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

Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. © 2020, The Author(s)

Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices

Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells

Fremtidig luftkvalitet i danske byer - effekter af skærpede emissionsnormer

Omfattende beregninger med en række luftkvalitetsmodeller udviklet af Danmarks Miljøundersøgelser viser, at den regionale baggrundsforurening uden for byerne, bybaggrundsforureningen over byerne og luftkvaliteten i gadeniveau bliver bedre i fremtiden. Dette skyldes især EU’s skærpede regulering af køretøjers emission. EU’s nye grænseværdier for kvælstofdioxid (NO2), kulilte (CO) og benzen gældende for 2010 forventes ikke at blive overskredet. Ozonniveauerne forventes at stige lidt, fordi begrænsningen i bilernes emission af kvælstofmonoxid (NO) betyder, at mindre ozon fjernes i reaktioner med NO i dannelsen af NO2. Det er endnu ikke muligt at modellere partikler. Ud fra foreløbige vurderinger er der usikkerhed om, hvorvidt EU’s grænseværdi for partikler kan overholdes i 2010. Grænseværdierne er opstillet for at beskytte befolkningens sundhed

Slow-fast dynamics in a chaotic system with strongly asymmetric memristive element

We investigate the effect of a memristive element on the dynamics of a chaotic system. For this purpose, the chaotic Chua’s oscillator is extended by a memory element in the form of a double-barrier memristive device. The device consists of Au/NbOx/Al2O3/Al/Nb layers and exhibits strong analog-type resistive changes depending on the history of the charge flow. In the obtained system we observe strong changes in the dynamics of chaotic oscillations. The otherwise fluctuating amplitudes of Chua’s system are disrupted by transient silent states. Numerical simulations and analysis of the extended model reveal that the underlying dynamics possesses slow–fast properties due to different timescales between the memory element and the base system. Furthermore, the stabilizing and destabilizing dynamic bifurcations are identified that are traversed by the system during its chaotic behavior

Analoge und Digitale Memristive Bauelemente basierend auf Hafniumoxid für Neuromorphe Anwendungen

Memristive devices are extensively investigated as non-volatile memory elements due to their variable resistance that is adjustable by electrical stimuli. They are promising for, e.g., neuromorphic circuits, which aim to replicate biological data processing to extend the computing capabilities of today's digital information technology, especially for data-intensive tasks. This dissertation focuses on analog interface-type devices and digital filamentary-type devices based on binary metal oxides. The analog devices are fabricated on 100 mm wafers, the current-voltage (I-V) characteristics are statistically evaluated, and the engineering of different read-out and switching parameters by up to several orders of magnitude is presented. The plasma conditions of the sputter deposition process are probed, and material properties are investigated by transmission electron microscopy (TEM), electron energy loss spectroscopy (EELS), as well as synchrotron-based X-ray photoelectron spectroscopy (XPS) and depth-dependent hard X-ray photoelectron spectroscopy (HAXPES) on functional devices. A device model is deduced that considers electron trapping/de-trapping within HfO2 as the switching mechanism that modulates a Schottky barrier. The spectroscopic evidence for the electronic switching mechanism in interface-type memristive devices should be emphasized. The utilized digital resistive random access memory (RRAM) devices were developed at the IHP in Frankfurt/Oder (Germany). The inherent randomness of the switching process is exploited to emulate stochastic synaptic plasticity in neuromorphic networks to learn images with novel supervised and unsupervised algorithms, partly realized with hardware synapses. It is further shown that the devices can potentially be used for computing matrix-vector-multiplications (MVMs) directly in-memory when storing pre-trained synaptic weights of an artificial neural network (ANN)

Pattern recognition with TiO_x-based memristive devices

We report on the development of TiOx-based memristive devices for bio-inspired neuromorphic systems. In particular, capacitor like structures of Al/AlOx/TiOx/Al with, respectively 20 nm and 50 nm thick TiOx-layers were fabricated and analyzed in terms of their use in neural network circuits. Therefore, an equivalent circuit model is presented which mimics the observed device properties on a qualitative level and relies on mobile oxygen ions by taking electronic transport through local conducting filaments and hopping between TiOx defect states into account. The model also comprises back diffusion of oxygen ions and allows for a realistic description of the experimental recorded device characteristics. The in Refs. [1-3] reported computing paradigms for pattern recognition have been used as guidelines for a device performance investigation at the network level. In particular, simulations of a spiking neural network are presented which allows for pattern recognition. As input patterns hand written digits taken from the MNIST Data base have been used. Within the network the memristive devices are arranged in a cross-bar array connected by 196 input neurons and ten output neurons. While, each input neuron corresponds to a specific pixel of the image of the input pattern, the output neurons were implemented as spiking neurons. In addition, the output neurons were inhibitory linked within an winner-take-it-all network and consist of a homeostasis-like behavior for their spiking thresholds. Based on the network simulation essential requirements for the development of optimal memristive device for neuromorphic circuits are discussed

Impedance Spectroscopy on Hafnium Oxide‐Based Memristive Devices

Abstract Memristive devices for neuromorphic computing have been attracting ever growing attention over the last couple of years. In neuromorphic electronics, memristive devices with multi‐level resistance states are required to accurately reproduce synaptic weights. Here, a memristive device based on a multilayer oxide system (Nb/NbOx/Al2O3/HfO2/Au), which features a filamentary‐free, homogenous interfacial resistive switching mechanism, is investigated. To gain a deeper insight into the switching mechanism, impedance spectroscopy (ImpSpec), X‐ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM) are exploited. While this work focuses on the analysis of impedance and current‐voltage characteristics, XPS and TEM investigations can be found in a companion paper (Zahari et al.). In the course of this investigation, potentiodynamic impedance spectroscopy (PD‐ImpSpec) and time resolved impedance spectroscopy (TR‐ImpSpec) in combination with transient analysis are used. Evidence is presented of switching kinetics at voltages above 2.1 V directly related to changes in Schottky barrier resistance. These switching kinetics can in turn be interpreted by the charging and discharging of double positively charged oxygen vacancies VO+2${f V}_{f O}^{{m{ + }}2}$ ≈ 0.9 eV. The results of the impedance analysis are translated into a more general model for memristive devices to map the physical processes during switching