79,350 research outputs found
SPICE model of memristive devices with threshold
Although memristive devices with threshold voltages are the norm rather than
the exception in experimentally realizable systems, their SPICE programming is
not yet common. Here, we show how to implement such systems in the SPICE
environment. Specifically, we present SPICE models of a popular
voltage-controlled memristive system specified by five different parameters for
PSPICE and NGSPICE circuit simulators. We expect this implementation to find
widespread use in circuits design and testing
Effects of large field cutoffs in scalar and gauge models
We discuss the notion of a large field cutoff for lattice gauge models with
compact groups. We propose and compare gauge invariant and gauge dependent (in
the Landau gauge) criteria to sort the configurations into ``large-field'' and
``small-field'' configurations. We show that the correlations between volume
average of field size indicators and the behavior of the tail of the
distribution are very different in the gauge and scalar cases. We show that the
effect of discarding the large field configurations on the plaquette average is
very different above, below and near beta=5.6 for a pure SU(3) LGT.Comment: Lattice2004(theory
On the validity of memristor modeling in the neural network literature
An analysis of the literature shows that there are two types of
non-memristive models that have been widely used in the modeling of so-called
"memristive" neural networks. Here, we demonstrate that such models have
nothing in common with the concept of memristive elements: they describe either
non-linear resistors or certain bi-state systems, which all are devices without
memory. Therefore, the results presented in a significant number of
publications are at least questionable, if not completely irrelevant to the
actual field of memristive neural networks
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
Memcomputing: a computing paradigm to store and process information on the same physical platform
In present day technology, storing and processing of information occur on
physically distinct regions of space. Not only does this result in space
limitations; it also translates into unwanted delays in retrieving and
processing of relevant information. There is, however, a class of two-terminal
passive circuit elements with memory, memristive, memcapacitive and
meminductive systems -- collectively called memelements -- that perform both
information processing and storing of the initial, intermediate and final
computational data on the same physical platform. Importantly, the states of
these memelements adjust to input signals and provide analog capabilities
unavailable in standard circuit elements, resulting in adaptive circuitry, and
providing analog massively-parallel computation. All these features are
tantalizingly similar to those encountered in the biological realm, thus
offering new opportunities for biologically-inspired computation. Of particular
importance is the fact that these memelements emerge naturally in nanoscale
systems, and are therefore a consequence and a natural by-product of the
continued miniaturization of electronic devices. We will discuss the various
possibilities offered by memcomputing, discuss the criteria that need to be
satisfied to realize this paradigm, and provide an example showing the solution
of the shortest-path problem and demonstrate the healing property of the
solution path.Comment: The first part of this paper has been published in Nature Physics 9,
200-202 (2013). The second part has been expanded and is now included in
arXiv:1304.167
On the physical properties of memristive, memcapacitive, and meminductive systems
We discuss the physical properties of realistic memristive, memcapacitive and
meminductive systems. In particular, by employing the well-known theory of
response functions and microscopic derivations, we show that resistors,
capacitors and inductors with memory emerge naturally in the response of
systems - especially those of nanoscale dimensions - subjected to external
perturbations. As a consequence, since memristances, memcapacitances, and
meminductances are simply response functions, they are not necessarily finite.
This means that, unlike what has always been argued in some literature,
diverging and non-crossing input-output curves of all these memory elements are
physically possible in both quantum and classical regimes. For similar reasons,
it is not surprising to find memcapacitances and meminductances that acquire
negative values at certain times during dynamics, while the passivity criterion
of memristive systems imposes always a non-negative value on the resistance at
any given time. We finally show that ideal memristors, namely those whose state
depends only on the charge that flows through them (or on the history of the
voltage) are subject to very strict physical conditions and are unable to
protect their memory state against the unavoidable fluctuations, and therefore
are susceptible to a stochastic catastrophe. Similar considerations apply to
ideal memcapacitors and meminductors
- …