Memristors: A New Age in Electronics for Sensors and...
Transcript of Memristors: A New Age in Electronics for Sensors and...
S.Carrara, EPFL Lausanne
(Switzerland)1
Moscow, 12 Moscow, 12 Sept.Sept., 2011, 2011
Memristors: Memristors:
A New Age A New Age
in Electronicsin Electronics
for Sensors for Sensors
and Memoriesand Memories
S.Carrara, EPFL Lausanne
(Switzerland)2
Tutorial OverviewTutorial Overview
� Concepts about Memristors
� Methods to fabricate memristors
� Structures of cross-bars based and
single- wires Memristors
� Measurements on Memristive effects
� Applications to Memories
� Applications to Neural Nets
� Applications to Nano-Bio-sensing
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Memristors First ConceptMemristors First Concept
dq
dv
di
dϕ
Four different circuit parameters:
dq
dϕ
)(),(),(),( ttqtitv ϕ
di
dv
The missed equation!
R=
C
1=
L=
M=
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The Memristor requestThe Memristor request
Symmetry reasons seem demanding for Memristors
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Memristors First ConceptMemristors First Concept
So, M = R in
case of constant
charge!
( )[ ]( )
dq
qdtqM
ϕ=
( )( )
Rtdi
tdv=
( )
dt
dqdt
qdϕ
=
Memristor depending on Charge Flux:
The usual Resistance:
( )di
qdv=
While, M will be an R with memory
effect in case of varying charge!
)(qR=
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The NameThe Name
Memory
Resistor
MEMRISTOR
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Memristor with lateral injectionMemristor with lateral injection
Carriers are injected by the source side
S D
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A two-terminals Memristor may be modeled by through two resistors as an element which vary the
resistance upon the applied voltage
Possible Memristor ModelPossible Memristor Model
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Memristors First ConceptMemristors First Concept
( ) )()(
1)(
tiD
twR
D
twRtv OFFON
−+=
)()(
tiD
R
dt
tdw ONVµ=
−= )(1)(
2tq
D
RRqM ONV
OFF
µ
)()( tqD
Rtw ON
Vµ=
More evident at nano-scale!
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Current/Voltage CharacteristicsCurrent/Voltage Characteristics
Memristic effects are observable in I-V curves
as a memory of the channel doping in time
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How to build a Memristor?How to build a Memristor?
“These results serve as the foundation for
understanding a wide range of hysteretic
current-voltage behavior observed in many
nano-scale electronic devices”
“[…] until now no one has presented either a
useful physical model or an example of a
memristor”
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Pt/TiOPt/TiO22--xx/TiO/TiO22/Pt memristors/Pt memristors
12
Source: Yang et al, Nature Nanotechnology 2008
O-v i
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Memristors by organicMemristors by organic
Carrier may be injected by the top polymer
S D
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Organic MemristorOrganic Memristor
14
Source: Berzina et al, Applied Materials & Interfaces 2009
Li or Rb+v i
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Memristors in polyMemristors in poly--SiliconSilicon
Thursday, September 22, 2011 15
Source: Ben-Jamaa, Carrara et al., IEEE Nano 2009
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Sizes OverviewSizes Overview
Insulator
(SiO2)
Poly-Si
spacer
Si substrate
• Dimensions are
not true to scale!
• Only frontend
processing is
depicted
• Backend includes
passivation +
metallization
~ 400 nm~ 400 nm
~ 70 nm
~ 70 nm~ 20 nm
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Fabrication ResultsFabrication Results
The SEM imaging shows the quality of poly-silicon wires fabricated by using spacers technique
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The registered I/V curves show memristic effects due to
a memory of the swept voltage windows
Current/Voltage CharacteristicsCurrent/Voltage Characteristics
Electrons de-trapping
Electrons trapping
Holes trapping
Holes de-trapping
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Conductivity MechanismsConductivity Mechanisms
Both electrons and holes based conductivity is affected by Memristor effect
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The registered I/V curves show memristic effects due to
a memory of the swept voltage windows
Current/Voltage CharacteristicsCurrent/Voltage Characteristics
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)()()()()( EgEgEgEgEg GDGATDTA +++=
TD
v
W
EE
TDTD eNEg
−
=)(
2
)(
−−
= GA
GA
W
EE
GAGA eNEg
2
)(
−−
= GD
GD
W
EE
GDGA eNEg
TA
c
W
EE
TATA eNEg
−
=)(
Tail States Gaussian StatesAcceptors State
Donors State
Simulations by Simulations by
driftdrift--diffusion modeldiffusion model
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2D Simulations by Atlas follow the experimental
data accounting for negative trapped charges at the
Poly-Si/SiO2 interface
-20 -10 0 10 20
10-11
10-10
10-9
10-8
10-7
Simulation
Dra
in C
urr
en
t (
A)
Gate Voltage (V)
Experiment
Simulations ResultSimulations Result
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Memristors by BulkMemristors by Bulk--Silicon, tooSilicon, too
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Memristors by CrystallineMemristors by Crystalline--SiliconSilicon
24
Source: D.Sacchetto, .., Carrara, et al., IEEE ISCAS 2010
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Silicon NanowiresSilicon Nanowires
25
Polysilicon
Gate All
Around
c-Si nanowire
channel
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Reliable for High ParallelismReliable for High Parallelism
Silicon nanowire transistors with Schottky Barrier Source/Drain are emerging devices candidates for next generation transistor technology
26
Source: Sacchetto et al., ESSDERC 2009
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Memristive BehaviorMemristive Behavior
27
V0(Vds)
Vds Ids
Vgs and t are
constants
Vds as the
input
x80
c-SiNW : Ids-Vds
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Hysteretic Charge TrappingHysteretic Charge Trapping
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c-SiNW : Sweep frequency dependence
V0(Vgs,
t)
Vgs
t
Ids
Vds as the
constant
Vgs and t as the
input
t
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Theory of Operation (1)Theory of Operation (1)
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e- h+
constant Vds
Vgs polarity
selects the
type of carrier
S SD D
e-/h+v i
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Theory of Operation (2)Theory of Operation (2)
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( )1/))((/ 0 −⋅=
−− kTVVVkT
sdsdsB eeII
φ
Schottky Barrier height
Schottky barrier built-in voltage
),,()( 00 tVVVVV gsds=
Ids
S D
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Theory of Operation (2)Theory of Operation (2)
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V0(Vds,Vgs,t)
Vgs
Vds
t
Ids
INP
UT
S
STATE
variable
OUTPUT
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Theory of Operation (2)Theory of Operation (2)
Simplification of model-w structure
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Theory of Operation (2)Theory of Operation (2)
Model-w equivalent circuit
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Theory of Operation (2)Theory of Operation (2)
∫=⇒ dttVL
aw )(
µ
[ ]btaL
w +=⇒ )(ϕµ
Eadt
dwµ=
L
tVa
)(µ=
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Theory of Operation (2)Theory of Operation (2)
VVV rl γ=−
0)(1 =
−−+− ti
L
wRVVV sirl
That’s the Kirchoff’s Voltage Law
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Theory of Operation (2)Theory of Operation (2)
0)(1 =
−−− ti
L
wRVV SIγ
That’s the Kirchoff’s Voltage Law
)(
1
)1(ti
L
wR
V
SI
=
−
−γ)()()( tVGti ϕ=⇒
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Theory of Operation (2)Theory of Operation (2)
Comparison of model-w with measurements
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Theory of Operation (2)Theory of Operation (2)
State variable after addition of phase parameter
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Theory of Operation (3)Theory of Operation (3)
Equivalent Circuit Model
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Theory of Operation (3)Theory of Operation (3)
Explanation of circuit operation
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Theory of Operation (3)Theory of Operation (3)
Equivalent Circuit Model vs measurements
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Applications to MemoryApplications to Memory
Monolithic 3D integration of state-of-the-art transistor technologies with memristive devices, with opportunities for high-density 3D crossbar
construction:
� Multi-level Resistive RAMs
� Dynamic RAMs
42
GATE
lines
CHANNEL lines
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Crossbar Memristors for (RRAM)
resistive random-access-memory (RRAM) from hysteretic resistive memristive devices
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Crossbar Memristors for (RRAM)Crossbar Memristors for (RRAM)
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Memristive Programmable Devices by Through Silicon Vias
Concept image of planar Re-RAM made of Pt/TiO2/Pt stack
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Memristive Programmable Devices by Through Silicon Vias
Reconstructed 3D photograph of the TSV −Cu/TiO2/Pt device stack.
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Memristive Programmable Devices by Through Silicon Vias
Resistive switching through I − V sweeps for planar Pt/TiO2/Pt
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Resistive switching through I − V sweeps using TSV
− Cu/TiO2/Pt programmable fuse
Memristive Programmable Memristive Programmable
Devices by Through Silicon Devices by Through Silicon ViasVias
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Resistive switching through I − V sweeps using TSV − Pt/TiO2/Pt programmable fuse
Memristive Programmable Memristive Programmable
Devices by Through Silicon Devices by Through Silicon ViasVias
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Applications to NeuralApplications to Neural--NetsNets
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
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Applications to NeuralApplications to Neural--NetsNets
The change in current in sequential voltage sweeps
The The
LearningLearning
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Applications to NeuralApplications to Neural--NetsNets
Neural Nets by organic materials as well
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Homo- (a) and hetero- (b)
Synaptic junctionsModel of learning for Limnea
Stagnalis: association of the
mechanical stimulus with presence of
food
Neural Network of a Snail
SYNAPSES
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Input 1 – mechanical (neutral) stimulus
Input 2 – presence of food
Output: current as a result of the presence
of the neutral stimulus only
Training: application of both stimuli
Organic
memristive device
–synapse analog
V. Erokhin et al., BioNanoScience, 1, 24-30 (2011)
Neural Network of a Snail
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Measure of the Carriers Measure of the Carriers
injectioninjection
The Li+ ions normally used to dope PEO were not
adequate for such measurements because their fluorescent
energy cannot be detected at atmospheric conditions.
Thus, the heavier Rb ions were used
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Measure of the Carriers Measure of the Carriers
injectioninjection
Grazing-incidence X-ray fluorescence measurements were applied for a time-resolved study of an organic memristor conductivity variation
mechanism
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Applications to BioApplications to Bio--SensingSensing
Frontend Passivation
BackendFunctionalization
Definition of Silicon
nanowire
Isolation of devices,
via opening for
metalization and
functionalization
Metalization: NiCr
or Cr/NiCr or Al
Grafting of bio-
probes
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pH Dependent SensorpH Dependent Sensor� pH Sensor obtained by modifying Si oxide surface with 3-
aminopropyltriethoxysilane yielding amino and silanol groups (acting as receptors) at surface
Patolsky et al., Nanowire-Based Biosensors, Analytical Chemistry, 2006, 4261-4269
Protonation/deprotonation altered charge density at surface thereby changing conductance
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Detecting Single VirusDetecting Single Virus� When virus binds to antibody receptor, conductance changes from
baseline value
� When it unbinds, conductance returns to baseline value
Patolsky et al., Nanowire-Based Biosensors, Analytical Chemistry, 2006, 4261-4269
Ultra-dense NW device where minimum scale is set by
size of virus
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Concept in Memristors forConcept in Memristors for
NanoNano--BioBio--SensingSensing
The charging from molecules affects the Memristic
effects onto the poly-silicon channel
Back Gate
Nitride Passivation
Nano-wire channel
Top-gate
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Memristors for NanoMemristors for Nano--BioBio--SensingSensing
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Suspended Si ribbon with 600nm trench dimension before sacrificial oxidation
Memristors for NanoMemristors for Nano--BioBio--SensingSensing
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Suspended Si nanowire with 100nm diameter and hard mask
opened at contact regions before Ni/Ti metal deposition
Memristors for NanoMemristors for Nano--BioBio--SensingSensing
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14.8nm14.8nm
12.8nm12.8nm
9.3 nm9.3 nm
The size of an AntibodyThe size of an Antibody
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Bio affects MemconductanceBio affects Memconductance
Ids − Vds curves taken (1) before and (2) after
memristive-biosensors functionalization
Minima Minima
VoltageVoltage
GapGap
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Bio affects MemconductanceBio affects Memconductance
Ids − Vds curves: before and after the up-take of 5 pM AG solution.
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The charges of an Antibody?The charges of an Antibody?
The crystallographic structure of an antibody
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Charged ResiduesCharged ResiduesPositively Charged
Arginine
+
Histidine
+
Lysine
+
Polar Uncharged
-
Aspartic Acid
-
Glutamic Acid
Neg. Charged
-
-
+
+
Serine
-
+
Threonine
-
+
Aspargine
-
+
Glutamine
-
+
-
+
-
+ +
-
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The charges of an AntibodyThe charges of an Antibody
The crystallographic structure of an antibody
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• Memristors are devices with a memory of its bias story
• Memristic effect has been registered in I/V characteristics with both organic and inorganic materials, including Silicon
• 2D simulations and Fluorescent Measurements confirmed that Memristic effects are due to charges trapping in the Channel
• Applications of memristic devices are feasible for Memories, biomorphic networks, and Biosensors
• Nano-electronics is now at the beginning of a new age thanks to the exploitation of Memristors
ConclusionsConclusions
(c) S.Carrara, EPFL - Lausanne
(Switzerland)71
Thanks to:Thanks to:
� Davide Sacchetto
� Haykel Ben Jamaa
� Marie-Agnès Doucey
� Akshat Dave
� Pietro Dalmastro
� Julius Georgiou
� Victor Erokhin
� Yusuf Leblebici
� Giovanni De Micheli
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(Switzerland)72
Coordinates:Dr. Sandro Carrara Ph.D
Integrated Laboratory Systems
Swiss Federal Institute of Technology (EPFL)
CH-1015 Lausanne
Web: http://si2.epfl.ch/~scarrara/
email: [email protected]
Thank you for your attention!Thank you for your attention!