Electron Beam Effects on Oxide Thin Films-Structure and Electrical Property Correlations.

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.

Unsupervised Hebbian learning experimentally realized with analogue memristive crossbar arrays.

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.

Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition.

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/Al2O3/Nb x O y /Au and are fabricated on a 4-inch wafer. The key functional ingredients of the devices are a 1.3 nm thick Al2O3 tunnel barrier and a 2.5 mm thick Nb x O y 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 I-V non-linearity might avoid the need for selector devices in crossbar array structures.

Memristive stochastic plasticity enables mimicking of neural synchrony: Memristive circuit emulates an optical illusion.

The human brain is able to integrate a myriad of information in an enormous and massively parallel network of neurons that are divided into functionally specialized regions such as the visual cortex, auditory cortex, or dorsolateral prefrontal cortex. Each of these regions participates as a context-dependent, self-organized, and transient subnetwork, which is shifted by changes in attention every 0.5 to 2 s. This leads to one of the most puzzling issues in cognitive neuroscience, well known as the "binding problem." The concept of neural synchronization tries to explain the problem by encoding information using coherent states, which temporally patterns neural activity. We show that memristive devices, that is, a two-terminal variable resistor that changes its resistance depending on the previous charge flow, allow a new degree of freedom for this concept: a local memory that supports transient connectivity patterns in oscillator networks. On the basis of the probability distribution of the resistance switching process of Ag-doped titanium dioxide memristive devices, a local plasticity model is proposed, which causes an autonomous phase and frequency locking in an oscillator network. To illustrate the performance of the proposed computing paradigm, the temporal binding problem is investigated in a network of memristively coupled self-sustained van der Pol oscillators. We show evidence that the implemented network allows achievement of the transition from asynchronous to multiple synchronous states, which opens a new pathway toward the construction of cognitive electronics.

The role of ion transport phenomena in memristive double barrier devices.

In this work we report on the role of ion transport for the dynamic behavior of a double barrier quantum mechanical Al/Al2O3/NbxOy/Au memristive device based on numerical simulations in conjunction with experimental measurements. The device consists of an ultra-thin NbxOy solid state electrolyte between an Al2O3 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 NbxOy 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.

A memristive spiking neuron with firing rate coding.

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.

Memristive Hebbian plasticity model: device requirements for the emulation of Hebbian plasticity based on memristive devices.

In this work we present a phenomenological model for synaptic plasticity suitable to describe common plasticity measurements of memristive devices. We show evidence that the presented model is basically compatible with advanced biophysical plasticity models, which account for a large body of experimental data on spike-timing-depending plasticity (STDP) as an asymmetric form of Hebbian learning. The basic characteristics of our model are a saturation of the synaptic weight growth and a weight dependent learning rate. Moreover, it accounts for common resistive switching behaviors of memristive devices under voltage pulse application and allows to study essential requirements of individual memristive devices for the emulation of Hebbian plasticity in neuromorphic circuits. In this respect, memristive devices based on mixed ionic/electronic and one exclusively electronic mechanism are explored. The ionic/electronic devices consist of the layer sequence metal/isolator/metal and represent today's most popular devices. The electronic device is a MemFlash-cell which is based on a conventional floating gate transistor in a diode configuration wiring scheme exhibiting a memristive (pinched) I-V characteristic.

Bipolar switching polarity reversal by electrolyte layer sequence in electrochemical metallization cells with dual-layer solid electrolytes.

Bipolar switching behaviours of electrochemical metallization (ECM) cells with dual-layer solid electrolytes (SiOx-Ge0.3Se0.7) were analyzed. Type 1 ECM cell, Pt (bottom electrode)/SiOx/Ge0.3Se0.7/Cu (top electrode), exhibited typical eightwise current-voltage (I-V) hysteresis of ECM cells whereas Type 2 ECM cell, Pt (bottom electrode)/Ge0.3Se0.7/SiOx/Cu(top electrode), showed counter-eightwise hysteresis. In addition, absolute off-switching voltage in Type 2 cell is lower than that in Type 1 cell while on-switching voltage in both cells is almost the same. An attempt to understand this electrolyte-stack-sequence-depending switching polarity reversal was made in terms of the ECM cell potential change upon the electrolyte stack sequence and the consequent change in Cu filament growth direction. Relevant experimental evidence for the hypothesis was obtained regarding the switching behaviours. Furthermore, given the switching polarity reversal, feasibility of serial complementary resistive switches was also demonstrated.