The development of alternative brain-inspired neuromorphic computing architectures is anticipated to play a key role in addressing the strict requirements of the artificial intelligence era. In order to obtain a high degree of learning accuracy within an artificial neural network (ANN) that operates with the backpropagation algorithm, a highly symmetric synaptic weight distribution is desired. Along these lines, we present here a detailed device engineering approach that enables analog synaptic properties in completely forming free SiO2-conductive bridge memories. This is achieved by either incorporating a dense layer of Pt nanoparticles as a bottom electrode or fabricating bilayer structures using a second switching layer of VOx. Interestingly, compared with the reference sample that manifests both threshold and bipolar switching modes, the Pt NC sample exhibits only the threshold switching pattern, whereas the bilayer configuration operates only under the bipolar switching mode, as illustrated by direct current measurements. These characteristics have a direct, while different impact, on the conductance modulation pattern and determine the analog nature of the synaptic weight distribution. Valuable insights regarding the origin of these effects and, in particular, of the symmetric and linear conductance modulation processes are gained through the implementation of a self-consistent numerical model that takes into account both the impact of the electrodes' thermal conductivity on the switching pattern and the different diffusion barriers for silver ion migration. Our approach provides useful guidelines toward the realization of high yield ANNs with biological-like dynamic behavior by controlling the conducting filament growth mechanism.

1.
D. B.
Strukov
, “
Smart connections
,”
Nature
476
,
403
405
(
2011
).
2.
D.
Silver
,
A.
Huang
,
C. J.
Maddison
,
A.
Guez
,
L.
Sifre
,
G.
van den Driessche
,
J.
Schrittwieser
,
I.
Antonoglou
,
V.
Panneershelvam
,
M.
Lanctot
,
S.
Dieleman
,
D.
Grewe
,
J.
Nham
,
N.
Kalchbrenner
,
I.
Sutskever
,
T.
Lillicrap
,
M.
Leach
,
K.
Kavukcuoglu
,
T.
Graepel
, and
D.
Hassabis
, “
Mastering the game of Go with deep neural networks and tree search
,”
Nature
529
,
484
489
(
2016
).
3.
Y.
Li
and
K.-W.
Ang
, “
Hardware implementation of neuromorphic computing using large-scale memristor crossbar arrays
,”
Adv. Intell. Syst.
3
,
2000137
(
2021
).
4.
H.-M.
Huang
,
Z.
Wang
,
T.
Wang
,
Y.
Xiao
, and
X.
Guo
, “
Artificial neural networks based on memristive devices: From device to system
,”
Adv. Intell. Syst.
2
,
2000149
(
2020
).
5.
Y.
Zhang
,
Z.
Wang
,
J.
Zhu
,
Y.
Yang
,
M.
Rao
,
W.
Song
,
Y.
Zhuo
,
X.
Zhang
,
M.
Cui
,
L.
Shen
,
R.
Huang
, and
J. J.
Yang
, “
Brain-inspired computing with memristors: Challenges in devices, circuits, and systems
,”
Appl. Phys. Rev.
7
,
011308
(
2020
).
6.
Z.
Wang
,
C.
L
,
P.
Lin
,
M.
Rao
,
Y.
Nie
,
W.
Song
,
Q.
Qiu
,
Y.
Li
,
P.
Yan
,
J. P.
Strachan
,
N.
Ge
,
N.
McDonald
,
Q.
Wu
,
M.
Hu
,
H.
Wu
,
R. S.
Williams
,
Q.
Xia
, and
J. J.
Yang
, “
In situ training of feed-forward and recurrent convolutional memristor networks
,”
Nat. Mach. Intell.
1
,
434
442
(
2019
).
7.
T.
Gokmen
,
M.
Onen
, and
W.
Haensch
, “
Training deep convolutional neural networks with resistive cross-point devices
,”
Front. Neurosci.
11
,
538
(
2017
).
8.
Y.
Xi
,
B.
Gao
,
J.
Tang
,
A.
Chen
,
M.-F.
Chang
,
X. S.
Hu
,
J.
van der Spiegel
,
H.
Qian
, and
H.
Wu
, “
In-memory learning with analog resistive switching memory: A review and perspective
,”
Proc. IEEE.
109
,
14
42
(
2021
).
9.
J.
Woo
,
K.
Moon
,
J.
Song
,
M.
Kwak
,
J.
Park
, and
H.
Hwang
, “
Optimized programming scheme enabling linear potentiation in filamentary HfO2 RRAM synapse for neuromorphic systems
,”
IEEE Trans. Electron Devices
63
,
5064
5067
(
2016
).
10.
J.
Woo
,
K.
Moon
,
J.
Song
,
S.
Lee
,
M.
Kwak
,
J.
Park
, and
H.
Hwang
, “
Improved synaptic behavior under identical pulses using AlOx/HfO2 bilayer RRAM array for neuromorphic systems
,”
IEEE Electron Device Lett.
37
,
994
997
(
2016
).
11.
J.
Park
,
M.
Kwak
,
K.
Moon
,
J.
Woo
,
D.
Lee
, and
H.
Hwang
, “
TiOx-based RRAM synapse with 64-levels of conductance and symmetric conductance change by adopting a hybrid pulse scheme for neuromorphic computing
,”
IEEE Electron Device Lett.
37
,
1559
1562
(
2016
).
12.
J.-W.
Jang
,
S.
Park
,
G. W.
Burr
,
H.
Hwang
, and
Y.-H.
Jeong
, “
Optimization of conductance change in Pr1−x CaxMnO3-based synaptic devices for neuromorphic systems
,”
IEEE Electron Device Lett.
36
,
457
459
(
2015
).
13.
S.
Kim
,
S.
Choi
,
J.
Lee
, and
W.
Lu
, “
Tuning resistive switching characteristics of tantalum oxide memristors through Si doping
,”
ACS Nano
8
,
10262
10269
(
2014
).
14.
H.
Yeon
,
P.
Lin
,
C.
Choi
,
S. H.
Tan
,
Y.
Park
,
D.
Lee
,
J.
Lee
,
F.
Xu
,
B.
Gao
,
H.
Wu
,
H.
Qian
,
Y.
Nie
,
S.
Kim
, and
J.
Kim
, “
Alloying conducting channels for reliable neuromorphic computing
,”
Nat. Nanotechnol.
15
,
574
579
(
2020
).
15.
K.
Kim
,
S.
Park
,
S. M.
Hu
,
J.
Song
,
W.
Lim
,
Y.
Jeong
,
J.
Kim
,
S.
Lee
,
J. Y.
Kwak
,
J.
Park
,
J. K.
Park
,
B.-K.
Ju
,
D. S.
Jeong
, and
I.
Kim
, “
Enhanced analog synaptic behavior of SiNx/a-Si bilayer memristors through Ge implantation
,”
NPG Asia Mater.
12
,
77
(
2020
).
16.
S.
Choi
,
S. H.
Tan
,
Z.
Li
,
Y.
Kim
,
C.
Choi
,
P.-Y.
Chen
,
H.
Yeon
,
S.
Yu
, and
J.
Kim
, “
SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations
,”
Nat. Mater.
17
,
335
340
(
2018
).
17.
I.-T.
Wang
,
C.-C.
Chang
,
L.-W.
Chiu
,
T.
Chou
, and
T.-H.
Hou
, “
3D Ta/TaOx/TiO2/Ti synaptic array and linearity tuning of weight update for hardware neural network applications
,”
Nanotechnology
27
,
365204
(
2016
).
18.
W.
Wu
,
H.
Wu
,
B.
Gao
,
N.
Deng
,
S.
Yu
, and
H.
Qian
, “
Improving analog switching in HfOx-based resistive memory with a thermal enhanced layer
,”
IEEE Electron Device Lett.
38
,
1019
1022
(
2017
).
19.
S.
Lim
,
M.
Kwak
, and
H.
Hwang
, “
Improved synaptic behavior of CBRAM using internal voltage divider for neuromorphic systems
,”
IEEE Trans. Electron Devices Meet.
65
,
3976
3981
(
2018
).
20.
Y.
Sun
,
H.
Xu
,
C.
Wang
,
B.
Song
,
H.
Liu
,
Q.
Liu
,
S.
Liu
, and
Q.
Li
, “
A Ti/AlOx/TaOx/Pt analog synapse for memristive neural network
,”
IEEE Electron Device Lett.
39
,
1298
1301
(
2018
).
21.
J.
Woo
,
A.
Padovani
,
K.
Moon
,
M.
Kwak
,
L.
Larcher
, and
H.
Hwang
, “
Linking conductive filament properties and evolution to synaptic behavior of RRAM devices for neuromorphic applications
,”
IEEE Electron Device Lett.
38
,
1220
1223
(
2017
).
22.
W.
Wu
,
H.
Wu
,
B.
Gao
,
P.
Yao
,
X.
Zhang
,
X.
Peng
,
S.
Yu
, and
H.
Qian
, “
A methodology to improve linearity of analog RRAM for neuromorphic computing
,” in
Proceedings of the IEEE Symposium on VLSI Technol
ogy (
2018
), pp.
T103
T104
.
23.
E.
Verrelli
,
I.
Michelakaki
,
N.
Boukos
,
G.
Kyriakou
, and
D.
Tsoukalas
, “
Coalescence of cluster beam generated sub-2 nm bare Au nanoparticles and analysis of Au film growth parameters
,”
Ann. Phys.
530
,
1700256
(
2018
).
24.
P.
Bousoulas
,
S.
Stathopoulos
,
D.
Tsialoukis
, and
D.
Tsoukalas
, “
Low-power and highly uniform 3-b multilevel switching in forming free TiO2–x-based RRAM with embedded Pt nanocrystals
,”
IEEE Electron Devices Lett.
37
,
874
877
(
2016
).
25.
P.
Bousoulas
,
D.
Sakellaropoulos
,
C.
Papakonstantinopoulos
,
S.
Kitsios
,
C.
Arvanitis
,
E.
Bagakis
, and
D.
Tsoukalas
, “
Investigating the origins of ultra-short relaxation times of silver filaments in forming-free SiO2-based conductive bridge memristors
,”
Nanotechnology
31
,
454002
(
2020
).
26.
R.
Wang
,
J.-Q.
Yang
,
J.-Y.
Mao
,
Z.-P.
Wang
,
S.
Wu
,
M.
Zhou
,
T.
Chen
,
Y.
Zhou
, and
S.-T.
Han
, “
Recent advances of volatile memristors: Devices, mechanisms, and applications
,”
Adv. Intell. Syst.
2
,
2000055
(
2020
).
27.
H.
Sun
,
Q.
Liu
,
C.
Li
,
S.
Long
,
H.
Lv
,
C.
Bi
,
Z.
Huo
,
L.
Li
, and
M.
Liu
, “
Direct observation of conversion between threshold switching and memory switching induced by conductive filament morphology
,”
Adv. Funct. Mater.
24
,
5679
5686
(
2014
).
28.
Y.
Yang
,
P.
Gao
,
S.
Gaba
,
T.
Chang
,
X.
Pan
, and
W.
Lu
, “
Observation of conducting filament growth in nanoscale resistive memories
,”
Nat. Commun.
3
,
732
(
2012
).
29.
W.
Wang
,
M.
Laudato
,
E.
Ambrosi
,
A.
Bricalli
,
E.
Covi
,
Y.-H.
Lin
, and
D.
Ielmini
, “
Volatile resistive switching memory based on Ag ion drift/diffusion Part I: Numerical modeling
,”
IEEE Trans. Electron Device
66
,
3795
3801
(
2019
).
30.
N. J.
Podraza
,
B. D.
Gauntt
,
M. A.
Motyka
,
E. C.
Dickey
, and
M. W.
Horn
, “
Electrical and optical properties of sputtered amorphous vanadium oxide thin films
,”
J. Appl. Phys.
111
,
073522
(
2012
).
31.
Z.
Yang
,
C.
Ko
,
V.
Balakrishnan
,
G.
Gopalakrishnan
, and
S.
Ramanathan
, “
Dielectric and carrier transport properties of vanadium dioxide thin films across the phase transition utilizing gated capacitor devices
,”
Phys. Rev. B
82
,
205101
(
2010
).
32.
A.
Padovani
,
D. Z.
Gao
,
A. L.
Shluger
, and
L.
Larcher
, “
A microscopic mechanism of dielectric breakdown in SiO2 films: An insight from multi-scale modeling
,”
J. Appl. Phys.
121
,
155101
(
2017
).
33.
J.
Galy
and
P. P.
Monchoux
, “
Spark Plasma synthesis and diffusion of Cu and Ag in vanadium mixed valence oxides
,”
J. Mater Sci.
43
,
6391
6399
(
2008
).
34.
D.
Sakellaropoulos
,
P.
Bousoulas
,
C.
Papakonstantinopoulos
,
S.
Kitsios
, and
D.
Tsoukalas
, “
Spatial confinement effects of embedded nanocrystals on multibit and synaptic properties of forming free SiO2-based conductive bridge random access memory
,”
IEEE Electron Device Lett.
41
,
1013
1016
(
2020
).
35.
N.
Ilyas
,
D.
Li
,
C.
Li
,
X.
Jiang
,
Y.
Jiang
, and
W.
Li
, “
Analog switching and artificial synaptic behavior of Ag/SiOx:Ag/TiOx/p++-Si memristor device
,”
Nanoscale Res. Lett.
15
,
30
(
2020
).
36.
Z.
Wang
,
M.
Yin
,
T.
Zhang
,
Y.
Cai
,
Y.
Wang
,
Y.
Yang
, and
R.
Huang
, “
Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing
,”
Nanoscale
8
,
14015
(
2016
).
37.
Y. J.
Jeong
,
S.
Kim
, and
W. D.
Lu
, “
Utilizing multiple state variables to improve the dynamic range of analog switching in a memristor
,”
Appl. Phys. Lett.
107
,
173105
(
2015
).
38.
S.-M.
Park
,
H.-G.
Hwang
,
J.-U.
Woo
,
W.-H.
Lee
,
S.-J.
Chae
, and
S.
Nahm
, “
Improvement of conductance modulation linearity in Cu-doped KNbO3 memristor through the increase of the number of oxygen vacancies
,”
ACS Appl. Mater. Interfaces
12
,
1069
1077
(
2020
).
39.
Y.
Yang
,
P.
Gao
,
L.
Li
,
X.
Pan
,
S.
Tappertzhofen
,
S.
Choi
,
R.
Waser
,
I.
Valov
, and
W. D.
Lu
, “
Electrochemical dynamics of nanoscale metallic inclusions in dielectrics
,”
Nat. Commun.
5
,
4232
(
2014
).
40.
J. M.
Goodwill
,
D. K.
Gala
,
J. A.
Bain
, and
M.
Skowronski
, “
Intrinsic current overshoot during thermal-runaway threshold switching events in TaOx devices
,”
J. Appl. Phys.
123
,
115105
(
2018
).
41.
M.-L.
Avramescu
,
P. E.
Rasmussen
,
M.
Chénier
, and
H. D.
Gardner
, “
Influence of pH, particle size and crystal form on dissolution behaviour of engineered nanomaterials
,”
Environ. Sci. Pollut. Res. Int.
24
,
1553
1564
(
2017
).
42.
K. K.
Nanda
,
S. N.
Sahu
, and
S. N.
Behera
, “
Liquid-drop model for the size-dependent melting of low-dimensional systems
,”
Phys. Rev. A
66
,
013208
(
2002
).
43.
M. A.
Asoro
,
J.
Damiano
, and
P. J.
Ferreira
, “
Size effects on the melting temperature of silver nanoparticles: In-situ TEM observations
,”
Microsc. Microanal.
15
,
706
707
(
2009
).
44.
W.
Luo
,
W.
Hu
, and
S.
Xiao
, “
Size effect on the thermodynamic properties of silver nanoparticles
,”
J. Phys. Chem. C
112
,
2359
2369
(
2008
).
45.
D. S.
Sheny
,
D.
Philip
, and
J.
Mathew
, “
Synthesis of platinum nanoparticles using dried Anacardium occidentale leaf and its catalytic and thermal applications
,”
Spectrochim. Acta, Part A
114
,
267
271
(
2013
).
46.
F.
Lacy
, “
Using nanometer platinum films as temperature sensors (constraints from experimental, mathematical, and finite-element analysis)
,”
IEEE Sens. J.
9
,
1111
1117
(
2009
).
47.
S. H.
Chang
,
S. C.
Chae
,
S. B.
Lee
,
C.
Liu
,
T. W.
Noh
,
J. S.
Lee
,
B.
Kahng
,
J. H.
Jang
,
M. Y.
Kim
,
D.-W.
Kim
, and
C. U.
Jung
, “
Effects of heat dissipation on unipolar resistance switching in Pt∕NiO∕Pt capacitors
,”
Appl. Phys. Lett.
92
,
183507
(
2008
).
48.
D. C.
Bock
,
K. J.
Takeuchi
,
A. C.
Marschilok
, and
E. S.
Takeuchi
, “
Structural and silver/vanadium ratio effects on silver vanadium phosphorous oxide solution formation kinetics: Impact on battery electrochemistry
,”
Phys. Chem. Chem. Phys.
17
,
2034
2042
(
2015
).
49.
J. D.
McBrayer
,
R. M.
Swanson
, and
T. W.
Sigmon
, “
Diffusion of metals in silicon dioxide
,”
J. Electrochem. Soc.
133
,
1242
1246
(
1986
).
50.
J.-P.
Monchoux
,
M.
Dollé
,
P.
Rozier
, and
J.
Galy
, “
Reaction kinetics during synthesis of CuxV2O5 and AgyV2O5 by spark plasma sintering
,”
Solid State Ionics
182
,
24
31
(
2011
).
51.
Y.
Kobayashi
,
W.
Zheng
,
T. B.
Chang
,
K.
Hirata
,
R.
Suzuki
,
T.
Ohdaira
, and
K.
Ito
, “
Nanoporous structure of sputter-deposited silicon oxide films characterized by positronium annihilation spectroscopy
,”
J. Appl. Phys.
91
,
1704
1706
(
2002
).

Supplementary Material

You do not currently have access to this content.