Unlocking Neuromorphic Vision: Advancements in IGZO-Based Optoelectronic Memristors with Visible Range Sensitivity
ACS Applied Electronic Materials,
Journal Year:
2024,
Volume and Issue:
6(7), P. 5230 - 5243
Published: July 5, 2024
Optoelectronic
memristors
based
on
amorphous
oxide
semiconductors
(AOSs)
are
promising
devices
for
the
development
of
spiking
neural
network
(SNN)
hardware
in
neuromorphic
vision
sensors.
In
such
devices,
conductance
state
can
be
controlled
by
both
optical
and
electrical
stimuli,
while
typical
persistent
photoconductivity
(PPC)
AOS
materials
used
to
emulate
synaptic
functions.
However,
due
large
band
gap
these
materials,
sensitivity
visible
light
(red/green/blue)
is
difficult
accomplish,
which
hinders
applications
requiring
color
discrimination.
this
work,
we
report
a
4
μm
Language: Английский
Solution-processed organic/inorganic heterojunction synaptic transistor for neuromorphic computing
Journal of Physics D Applied Physics,
Journal Year:
2025,
Volume and Issue:
58(13), P. 135110 - 135110
Published: Jan. 24, 2025
Abstract
Artificial
synaptic
devices
are
the
hardware
foundation
of
modern
computing
systems
which
have
shown
great
potential
in
overcoming
bottleneck
traditional
von-Neumann
architectures.
Organic
transistors
garnered
considerable
attention
due
to
their
merits,
such
as
low
cost,
weight,
and
mechanical
flexibility.
Various
materials
utilized
for
charge-capture
layer
organic
transistors.
Indium
gallium
zinc
oxide
(IGZO)
is
a
typical
metal
semiconductor
with
wide
bandgap,
high
carrier
mobility,
stable
characteristics.
Moreover,
IGZO
an
n-type
lower
highest
occupied
molecular
orbital
(HOMO)
lowest
unoccupied
(LUMO)
energy
level
compared
p-type
semiconductor,
has
capture
material
fabricate
high-performance
devices.
However,
application
trapping
received
limited
attention.
Consequently,
transistor
based
on
organic/inorganic
heterojunction
was
developed.
The
impact
program/erase
time
memory
performance
investigated,
revealing
that
window
ratio
increased
write/erase
extended.
Additionally,
behavior
were
successfully
emulated,
including
excitatory/inhibitory
postsynaptic
current,
paired-pulse
facilitation,
depression,
high-pass
filtering
characteristics,
transformation
short-term
plasticity
long-term
plasticity.
Notably,
inorganic–organic
bilayer
achieved
recognition
accuracy
89.2%
using
Modified
National
Institute
Standards
Technology
dataset
handwritten
digit
training.
This
study
provides
facile
route
fabricating
transistors,
paving
way
development
advanced
brain-like
computers.
Language: Английский
Low‐Power and Multimodal Organic Photoelectric Synaptic Transistors Modulated by Photoisomerization for UV Damage Perception and Artificial Visual Recognition
Advanced Functional Materials,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 19, 2025
Abstract
Low‐power
and
efficiently
parallel
neuromorphic
computing
is
expected
to
break
the
bottleneck
of
von
Neumann
architecture.
Due
direct
responses
optical
signals,
photonic
synaptic
devices
can
work
as
core
components
artificial
visual
systems,
accelerating
development
neural
computing.
Furthermore,
community
looking
for
effective
coupling
electronic
behaviors
within
an
individual
organic
device
achieve
further
functional
integration.
Photoisomeric
molecules
with
photo‐regulatable
properties
are
facilitate
this
process.
Herein,
photoelectric
transistors
(OPSTs)
constructed
by
introducing
poly(2‐(3′,3′‐dimethyl‐6‐nitrospiro[chromene‐2,2′‐indolin]‐1′‐yl)
ethyl
methacrylate)
(PSPMA)
photoisomeric
groups,
which
effectively
improves
photo‐synaptic
response.
polarization
induction
light‐assisted
charge
trapping
PSPMA,
OPSTs
simulate
typical
significant
conductance
modulation
at
low
voltage
assistance
UV
light.
The
power
consumption
84
aJ
per
event.
Moreover,
mimic
nociceptors,
recognize
handwritten
digits
93.33%
accuracy,
decode
encrypted
information,
demonstrating
potential
applications
in
damage
perception
recognition.
These
findings
will
expand
application
devices,
open
up
new
possibilities
hardware
architectures
synapses.
Language: Английский
Emerging Artificial Synaptic Devices Based on Organic Semiconductors: Molecular Design, Structure and Applications
ACS Applied Materials & Interfaces,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 9, 2025
In
modern
computing,
the
Von
Neumann
architecture
faces
challenges
such
as
memory
bottleneck,
hindering
efficient
processing
of
large
datasets
and
concurrent
programs.
Neuromorphic
inspired
by
brain's
architecture,
emerges
a
promising
alternative,
offering
unparalleled
computational
power
while
consuming
less
energy.
Artificial
synaptic
devices
play
crucial
role
in
this
paradigm
shift.
Various
material
systems,
from
organic
to
inorganic,
have
been
explored
for
neuromorphic
devices,
with
materials
attracting
attention
their
excellent
photoelectric
properties,
diverse
choices,
versatile
preparation
methods.
Organic
semiconductors,
particular,
offer
advantages
over
transition-metal
dichalcogenides,
including
ease
flexibility,
making
them
suitable
large-area
films.
This
review
focuses
on
emerging
artificial
based
discussing
different
branches
within
semiconductor
system,
various
fabrication
methods,
device
structure
designs,
applications
synapse.
Critical
considerations
achieving
truly
human-like
dynamic
perception
systems
semiconductors
are
also
outlined,
reflecting
ongoing
evolution
computing.
Language: Английский
A Violet‐Light‐Responsive ReRAM Based on Zn2SnO4/Ga2O3 Heterojunction as an Artificial Synapse for Visual Sensory and In‐Memory Computing
Advanced Electronic Materials,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 9, 2024
Abstract
Due
to
the
imitation
of
neural
functionalities
human
brain
via
optical
modulation
resistance
states,
photoelectric
resistive
random
access
memory
(ReRAM)
devices
attract
extensive
attraction
for
synaptic
electronics
and
in‐memory
computing
applications.
In
this
work,
a
ReRAM
(PSR)
structure
ITO/Zn
2
SnO
4
/Ga
O
3
/ITO/glass
with
simple
fabrication
process
is
reported
imitate
plasticity.
Electrically
induced
long‐term
potentiation/depression
(LTP/D)
behavior
indicates
fulfillment
fundamental
requirement
artificial
neuron
devices.
Classification
three‐channeled
images
corrupted
different
levels
(0.15–0.9)
Gaussian
noise
achieved
by
simulating
convolutional
network
(CNN).
The
violet
light
(405
nm)
illumination
generates
excitatory
post
current
(EPSC),
which
influenced
persistent
photoconductivity
(PPC)
effect
after
discontinuing
excitation.
As
an
device,
PSR
able
some
basic
functions
such
as
multi‐levels
linearly
increasing
trend,
learning‐forgetting‐relearning
behavior.
same
device
also
shows
emulation
visual
persistency
optic
nerve
skin‐damage
warning.
This
executes
high‐pass
filtering
function
demonstrates
its
potential
in
image‐sharpening
process.
These
findings
provide
avenue
develop
oxide
semiconductor‐based
multifunctional
advanced
systems.
Language: Английский