127 research outputs found
Memristive switching of MgO based magnetic tunnel junctions
Here we demonstrate that both, tunnel magneto resistance (TMR) and resistive
switching (RS), can be observed simultaneously in nano-scale magnetic tunnel
junctions. The devices show bipolar RS of 6 % and TMR ratios of about 100 %.
For each magnetic state, multiple resistive sates are created depending on the
bias history which provides a method for multi-bit data storage and logic. The
electronic transport measurements are discussed in the framework of a
memristive system. Differently prepared MgO barriers are compared to gain
insight into the switching mechanism
Computational Capacity and Energy Consumption of Complex Resistive Switch Networks
Resistive switches are a class of emerging nanoelectronics devices that
exhibit a wide variety of switching characteristics closely resembling
behaviors of biological synapses. Assembled into random networks, such
resistive switches produce emerging behaviors far more complex than that of
individual devices. This was previously demonstrated in simulations that
exploit information processing within these random networks to solve tasks that
require nonlinear computation as well as memory. Physical assemblies of such
networks manifest complex spatial structures and basic processing capabilities
often related to biologically-inspired computing. We model and simulate random
resistive switch networks and analyze their computational capacities. We
provide a detailed discussion of the relevant design parameters and establish
the link to the physical assemblies by relating the modeling parameters to
physical parameters. More globally connected networks and an increased network
switching activity are means to increase the computational capacity linearly at
the expense of exponentially growing energy consumption. We discuss a new
modular approach that exhibits higher computational capacities and energy
consumption growing linearly with the number of networks used. The results show
how to optimize the trade-off between computational capacity and energy
efficiency and are relevant for the design and fabrication of novel computing
architectures that harness random assemblies of emerging nanodevices
A Compact Model of Interface-Type Memristors Linking Physical and Device Properties
Memristors are an electronic device whose resistance depends on the voltage
history that has been applied to its two terminals. Despite its clear advantage
as a computational element, a suitable transport model is lacking for the
special class of interface-based memristors. Here, we adapt the widely-used
Yakopcic compact model by including transport equations relevant to
interface-type memristors. This model is able to reproduce the qualitative
behaviour measured upon Nb-doped SrTiO memristive devices. Our analysis
demonstrates a direct correlation between the devices' characteristic
parameters and those of our model. The model can clearly identify the charge
transport mechanism in different resistive states thus facilitating evaluation
of the relevant parameters pertaining to resistive switching in interface-based
memristors. One clear application of our study is its ability to inform the
design and fabrication of related memristive devices.Comment: 14 pages, 2 pages of Supplementary Data, 4 figures, 4 table
Lagrange formalism of memory circuit elements: classical and quantum formulations
The general Lagrange-Euler formalism for the three memory circuit elements,
namely, memristive, memcapacitive, and meminductive systems, is introduced. In
addition, {\it mutual meminductance}, i.e. mutual inductance with a state
depending on the past evolution of the system, is defined. The Lagrange-Euler
formalism for a general circuit network, the related work-energy theorem, and
the generalized Joule's first law are also obtained. Examples of this formalism
applied to specific circuits are provided, and the corresponding Hamiltonian
and its quantization for the case of non-dissipative elements are discussed.
The notion of {\it memory quanta}, the quantum excitations of the memory
degrees of freedom, is presented. Specific examples are used to show that the
coupling between these quanta and the well-known charge quanta can lead to a
splitting of degenerate levels and to other experimentally observable quantum
effects
Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications
In the last decade a large scientific community has focused on the study of the
memristor. The memristor is thought to be by many the best alternative to CMOS
technology, which is gradually showing its flaws. Transistor technology has developed
fast both under a research and an industrial point of view, reducing the
size of its elements to the nano-scale. It has been possible to generate more and
more complex machinery and to communicate with that same machinery thanks
to the development of programming languages based on combinations of boolean
operands. Alas as shown by Moore’s law, the steep curve of implementation and
of development of CMOS is gradually reaching a plateau. It is clear the need of
studying new elements that can combine the efficiency of transistors and at the same
time increase the complexity of the operations.
Memristors can be described as non-linear resistors capable of maintaining
memory of the resistance state that they reached. From their first theoretical treatment
by Professor Leon O. Chua in 1971, different research groups have devoted their
expertise in studying the both the fabrication and the implementation of this new
promising technology. In the following thesis a complete study on memristors
and memristive elements is presented. The road map that characterizes this study
departs from a deep understanding of the physics that govern memristors, focusing
on the HP model by Dr. Stanley Williams. Other devices such as phase change
memories (PCMs) and memristive biosensors made with Si nano-wires have been
studied, developing emulators and equivalent circuitry, in order to describe their
complex dynamics. This part sets the first milestone of a pathway that passes trough
more complex implementations such as neuromorphic systems and neural networks
based on memristors proving their computing efficiency. Finally it will be presented
a memristror-based technology, covered by patent, demonstrating its efficacy for
clinical applications. The presented system has been designed for detecting and
assessing automatically chronic wounds, a syndrome that affects roughly 2% of
the world population, through a Cellular Automaton which analyzes and processes
digital images of ulcers. Thanks to its precision in measuring the lesions the proposed
solution promises not only to increase healing rates, but also to prevent the worsening
of the wounds that usually lead to amputation and death
Applications of memristors in conventional analogue electronics
This dissertation presents the steps employed to activate and utilise analogue memristive devices in conventional analogue circuits and beyond.
TiO2 memristors are mainly utilised in this study, and their large variability in operation in between similar devices is identified.
A specialised memristor characterisation instrument is designed and built to mitigate this issue and to allow access to large numbers of devices at a time.
Its performance is quantified against linear resistors, crossbars of linear resistors, stand-alone memristive elements and crossbars of memristors.
This platform allows for a wide range of different pulsing algorithms to be applied on individual devices, or on crossbars of memristive elements, and is used throughout this dissertation.
Different ways of achieving analogue resistive switching from any device state are presented.
Results of these are used to devise a state-of-art biasing parameter finder which automatically extracts pulsing parameters that induce repeatable analogue resistive switching.
IV measurements taken during analogue resistive switching are then utilised to model the internal atomic structure of two devices, via fittings by the Simmons tunnelling barrier model.
These reveal that voltage pulses modulate a nano-tunnelling gap along a conical shape.
Further retention measurements are performed which reveal that under certain conditions, TiO2 memristors become volatile at short time scales.
This volatile behaviour is then implemented into a novel SPICE volatile memristor model.
These characterisation methods of solid-state devices allowed for inclusion of TiO2 memristors in practical electronic circuits.
Firstly, in the context of large analogue resistive crossbars, a crosspoint reading method is analysed and improved via a 3-step technique.
Its scaling performance is then quantified via SPICE simulations.
Next, the observed volatile dynamics of memristors are exploited in two separate sequence detectors, with applications in neuromorphic engineering.
Finally, the memristor as a programmable resistive weight is exploited to synthesise a memristive programmable gain amplifier and a practical memristive automatic gain control circuit.Open Acces
Electrical Characterization of Spherical Copper Oxide Memristive Array Sensors
A new System Protection (SP) technology is explored by using electrical and mechanical interference-sensing devices that are implemented with granular memristive material. The granular materials consist of oxide-coated copper spheres with radii of about 700 µm that are placed in contact to produce thin oxide junctions which exhibit memristive behavior. Processes for etching, which compared acetic acid and nitric acid etches, and thermal oxidation at 100°C are performed and compared to produce copper spheres with a copper oxide layer over the sphere surface. Oxidized copper spheres are tested as sensor arrays by loading into a capillary tube in an aligned arrangement. The spheres are held in contact to characterize current-voltage behavior for various oxide thicknesses with typical ROFF values in the megaohm range. Electrical characterization of the oxidized copper spheres reveal directly proportional changes to current-voltage hyseteresis in µW under compressive forces. The thinnest oxide exhibited changes of 8.3 to 21.2 µW over 9 mN while the thickest had a response from 0.4 to 2.5 µW over 22.3 mN
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