Advanced Energy Materials,
Journal Year:
2024,
Volume and Issue:
14(26)
Published: May 5, 2024
Abstract
Halide
perovskites
are
at
the
forefront
of
active
research
in
many
applications,
such
as
high
performance
solar
cells,
photodetectors,
and
synapses
neurons
for
neuromorphic
computation.
As
a
result
ion
transport
ionic‐electronic
interactions,
current
recombination
influenced
by
delay
memory
effects
that
cause
hysteresis
current–voltage
curves
long
switching
times.
A
methodology
to
formulate
device
models
is
shown,
which
conduction
electronic
variables
internal
state
variables.
The
inspired
biological
frameworks
Hodgkin–Huxley
class
models.
Here,
theoretical
precedents,
main
physical
components
models,
their
application
describe
dynamical
measurements
halide
perovskite
devices
summarized.
several
measurement
methods
analyzed,
different
scan
rates,
impedance
spectroscopy
response,
time
transients.
transition
from
normal
(capacitive)
inverted
(inductive)
hysteresis,
convergence
stable
value,
described.
It
proposed
neuron‐style
capture
complexity
with
favorable
economy
parameters,
toward
identification
dominant
global
dynamic
processes
across
wide
voltage
span
determines
practical
response
types
devices.
ACS Nano,
Journal Year:
2023,
Volume and Issue:
18(1), P. 14 - 27
Published: Dec. 28, 2023
Memristors,
promising
nanoelectronic
devices
with
in-memory
resistive
switching
behavior
that
is
assembled
a
physically
integrated
core
processing
unit
(CPU)
and
memory
even
possesses
highly
possible
multistate
electrical
behavior,
could
avoid
the
von
Neumann
bottleneck
of
traditional
computing
show
efficient
ability
parallel
computation
high
information
storage.
These
advantages
position
them
as
potential
candidates
for
future
data-centric
requirements
add
remarkable
vigor
to
research
next-generation
artificial
intelligence
(AI)
systems,
particularly
those
involve
brain-like
applications.
This
work
provides
an
overview
evolution
memristor-based
devices,
from
their
initial
use
in
creating
synapses
neural
networks
application
developing
advanced
AI
systems
chips.
It
offers
broad
perspective
key
device
primitives
enabling
special
applications
view
materials,
nanostructure,
mechanism
models.
We
highlight
these
demonstrations
have
field
AI,
point
out
existing
challenges
nanodevices
toward
chips,
propose
guiding
principle
outlook
promotion
system
optimization
biomedical
field.
Advanced Functional Materials,
Journal Year:
2024,
Volume and Issue:
34(36)
Published: March 13, 2024
Abstract
Benefiting
from
powerful
logic‐computing,
higher
packaging
density,
and
extremely
low
electricity
consumption,
memristors
are
regarded
as
the
most
promising
next‐generation
of
electric
devices
capable
realizing
brain‐like
neuromorphic
computation.
However,
design
emerging
circuit
based
on
their
potential
application
in
unconventional
fields
very
meaningful
for
achieving
some
tasks
that
traditional
electronic
cannot
accomplish.
Herein,
a
Cu/PEDOT:PSS‐PP:PVDF/Ti
structured
memristor
is
fabricated
by
using
polyvinylidene
difluoride
(PVDF)
dopped
biomaterial
papaya
peel
(PP)
organic
poly(3,4‐ethylenedioxythiophene):polystyrene
sulfonate
(PEDOT:PSS)
heterojunction
functional
layer,
which
can
be
switched
among
resistive
switching,
self‐rectification
effect,
capacitive
behavior
adjusting
voltage
bias/scan
rate.
Through
further
fitting
data
simulating
interfacial
group
reactions,
this
work
innovatively
proposes
charge
conduction
mode
device
driven
Fowler–Nordheim
tunneling,
complexation
PEDOT:PSS
pore
removal.
Finally,
regular
logic
gate
adder
circuits
constructed
memristor,
while
fully
adder‐based
encryption
unit
designed
to
realize
image
reconstruction.
This
renders
compatible
with
circuits,
widening
path
toward
information
security.
Advanced Materials,
Journal Year:
2024,
Volume and Issue:
36(37)
Published: Feb. 10, 2024
Abstract
Brain–computer
interfaces
(BCIs)
that
enable
human–machine
interaction
have
immense
potential
in
restoring
or
augmenting
human
capabilities.
Traditional
BCIs
are
realized
based
on
complementary
metal‐oxide‐semiconductor
(CMOS)
technologies
with
complex,
bulky,
and
low
biocompatible
circuits,
suffer
the
energy
efficiency
of
von
Neumann
architecture.
The
brain–neuromorphics
interface
(BNI)
would
offer
a
promising
solution
to
advance
BCI
shape
interactions
machineries.
Neuromorphic
devices
systems
able
provide
substantial
computation
power
extremely
high
energy‐efficiency
by
implementing
in‐materia
computing
such
as
situ
vector–matrix
multiplication
(VMM)
physical
reservoir
computing.
Recent
progresses
integrating
neuromorphic
components
sensing
and/or
actuating
modules,
give
birth
afferent
nerve,
efferent
sensorimotor
loop,
so
on,
which
has
advanced
for
future
neurorobotics
achieving
sophisticated
capabilities
biological
system.
With
development
compact
artificial
spiking
neuron
bioelectronic
interfaces,
seamless
communication
between
BNI
bioentity
is
reasonably
expectable.
In
this
review,
upcoming
BNIs
profiled
introducing
brief
history
neuromorphics,
reviewing
recent
related
areas,
discussing
advances
challenges
lie
ahead.
Nature Communications,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: May 1, 2024
Abstract
Human
visual
neurons
rely
on
event-driven,
energy-efficient
spikes
for
communication,
while
silicon
image
sensors
do
not.
The
energy-budget
mismatch
between
biological
systems
and
machine
vision
technology
has
inspired
the
development
of
artificial
use
in
spiking
neural
network
(SNN).
However,
lack
multiplexed
data
coding
schemes
reduces
ability
SNN
to
emulate
perception
systems.
Here,
we
present
an
neuron
that
enables
rate
temporal
fusion
(RTF)
external
information.
can
code
information
at
different
frequencies
(rate
coding)
precise
time-to-first-spike
(TTFS)
coding.
This
sensory
scheme
could
improve
computing
capability
efficacy
neurons.
A
hardware-based
with
RTF
exhibits
good
consistency
real-world
ground
truth
achieves
highly
accurate
steering
speed
predictions
self-driving
vehicles
complex
conditions.
demonstrates
feasibility
developing
efficient
spike-based
neuromorphic
hardware.
Advanced Materials,
Journal Year:
2024,
Volume and Issue:
36(37)
Published: Feb. 29, 2024
Abstract
Human–machine
interaction
(HMI)
technology
has
undergone
significant
advancements
in
recent
years,
enabling
seamless
communication
between
humans
and
machines.
Its
expansion
extended
into
various
emerging
domains,
including
human
healthcare,
machine
perception,
biointerfaces,
thereby
magnifying
the
demand
for
advanced
intelligent
technologies.
Neuromorphic
computing,
a
paradigm
rooted
nanoionic
devices
that
emulate
operations
architecture
of
brain,
emerged
as
powerful
tool
highly
efficient
information
processing.
This
paper
delivers
comprehensive
review
developments
device‐based
neuromorphic
computing
technologies
their
pivotal
role
shaping
next‐generation
HMI.
Through
detailed
examination
fundamental
mechanisms
behaviors,
explores
ability
memristors
ion‐gated
transistors
to
intricate
functions
neurons
synapses.
Crucial
performance
metrics,
such
reliability,
energy
efficiency,
flexibility,
biocompatibility,
are
rigorously
evaluated.
Potential
applications,
challenges,
opportunities
using
HMI
technologies,
discussed
outlooked,
shedding
light
on
fusion
with
Chemical Reviews,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 2, 2025
Recent
breakthroughs
in
brain-inspired
computing
promise
to
address
a
wide
range
of
problems
from
security
healthcare.
However,
the
current
strategy
implementing
artificial
intelligence
algorithms
using
conventional
silicon
hardware
is
leading
unsustainable
energy
consumption.
Neuromorphic
based
on
electronic
devices
mimicking
biological
systems
emerging
as
low-energy
alternative,
although
further
progress
requires
materials
that
can
mimic
function
while
maintaining
scalability
and
speed.
As
result
their
diverse
unique
properties,
atomically
thin
two-dimensional
(2D)
are
promising
building
blocks
for
next-generation
electronics
including
nonvolatile
memory,
in-memory
neuromorphic
computing,
flexible
edge-computing
systems.
Furthermore,
2D
achieve
biorealistic
synaptic
neuronal
responses
extend
beyond
logic
memory
Here,
we
provide
comprehensive
review
growth,
fabrication,
integration
van
der
Waals
heterojunctions
optoelectronic
devices,
circuits,
For
each
case,
relationship
between
physical
properties
device
emphasized
followed
by
critical
comparison
technologies
different
applications.
We
conclude
with
forward-looking
perspective
key
remaining
challenges
opportunities
applications
leverage
fundamental
heterojunctions.
