Chemical Society Reviews,
Год журнала:
2023,
Номер
52(20), С. 7071 - 7136
Опубликована: Янв. 1, 2023
This
review
highlights
the
film
preparation
methods
and
application
advances
in
memory
neuromorphic
electronics
of
porous
crystalline
materials,
involving
MOFs,
COFs,
HOFs,
zeolites.
International Journal of Extreme Manufacturing,
Год журнала:
2023,
Номер
5(4), С. 042006 - 042006
Опубликована: Авг. 8, 2023
Abstract
Neuromorphic
computing
systems
can
perform
memory
and
tasks
in
parallel
on
artificial
synaptic
devices
through
simulating
functions,
which
is
promising
for
breaking
the
conventional
von
Neumann
bottlenecks
at
hardware
level.
Artificial
optoelectronic
synapses
enable
synergistic
coupling
between
optical
electrical
signals
modulation,
opens
up
an
innovative
path
effective
neuromorphic
systems.
With
advantages
of
high
mobility,
transparency,
ultrawideband
tunability,
environmental
stability,
graphene
has
attracted
tremendous
interest
electronic
applications.
Recent
progress
highlights
significance
implementing
into
devices.
Herein,
to
better
understand
potential
graphene-based
devices,
fabrication
technologies
are
first
presented.
Then,
roles
various
demonstrated.
Furthermore,
their
typical
applications
reviewed.
Finally,
outlooks
development
based
proposed.
This
review
will
provide
a
comprehensive
understanding
device
applications,
also
present
outlook
future
Chemical Reviews,
Год журнала:
2024,
Номер
124(22), С. 12738 - 12843
Опубликована: Ноя. 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.
Machine‐learning‐enhanced
nanosensors
are
rapidly
emerging
as
a
promising
solution
in
the
field
of
sensor
technology,
traditional
sensors
encounter
limitations
data
analysis
their
development.
Since
inception
machine‐learning
algorithms
being
applied
to
enhance
nanosensors,
they
have
gained
significant
attention
due
adaptive
and
predictive
capabilities,
which
promise
dramatically
improve
efficiency
collection
processing
applications.
Herein,
comprehensive
overview
technological
innovation
is
provided
by
reviewing
latest
developments
cloud
computing,
edge
burgeoning
realm
neuromorphic
computing.
Cloud
computing
has
emerged
powerhouse,
harnessing
formidable
computational
capabilities
process
vast
volumes
high‐dimensional
data.
Then,
research
directions
for
various
applications
these
artificial
intelligence
(AI)‐enhanced
outlined.
Moreover,
integration
AI
nanosensor
technology
into
chip‐level
although
promising,
still
faces
challenges
such
energy‐efficient
hardware
development,
algorithm
optimization,
scalability
mass
production.
Finally,
forward‐looking
perspective
on
future
machine‐learning‐enhanced
provided,
delineating
opportunities
further
this
exciting
field.
ACS Applied Materials & Interfaces,
Год журнала:
2023,
Номер
15(30), С. 36527 - 36538
Опубликована: Июль 19, 2023
The
demands
of
modern
electronic
components
require
advanced
computing
platforms
for
efficient
information
processing
to
realize
in-memory
operations
with
a
high
density
data
storage
capabilities
toward
developing
alternatives
von
Neumann
architectures.
Herein,
we
demonstrate
the
multifunctionality
monolayer
MoS2
memtransistors,
which
can
be
used
as
high-geared
intrinsic
transistor
at
room
temperature;
however,
temperature
(>350
K),
they
exhibit
synaptic
multilevel
memory
operations.
temperature-dependent
mechanism
is
governed
by
interfacial
physics,
solely
depends
on
gate
field
modulated
ion
dynamics
and
charge
transfer
MoS2/dielectric
interface.
We
have
proposed
non-volatile
application
using
single
Field
Effect
Transistor
(FET)
device
where
thermal
energy
ventured
aid
functions
(3-bit)
capabilities.
Furthermore,
our
devices
linear
symmetry
in
conductance
weight
updates
when
subjected
electrical
potentiation
depression.
This
feature
has
enabled
us
attain
classification
accuracy
while
training
testing
Modified
National
Institute
Standards
Technology
datasets
through
artificial
neural
network
simulation.
work
paves
way
reliable
2D
semiconductors
high-packing
arrays
brain-inspired
learning.
ACS Applied Electronic Materials,
Год журнала:
2024,
Номер
6(1), С. 587 - 598
Опубликована: Янв. 10, 2024
The
limitations
of
Moore's
law
and
the
von
Neumann
bottleneck
have
sparked
an
increasing
interest
in
advanced
intelligent
systems,
such
as
memristors
neuromorphic
devices.
This
work
unveils
role
slow
ion
migration
for
resistive
switching
(RS)
exceptional
environmental
mechanical
resilience
achieved
with
butane-1,4-diammonium
(BDA)-based
BDAPbI4
memristors,
meticulously
fabricated
measured
ambient
conditions.
These
demonstrate
durability
consistent
characteristics
up
to
60
days
a
slight
decay
ON/OFF
ratio
on
140th
day.
Devices
show
potential
flexible
random-access
memories
low
operating
voltage
∼100
mV
strong
data
retention
endurance
35
h
∼1000
cycles,
respectively.
RS
these
devices
is
attributed
energy
barrier
modulation
at
perovskite/Ag
interface
perovskite
film.
Furthermore,
initial
investigations
into
their
synaptic
reveal
stable
learning
behavior
(potentiation
depression)
invariant
paired
pulse
facilitation
(PPF),
tested
flat
5
mm
bending
radii.
Additionally,
application
spike
time-dependent
plasticity
(STDP)
Hebbian
rule
effectively
demonstrates
feasibility
computing
applications.
particularly
promising
use
extreme
conditions,
electronic
skins,
extends
beyond
traditional
storage
solutions.
Communications Materials,
Год журнала:
2024,
Номер
5(1)
Опубликована: Июль 20, 2024
Abstract
Neuromorphic
architectures,
expanding
the
limits
of
computing
from
conventional
data
processing
and
storage
to
advanced
cognition,
learning,
in-memory
computing,
impose
restrictions
on
materials
that
should
operate
fast,
energy
efficiently,
highly
endurant.
Here
we
report
architecture
based
metal-organic
framework
(MOF)
single
crystal
with
a
light
control.
We
demonstrate
MOF
inherent
memristive
behavior
(for
storage)
changes
nonlinearly
its
electric
response
when
irradiated
by
light.
This
leads
three
more
electronic
states
(spikes)
81
ms
duration
1
s
refractory
time,
allowing
implement
40
bits
−1
optoelectronic
processing.
Next,
is
switched
neuromorphic
state
upon
action
set
laser
pulses,
providing
text
recognition
over
50
times
app.
100%
accuracy.
Thereby,
simultaneous
storage,
processing,
MOF,
driven
light,
pave
way
for
multifunctional
architectures.
ACS Applied Electronic Materials,
Год журнала:
2023,
Номер
5(9), С. 5249 - 5256
Опубликована: Сен. 13, 2023
In-memory
computing
enables
fast
and
low
power
consumption
by
overcoming
major
drawbacks
of
traditional
computers
built
with
a
von
Neumann
architecture.
In
memristor,
multilevel
storage
history-dependent
conductivity
modulation
characteristics
allow
us
to
store
the
information
simulate
synaptic
behaviors
mimic
biological
brain.
this
work,
role
interfacial
layers
has
been
investigated
in
suppression
charge
transfer
barrier
Dion-Jacobson
hybrid
perovskite-based
memristor
devices.
The
insertion
between
active
layer
electrodes
(ITO/PEDOT:PSS/Active
layer/PMMA/Ag)
improves
ON/OFF
ratio
(103),
data
endurance
(102),
retention
(>6000
s)
for
nonvolatile
applications
3-(aminomethyl)
piperidinium
(3AMP)
organic
spacer
cation-based
presence
reduces
SET
voltage
0.33
V
energy
an
estimated
value
∼26
nJ.
A
mathematical
model
is
presented
fitted
experimental
understand
formation/rupture
conducting
filament
resistive
switching
mechanism.
Neuromorphic
properties
like
learning
forgetting
nature
device
(potentiation
depression),
inhibitory
postsynaptic
current,
spike
number
dependent
plasticity,
paired
pulse
facilitation
index
are
also
systematically
presented.
Thus,
potential
human
brain
processes
these
memristors
profound
implications
artificial
intelligence,
robotics,
brain-machine
interfaces,
shaping
future
cognitive
AI-driven
technologies.
