Applied Physics Letters,
Journal Year:
2024,
Volume and Issue:
125(22)
Published: Nov. 25, 2024
Perovskite
memristors
have
garnered
significant
interest
for
their
potential
simulating
artificial
synapses;
however,
the
presence
of
toxic
lead-based
perovskites
has
hindered
advancements
in
this
field.
In
work,
a
nontoxic,
thickness-controllable
Cs3Cu2I5
perovskite
functional
layer
is
synthesized
through
dual-source
vapor
deposition
Ag/Cs3Cu2I5/ITO
memristor.
The
co-evaporation
method
shows
advantages
various
element,
controllable
atomic
ratio
and
thickness,
free
impurity,
continuously
uniform
film.
This
device
demonstrates
an
operating
voltage
1.2
V,
low
power
consumption
0.013
W,
retention
time
exceeding
104
s,
endurance
over
400
cycles.
synaptic
behavior
emulated
using
memristor,
focusing
on
phenomena
such
as
short-term
potentiation
depression,
paired-pulse
facilitation,
spike-time-dependent
plasticity.
migration
Na+
Cl−
ions,
which
occurs
between
cleft
postsynaptic
membrane
biological
synapses,
analogously
represented
by
movement
Ag+
ions
bottom
electrode
process
further
analyzed
Hodgkin–Huxley
neuron
model.
Cs3Cu2I5-based
memristor
considerable
promise
applications
storage
systems
synapses.
Fractal and Fractional,
Journal Year:
2025,
Volume and Issue:
9(2), P. 115 - 115
Published: Feb. 13, 2025
Memristor-based
fractional-order
chaotic
systems
can
record
information
from
the
past,
present,
and
future,
describe
real
world
more
accurately
than
integer-order
systems.
This
paper
proposes
a
novel
memristor
model
verifies
its
characteristics
through
pinched
loop
(PHL)
method.
Subsequently,
new
memristive
Hopfield
neural
network
(4D-FOMHNN)
is
introduced
to
simulate
induced
current,
accompanied
by
Caputo’s
definition
of
fractional
order.
An
Adomian
decomposition
method
(ADM)
employed
for
system
solution.
By
varying
parameters
order
4D-FOMHNN,
rich
dynamic
behaviors
including
transient
chaos,
coexistence
attractors
are
observed
using
methods
such
as
bifurcation
diagrams
Lyapunov
exponent
analysis.
Finally,
proposed
FOMHNN
implemented
on
field-programmable
gate
array
(FPGA),
oscilloscope
observation
results
consistent
with
MATLAB
numerical
simulation
results,
which
further
validate
theoretical
analysis
provide
basis
application
in
field
encryption.
Physica Scripta,
Journal Year:
2024,
Volume and Issue:
99(5), P. 055225 - 055225
Published: March 25, 2024
Abstract
Involvement
of
two
capacitive
variables
into
neuron
models
provides
better
description
the
cell
membrane
property
and
then
diversity
effect
electromagnetic
field
inner
outer
can
be
estimated
in
clear
way.
Specific
electric
components
combined
to
build
equivalent
neural
circuits
for
reproducing
similar
activities
under
some
self-adaptive
control
schemes.
A
phototube
converts
external
light
stimuli
injected
energy
is
encoded
excite
membranes
presenting
suitable
firing
patterns.
Two
capacitors
are
connected
via
a
linear
resistor
mimicking
exchange
changes
potentials.
Combination
memristor
an
additive
branch
circuit
estimate
induction
absorption.
The
function
H
this
light-sensitive
memristive
calculated
theoretical
way,
average
〈
〉
predict
occurrence
stochastic
resonance,
which
confirmed
by
estimating
distribution
signal
noise
ratios.
mode
relative
value
neuron,
law
suggested
transition
adaptive
International Journal of Bifurcation and Chaos,
Journal Year:
2024,
Volume and Issue:
34(11)
Published: Aug. 24, 2024
In
this
paper,
a
novel
discrete
flux-controlled
generalized
memristor
is
proposed
to
be
used
in
Electromagnetic
Radiation
(EMR)
KTz
neuron.
The
introduction
of
can
simulate
the
behavior
changes
neuron
under
EMR.
stability
EMR
Neuron
(EKN)
map
discussed,
and
fixed
point
system
characterized.
With
change
parameters,
complex
rich
dynamical
characteristics
EKN
are
demonstrated.
firing
patterns
different
initial
values
also
investigated,
discharge
waveforms
similar
general
shape
structure
human
heart
brain
disease
phenomena
analyzed.
Finally,
hardware
implementation
verified
on
Digital
Signal
Processing
(DSP)
platform.
results
show
that
these
findings
great
value
for
practical
applications
neural
networks,
with
theoretical
basis
provided.
