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In situ transmission electron microscope (TEM) characterization techniques provide valuable information on structure–property correlations to understand the behavior of materials at the nanoscale. However, understanding nanoscale structures and their interaction with the electron beam is pivotal for the reliable interpretation of in situ/ex situ TEM studies. Here, we report that oxides commonly used in nanoelectronic applications, such as transistor gate oxides or memristive devices, are prone to electron beam induced damage that causes small structural changes even under very low dose conditions, eventually changing their electrical properties as examined via in situ measurements. In this work, silicon, titanium, and niobium oxide thin films are used for in situ TEM electrical characterization studies. The electron beam induced reduction of the oxides turns these insulators into conductors. The conductivity change is reversible by exposure to air, supporting the idea of electron beam reduction of oxides as primary damage mechanism. Through these measurements we propose a limit for the critical dose to be considered for in situ scanning electron microscopy and TEM characterization studies.

Conventional transistor electronics are reaching their limits in terms of scalability, power dissipation, and the underlying Boolean system architecture. To overcome this obstacle neuromorphic analogue systems are recently highly investigated. Particularly, the use of memristive devices in VLSI analogue concepts provides a promising pathway to realize novel bio-inspired computing architectures, which are able to unravel the foreseen difficulties of traditional electronics. Currently, a variety of materials and device structures are being studied along with novel computing schemes to make use of the attractive features of memristive devices for neuromorphic computing. However, a number of obstacles still have to be overcome to cast memristive devices into hardware systems. Most important is a physical implementation of memristive devices, which can cope with the high complexity of neural networks. This includes the integration of analogue and electroforming-free memristive devices into crossbar structures with no additional electronic components, such as selector devices. Here, an unsupervised, bio-motivated Hebbian based learning platform for visual pattern recognition is presented. The heart of the system is a crossbar array (16 × 16) which consists of selector-free and forming-free (non-filamentary) memristive devices, which exhibit analogue I-V characteristics.

In this work we report on the role of ion transport for the dynamic behavior of a double barrier quantum mechanical Al/Al 2 O 3 /Nb x O y /Au memristive device based on numerical simulations in conjunction with experimental measurements. The device consists of an ultra-thin Nb x O y solid state electrolyte between an Al 2 O 3 tunnel barrier and a semiconductor metal interface at an Au electrode. It is shown that the device provides a number of interesting features such as an intrinsic current compliance, a relatively long retention time, and no need for an initialization step. Therefore, it is particularly attractive for applications in highly dense random access memories or neuromorphic mixed signal circuits. However, the underlying physical mechanisms of the resistive switching are still not completely understood yet. To investigate the interplay between the current transport mechanisms and the inner atomistic device structure a lumped element circuit model is consistently coupled with 3D kinetic Monte Carlo model for the ion transport. The simulation results indicate that the drift of charged point defects within the Nb x O y is the key factor for the resistive switching behavior. It is shown in detail that the diffusion of oxygen modifies the local electronic interface states resulting in a change of the interface properties.

The use of interface-based resistive switching devices for neuromorphic computing is investigated. In a combined experimental and numerical study, the important device parameters and their impact on a neuromorphic pattern recognition system are studied. The memristive cells consist of a layer sequence Al/Al O /Nb O /Au and are fabricated on a 4-inch wafer. The key functional ingredients of the devices are a 1.3 nm thick Al O tunnel barrier and a 2.5 mm thick Nb O memristive layer. Voltage pulse measurements are used to study the electrical conditions for the emulation of synaptic functionality of single cells for later use in a recognition system. The results are evaluated and modeled in the framework of the plasticity model of Ziegler et al. Based on this model, which is matched to experimental data from 84 individual devices, the network performance with regard to yield, reliability, and variability is investigated numerically. As the network model, a computing scheme for pattern recognition and unsupervised learning based on the work of Querlioz et al. (2011), Sheridan et al. (2014), Zahari et al. (2015) is employed. This is a two-layer feedforward network with a crossbar array of memristive devices, leaky integrate-and-fire output neurons including a winner-takes-all strategy, and a stochastic coding scheme for the input pattern. As input pattern, the full data set of digits from the MNIST database is used. The numerical investigation indicates that the experimentally obtained yield, reliability, and variability of the memristive cells are suitable for such a network. Furthermore, evidence is presented that their strong - non-linearity might avoid the need for selector devices in crossbar array structures.

Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2) and on the chemical electromigration cell Ag/TiO2-x /Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

The massive parallel approach of neuromorphic circuits leads to effective methods for solving complex problems. It has turned out that resistive switching devices with a continuous resistance range are potential candidates for such applications. These devices are memristive systems - nonlinear resistors with memory. They are fabricated in nanotechnology and hence parameter spread during fabrication may aggravate reproducible analyses. This issue makes simulation models of memristive devices worthwhile. Kinetic Monte-Carlo simulations based on a distributed model of the device can be used to understand the underlying physical and chemical phenomena. However, such simulations are very time-consuming and neither convenient for investigations of whole circuits nor for real-time applications, e.g. emulation purposes. Instead, a concentrated model of the device can be used for both fast simulations and real-time applications, respectively. We introduce an enhanced electrical model of a valence change mechanism (VCM) based double barrier memristive device (DBMD) with a continuous resistance range. This device consists of an ultra-thin memristive layer sandwiched between a tunnel barrier and a Schottky-contact. The introduced model leads to very fast simulations by using usual circuit simulation tools while maintaining physically meaningful parameters. Kinetic Monte-Carlo simulations based on a distributed model and experimental data have been utilized as references to verify the concentrated model.

In this work we report on kinetic Monte-Carlo calculations of resistive switching and the underlying growth dynamics of filaments in an electrochemical metallization device consisting of an Ag/TiO2/Pt sandwich-like thin film system. The developed model is not limited to i) fast time scale dynamics and ii) only one growth and dissolution cycle of metallic filaments. In particular, we present results from the simulation of consecutive cycles. We find that the numerical results are in excellent agreement with experimentally obtained data. Additionally, we observe an unexpected filament growth mode which is in contradiction to the widely acknowledged picture of filament growth, but consistent with recent experimental findings.

In this work we report on the role of ion transport for the dynamic behavior of a double barrier quantum mechanical Al/Al\\(_2\\)O\\(_3\\)/Nb\\(_{\\text{x}}\\)O\\(_{\\text{y}}\\)/Au memristive device based on numerical simulations in conjunction with experimental measurements. The device consists of an ultra-thin Nb\\(_{\\text{x}}\\)O\\(_{\\text{y}}\\) solid state electrolyte between an Al\\(_2\\)O\\(_3\\) tunnel barrier and a semiconductor metal interface at an Au electrode. It is shown that the device provides a number of interesting features for potential applications such as an intrinsic current compliance, a relatively long retention time, and no need for an initialization step. Therefore, it is particularly attractive for applications in highly dense random access memories or neuromorphic mixed signal circuits. However, the underlying physical mechanisms of the resistive switching are still not completely understood yet. To investigate the interplay between the current transport mechanisms and the inner atomistic device structure a lumped element circuit model is consistently coupled with 3D kinetic Monte Carlo model for the ion transport. The simulation results indicate that the drift of charged point defects within the Nb\\(_{\\text{x}}\\)O\\(_{\\text{y}}\\) is the key factor for the resistive switching behavior. It is shown in detail that the diffusion of oxygen modifies the local electronic interface states resulting in a change of the interface properties of the double barrier device.

Memristors based on a double barrier design have been analysed by various nano spectroscopic methods to unveil details about its microstructure and conduction mechanism. The device consists of an AlOx tunnel barrier and a NbOy/Au Schottky barrier sandwiched between Nb bottom electrode and Au top electrode. As it was anticipated that the local chemical composition of the tunnel barrier, i.e. oxidation state of the metals as well as concentration and distribution of oxygen ions, have a major influence on electronic conduction, these factors were carefully analysed. A combined approach was chosen in order to reliably investigate electronic states of Nb and O by electron energy-loss spectroscopy as well as map elements whose transition edges exhibit a different energy range by energy-dispersive X-ray spectroscopy like Au and Al. The results conclusively demonstrate significant oxidation of the bottom electrode as well as a small oxygen vacancy concentration in the Al oxide tunnel barrier. Possible scenarios to explain this unexpected additional oxide layer are discussed and kinetic Monte Carlo simulations were applied in order to identify its influence on conduction mechanisms in the device. In light of the strong deviations between observed and originally sought layout, this study highlights the robustness in terms of structural deviations of the double barrier memristor device.