1,348 research outputs found
Filamentary Switching: Synaptic Plasticity through Device Volatility
Replicating the computational functionalities and performances of the brain
remains one of the biggest challenges for the future of information and
communication technologies. Such an ambitious goal requires research efforts
from the architecture level to the basic device level (i.e., investigating the
opportunities offered by emerging nanotechnologies to build such systems).
Nanodevices, or, more precisely, memory or memristive devices, have been
proposed for the implementation of synaptic functions, offering the required
features and integration in a single component. In this paper, we demonstrate
that the basic physics involved in the filamentary switching of electrochemical
metallization cells can reproduce important biological synaptic functions that
are key mechanisms for information processing and storage. The transition from
short- to long-term plasticity has been reported as a direct consequence of
filament growth (i.e., increased conductance) in filamentary memory devices. In
this paper, we show that a more complex filament shape, such as dendritic paths
of variable density and width, can permit the short- and long-term processes to
be controlled independently. Our solid-state device is strongly analogous to
biological synapses, as indicated by the interpretation of the results from the
framework of a phenomenological model developed for biological synapses. We
describe a single memristive element containing a rich panel of features, which
will be of benefit to future neuromorphic hardware systems
Tuning a binary ferromagnet into a multi-state synapse with spin-orbit torque induced plasticity
Inspired by ion-dominated synaptic plasticity in human brain, artificial
synapses for neuromorphic computing adopt charge-related quantities as their
weights. Despite the existing charge derived synaptic emulations, schemes of
controlling electron spins in ferromagnetic devices have also attracted
considerable interest due to their advantages of low energy consumption,
unlimited endurance, and favorable CMOS compatibility. However, a generally
applicable method of tuning a binary ferromagnet into a multi-state memory with
pure spin-dominated synaptic plasticity in the absence of an external magnetic
field is still missing. Here, we show how synaptic plasticity of a
perpendicular ferromagnetic FM1 layer can be obtained when it is
interlayer-exchange-coupled by another in-plane ferromagnetic FM2 layer, where
a magnetic-field-free current-driven multi-state magnetization switching of FM1
in the Pt/FM1/Ta/FM2 structure is induced by spin-orbit torque. We use current
pulses to set the perpendicular magnetization state which acts as the synapse
weight, and demonstrate spintronic implementation of the excitatory/inhibitory
postsynaptic potentials and spike timing-dependent plasticity. This
functionality is made possible by the action of the in-plane interlayer
exchange coupling field which leads to broadened, multi-state magnetic reversal
characteristics. Numerical simulations, combined with investigations of a
reference sample with a single perpendicular magnetized Pt/FM1/Ta structure,
reveal that the broadening is due to the in-plane field component tuning the
efficiency of the spin-orbit-torque to drive domain walls across a landscape of
varying pinning potentials. The conventionally binary FM1 inside our
Pt/FM1/Ta/FM2 structure with inherent in-plane coupling field is therefore
tuned into a multi-state perpendicular ferromagnet and represents a synaptic
emulator for neuromorphic computing.Comment: 37 pages with 11 figures, including 20 pages for manuscript and 17
pages for supplementary informatio
Progressive amorphization of GeSbTe phase-change material under electron beam irradiation
Fast and reversible phase transitions in chalcogenide phase-change materials
(PCMs), in particular, Ge-Sb-Te compounds, are not only of fundamental
interests, but also make PCMs based random access memory (PRAM) a leading
candidate for non-volatile memory and neuromorphic computing devices. To RESET
the memory cell, crystalline Ge-Sb-Te has to undergo phase transitions firstly
to a liquid state and then to an amorphous state, corresponding to an abrupt
change in electrical resistance. In this work, we demonstrate a progressive
amorphization process in GeSb2Te4 thin films under electron beam irradiation on
transmission electron microscope (TEM). Melting is shown to be completely
absent by the in situ TEM experiments. The progressive amorphization process
resembles closely the cumulative crystallization process that accompanies a
continuous change in electrical resistance. Our work suggests that if
displacement forces can be implemented properly, it should be possible to
emulate symmetric neuronal dynamics by using PCMs
An organic nanoparticle transistor behaving as a biological synapse
Molecule-based devices are envisioned to complement silicon devices by
providing new functions or already existing functions at a simpler process
level and at a lower cost by virtue of their self-organization capabilities.
Moreover, they are not bound to von Neuman architecture and this feature may
open the way to other architectural paradigms. Neuromorphic electronics is one
of them. Here we demonstrate a device made of molecules and nanoparticles, a
nanoparticle organic memory filed-effect transistor (NOMFET), which exhibits
the main behavior of a biological spiking synapse. Facilitating and depressing
synaptic behaviors can be reproduced by the NOMFET and can be programmed. The
synaptic plasticity for real time computing is evidenced and described by a
simple model. These results open the way to rate coding utilization of the
NOMFET in dynamical neuromorphic computing circuits.Comment: To be publsihed in Adv. Func. Mater. Revised version. One pdf file
including main paper and supplementary informatio
Emulating long-term synaptic dynamics with memristive devices
The potential of memristive devices is often seeing in implementing
neuromorphic architectures for achieving brain-like computation. However, the
designing procedures do not allow for extended manipulation of the material,
unlike CMOS technology, the properties of the memristive material should be
harnessed in the context of such computation, under the view that biological
synapses are memristors. Here we demonstrate that single solid-state TiO2
memristors can exhibit associative plasticity phenomena observed in biological
cortical synapses, and are captured by a phenomenological plasticity model
called triplet rule. This rule comprises of a spike-timing dependent plasticity
regime and a classical hebbian associative regime, and is compatible with a
large amount of electrophysiology data. Via a set of experiments with our
artificial, memristive, synapses we show that, contrary to conventional uses of
solid-state memory, the co-existence of field- and thermally-driven switching
mechanisms that could render bipolar and/or unipolar programming modes is a
salient feature for capturing long-term potentiation and depression synaptic
dynamics. We further demonstrate that the non-linear accumulating nature of
memristors promotes long-term potentiating or depressing memory transitions
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