290 research outputs found
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Teaching Memory Circuit Elements via Experiment-Based Learning
The class of memory circuit elements which comprises memristive,
memcapacitive, and meminductive systems, is gaining considerable attention in a
broad range of disciplines. This is due to the enormous flexibility these
elements provide in solving diverse problems in analog/neuromorphic and
digital/quantum computation; the possibility to use them in an integrated
computing-memory paradigm, massively-parallel solution of different
optimization problems, learning, neural networks, etc. The time is therefore
ripe to introduce these elements to the next generation of physicists and
engineers with appropriate teaching tools that can be easily implemented in
undergraduate teaching laboratories. In this paper, we suggest the use of
easy-to-build emulators to provide a hands-on experience for the students to
learn the fundamental properties and realize several applications of these
memelements. We provide explicit examples of problems that could be tackled
with these emulators that range in difficulty from the demonstration of the
basic properties of memristive, memcapacitive, and meminductive systems to
logic/computation and cross-bar memory. The emulators can be built from
off-the-shelf components, with a total cost of a few tens of dollars, thus
providing a relatively inexpensive platform for the implementation of these
exercises in the classroom. We anticipate that this experiment-based learning
can be easily adopted and expanded by the instructors with many more case
studies.Comment: IEEE Circuits and Systems Magazine (in press
Robust Simulation of a TaO Memristor Model
This work presents a continuous and differentiable approximation of a Tantalum oxide memristor model which is suited for robust numerical simulations in software. The original model was recently developed at Hewlett Packard labs on the basis of experiments carried out on a memristor manufactured in house. The Hewlett Packard model of the nano-scale device is accurate and may be taken as reference for a deep investigation of the capabilities of the memristor based on Tantalum oxide. However, the model contains discontinuous and piecewise differentiable functions respectively in state equation and Ohm's based law. Numerical integration of the differential algebraic equation set may be significantly facilitated under substitution of these functions with appropriate continuous and differentiable approximations. A detailed investigation of classes of possible continuous and differentiable kernels for the approximation of the discontinuous and piecewise differentiable functions in the original model led to the choice of near optimal candidates. The resulting continuous and differentiable DAE set captures accurately the dynamics of the original model, delivers well-behaved numerical solutions in software, and may be integrated into a commercially-available circuit simulator
The Fourth Element: Characteristics, Modelling, and Electromagnetic Theory of the Memristor
In 2008, researchers at HP Labs published a paper in {\it Nature} reporting
the realisation of a new basic circuit element that completes the missing link
between charge and flux-linkage, which was postulated by Leon Chua in 1971. The
HP memristor is based on a nanometer scale TiO thin-film, containing a
doped region and an undoped region. Further to proposed applications of
memristors in artificial biological systems and nonvolatile RAM (NVRAM), they
also enable reconfigurable nanoelectronics. Moreover, memristors provide new
paradigms in application specific integrated circuits (ASICs) and field
programmable gate arrays (FPGAs). A significant reduction in area with an
unprecedented memory capacity and device density are the potential advantages
of memristors for Integrated Circuits (ICs). This work reviews the memristor
and provides mathematical and SPICE models for memristors. Insight into the
memristor device is given via recalling the quasi-static expansion of Maxwell's
equations. We also review Chua's arguments based on electromagnetic theory.Comment: 28 pages, 14 figures, Accepted as a regular paper - the Proceedings
of Royal Society
First order devices, hybrid memristors, and the frontiers of nonlinear circuit theory
Several devices exhibiting memory effects have shown up in nonlinear circuit
theory in recent years. Among others, these circuit elements include Chua's
memristors, as well as memcapacitors and meminductors. These and other related
devices seem to be beyond the, say, classical scope of circuit theory, which is
formulated in terms of resistors, capacitors, inductors, and voltage and
current sources. We explore in this paper the potential extent of nonlinear
circuit theory by classifying such mem-devices in terms of the variables
involved in their constitutive relations and the notions of the differential-
and the state-order of a device. Within this framework, the frontier of first
order circuit theory is defined by so-called hybrid memristors, which are
proposed here to accommodate a characteristic relating all four fundamental
circuit variables. Devices with differential order two and mem-systems are
discussed in less detail. We allow for fully nonlinear characteristics in all
circuit elements, arriving at a rather exhaustive taxonomy of C^1-devices.
Additionally, we extend the notion of a topologically degenerate configuration
to circuits with memcapacitors, meminductors and all types of memristors, and
characterize the differential-algebraic index of nodal models of such circuits.Comment: Published in 2013. Journal reference included as a footnote in the
first pag
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
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