redox processes that give rise to resistive switching remain poorly understood. By using X-ray absorption spectromicroscopy, it is demonstrated that the reversible redox-based topotactic phase transition between the insulating brownmillerite phase, SrFeO2.5 , and the conductive perovskite phase, SrFeO3 , gives rise to the resistive-switching properties of SrFeOx memristive devices. Furthermore, it is found that the electric-field-induced phase transition spreads over a large area in (001) oriented SrFeO2.5 devices, where oxygen vacancy channels are ordered along the in-plane direction of the device. In contrast, (111)-grown SrFeO2.5 devices with out-of-plane oriented oxygen vacancy channels, reaching from the bottom to the top electrode, show a localized phase transition. These findings provide detailed insight into the resistive-switching mechanism in SrFeOx -based memristive devices within the framework of metal-insulator topotactic phase transitions.

Introduction to new memory paradigms: memristive phenomena and neuromorphic applications.

This article provides a brief introduction to the Faraday Discussion "New memory paradigms: memristive phenomena and neuromorphic applications" held in Aachen, Germany, 15-17 October 2018. It will cover basic definitions of memristive switching elements, their main switching modes, and their most important performance parameters as well as applications in neuromorphic computing. The article comprises parts from the following sources: General Introduction and Introduction to Part V of Nanoelectronics and Information Technology, ed. R. Waser, Wiley-VCH, 2012; Chapter 4 of Nanotechnology: Volume 3: Information Technology I, ed. R. Waser, Wiley-VCH, Weinheim, 2008; Chapters 3-9 of Emerging Nanoelectronic Devices, ed. A. Chen, J. Hutchby, V. Zhirnov and G. Bourianoff, Wiley, 2015; Chapter 1 of Resistive Switching, ed. D. Ielmini and R. Waser, Wiley-VCH, 2016 (with permission by Wiley-VCH).

In-Gap States and Band-Like Transport in Memristive Devices.

Point defects such as oxygen vacancies cause emergent phenomena such as resistive switching in transition-metal oxides, but their influence on the electron-transport properties is far from being understood. Here, we employ direct mapping of the electronic structure of a memristive device by spectromicroscopy. We find that oxygen vacancies result in in-gap states that we use as input for single-band transport simulations. Because the in-gap states are situated below the Fermi level, they do not contribute to the current directly but impact the shape of the conduction band. Accordingly, we can describe our devices with band-like transport and tunneling across the Schottky barrier at the interface.