Chaotic memristor

We suggest and experimentally demonstrate a chaotic memory resistor (memristor). The core of our approach is to use a resistive system whose equations of motion for its internal state variables are similar to those describing a particle in a multi-well potential. Using a memristor emulator, the chaotic memristor is realized and its chaotic properties are measured. A Poincar\'{e} plot showing chaos is presented for a simple nonautonomous circuit involving only a voltage source directly connected in series to a memristor and a standard resistor. We also explore theoretically some details of this system, plotting the attractor and calculating Lyapunov exponents. The multi-well potential used resembles that of many nanoscale memristive devices, suggesting the possibility of chaotic dynamics in other existing memristive systems.Comment: Applied Physics A (in press

Molecular dynamics simulations of oxide memristors: thermal effects

We have extended our recent molecular-dynamic simulations of memristors to include the effect of thermal inhomogeneities on mobile ionic species appearing during operation of the device. Simulations show a competition between an attractive short-ranged interaction between oxygen vacancies and an enhanced local temperature in creating/destroying the conducting oxygen channels. Such a competition would strongly affect the performance of the memristive devices.Comment: submit/0169777; 6 pages, 4 figure

Evolving unipolar memristor spiking neural networks

© 2015 Taylor & Francis. Neuromorphic computing – brain-like computing in hardware – typically requires myriad complimentary metal oxide semiconductor spiking neurons interconnected by a dense mesh of nanoscale plastic synapses. Memristors are frequently cited as strong synapse candidates due to their statefulness and potential for low-power implementations. To date, plentiful research has focused on the bipolar memristor synapse, which is capable of incremental weight alterations and can provide adaptive self-organisation under a Hebbian learning scheme. In this paper, we consider the unipolar memristor synapse – a device capable of non-Hebbian switching between only two states (conductive and resistive) through application of a suitable input voltage – and discuss its suitability for neuromorphic systems. A self-adaptive evolutionary process is used to autonomously find highly fit network configurations. Experimentation on two robotics tasks shows that unipolar memristor networks evolve task-solving controllers faster than both bipolar memristor networks and networks containing constant non-plastic connections whilst performing at least comparably

Memristive devices for computing

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. \ua9 2013 Macmillan Publishers Limited. All rights reserved.Peer reviewed: YesNRC publication: Ye

Mixed-Signal Neuromorphic Inference Accelerators: Recent Results and Future Prospects

International audienc