ACS Nano,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 31, 2024
Two-dimensional
(2D)
materials
hold
significant
potential
for
the
development
of
neuromorphic
computing
architectures
owing
to
their
exceptional
electrical
tunability,
mechanical
flexibility,
and
compatibility
with
heterointegration.
However,
practical
implementation
2D
memristors
in
is
often
hindered
by
challenges
simultaneously
achieving
low
latency
energy
consumption.
Here,
we
demonstrate
based
on
cobalt
phosphorus
trisulfide
(CoPS
Chemical Reviews,
Journal Year:
2024,
Volume and Issue:
124(22), P. 12738 - 12843
Published: Nov. 5, 2024
The
quest
to
imbue
machines
with
intelligence
akin
that
of
humans,
through
the
development
adaptable
neuromorphic
devices
and
creation
artificial
neural
systems,
has
long
stood
as
a
pivotal
goal
in
both
scientific
inquiry
industrial
advancement.
Recent
advancements
flexible
electronics
primarily
rely
on
nanomaterials
polymers
owing
their
inherent
uniformity,
superior
mechanical
electrical
capabilities,
versatile
functionalities.
However,
this
field
is
still
its
nascent
stage,
necessitating
continuous
efforts
materials
innovation
device/system
design.
Therefore,
it
imperative
conduct
an
extensive
comprehensive
analysis
summarize
current
progress.
This
review
highlights
applications
neuromorphics,
involving
inorganic
(zero-/one-/two-dimensional,
heterostructure),
carbon-based
such
carbon
nanotubes
(CNTs)
graphene,
polymers.
Additionally,
comparison
summary
structural
compositions,
design
strategies,
key
performance,
significant
these
are
provided.
Furthermore,
challenges
future
directions
pertaining
materials/devices/systems
associated
neuromorphics
also
addressed.
aim
shed
light
rapidly
growing
attract
experts
from
diverse
disciplines
(e.g.,
electronics,
science,
neurobiology),
foster
further
for
accelerated
development.
Advanced Functional Materials,
Journal Year:
2023,
Volume and Issue:
34(9)
Published: Nov. 27, 2023
Abstract
While
transition‐metal
thiophosphate
(MPX
3
)
materials
have
been
a
subject
of
extensive
research
in
recent
years,
experimental
studies
on
MPX
‐based
memristors
are
still
their
early
stages,
with
device
performance
being
less
than
ideal.
Here,
the
successful
fabrication
high‐yield,
high‐performance,
and
uniform
demonstrated
to
possess
desired
characteristics
for
neuromorphic
computing
using
single‐crystalline
few‐layered
manganese
phosphorus
trisulfide
(MnPS
as
resistive
switching
medium.
The
Ti/MnPS
/Au
memristor
exhibits
small
voltage
(<1
V),
long
memory
retention
(10
4
s),
fast
speed
(≈20
ns),
high
On/Off
ratio
(nearly
two
orders
magnitude),
simultaneously
achieves
emulation
synaptic
weight
plasticity.
microscopic
investigation
structural
chemical
few‐layer
MnPS
reveals
presence
defects
residual
Ti
throughout
stacked
layer
following
application
voltage,
which
contributes
uniformity
low
set
voltage.
With
highly
linear
symmetric
analog
updates
coupled
capability
accurate
decimal
arithmetic
operations,
accuracy
95.15%
supervised
learning
MNIST
handwritten
recognition
dataset
is
achieved
artificial
neural
network.
Furthermore,
convolutional
image
processing
can
be
implemented
hardware
multiply‐and‐accumulate
operation
an
crossbar
array.
Nano Convergence,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: June 27, 2024
Two-dimensional
(2D)
materials
have
emerged
as
promising
building
blocks
for
next
generation
memristive
devices,
owing
to
their
unique
electronic,
mechanical,
and
thermal
properties,
resulting
in
effective
switching
mechanisms
charge
transport.
Memristors
are
key
components
a
wide
range
of
applications
including
neuromorphic
computing,
which
is
becoming
increasingly
important
artificial
intelligence
applications.
Crossbar
arrays
an
component
the
development
hardware-based
neural
networks
composed
2D
materials.
In
this
paper,
we
summarize
current
state
research
on
material-based
devices
utilizing
different
mechanisms,
along
with
application
these
crossbar
arrays.
Additionally,
discuss
challenges
future
directions
field.
Applied Physics Letters,
Journal Year:
2025,
Volume and Issue:
126(6)
Published: Feb. 10, 2025
Neuromimetic
devices
have
emerged
as
transformative
technologies
with
the
potential
to
redefine
traditional
computing
paradigms
and
enable
advanced
artificial
neural
systems.
Among
various
innovative
materials,
two-dimensional
(2D)
materials
garnered
attention
frontrunners
for
next-generation
device
fabrication.
In
this
work,
we
report
fabrication
comprehensive
characterization
of
a
memristor
based
on
2D
PtTe2.
The
demonstrates
exceptional
performance
metrics,
including
high
OFF/ON
ratio,
low
switching
voltage,
long
data
retention
time.
Leveraging
density
functional
theory
calculations,
unravel
underlying
conduction
mechanism,
revealing
pivotal
role
Ag
conductive
filaments
in
resistive
behavior.
Furthermore,
neuromorphic
capabilities
PtTe2
were
evaluated
through
its
emulation
key
brain-inspired
synaptic
functionalities,
such
long-term
depression/enhancement,
paired-pulse
facilitation,
spike-timing-dependent
plasticity.
By
modulating
electrical
conductance,
implemented
convolutional
network
MNIST
handwritten
digit
recognition,
achieving
remarkable
accuracy
97.49%.
To
further
illustrate
adaptive
learning
capabilities,
demonstrated
Pavlov's
dog
experiment
using
device.
This
study
establishes
promising
material
applications
represents
critical
step
forward
bridging
gap
between
architectures.
These
findings
lay
robust
foundation
future
exploration
field
engineering.
