bion: synthetic pathways to bio-inspired information processing micro-phase separated,...
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BION: SYNTHETIC PATHWAYS TO BIO-INSPIRED INFORMATION PROCESSINGBION: SYNTHETIC PATHWAYS TO BIO-INSPIRED INFORMATION PROCESSING
Micro-phase separated, self-assembled 3D system
TECHNOLOGY AND CHARACTERIZATIONTECHNOLOGY AND CHARACTERIZATION
Organic Memristive Device and its Application to the Information Processing
Victor Erokhin
IPCF, CNR Rome, ItalyDepartment of Physics, University of Parma
ICECS 2010 December 15, 2010 Athens
COMPUTER BRAIN
PROCESSOR MEMORY PROCESSOR AND MEMORY
NEW SYSTEMS WITH LEARNING AND DECISION MAKING CAPABILITIES REQUIRE NEW ELEMENTS
PROPERTIES OF ADAPTIVE (BIO-INSPIRED)NETWORKS
• Integration of processing and memory properties for the network elements
• Very high level of parallel processing
• Learning procedure of the network must be based on combined learning paradigm (supervised and unsupervised learning)
• Hebbian rule: “When an axon of cell A is near enough to excite cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased”
BIOLOGICALLY INSPIRED ADAPTIVE NETWORKS
• Neuron body – allows further transmission of the signal when some threshold level is reached
• Dendrites – income of the signal
• Axon – drain of the signal
• Synapses – variation of the signal pathways and junctions weight functions
SYNAPSES ANALOG: ELECTROCHEMICAL ELEMENT (ORGANIC MEMRISTOR)
PANIS DPEO
G
IG
ID
E.T. Kang, K.G. Neoh, and K.L. Tan, Progr. Polymer Sci., 23, 277-324 (1998)
-400
-200
0
200
400
-1 -0.5 0 0.5 1
voltage (V)
curr
ent
(nA
)
0
200
400
600
800
1000
-1 -0.5 0 0.5 1
voltage (V)
curr
ent
(nA
)
Gate current Differential currentEmpty squares – increasing VFilled squares – decreasing V
V. Erokhin, T. Berzina and M.P. Fontana, J. Appl. Phys., 97, 064501 (2005)
ELECTROCHEMICAL NONLINEAR ELEMENT (adaptive behavior)
0100200300400500600700
0 1000 2000 3000 4000
time (s)
cu
rre
nt
(nA
)
-250
-200
-150
-100
-50
0
0 500 1000 1500 2000
time (s)
cu
rre
nt
(nA
)
Kinetics of drain current variation at positive (+ 0.6 V) bias
Kinetics of drain current variation at negative (- 0.1 V) bias
LiClPANIeLiClPANI :
When biased negatively, Li+ ions penetrate on practically whole depth of active PANI layer,
transferring it into insulator (reduction)
CONFIGURATION OF DEVICEFOR X-RAY FLUORESCENCE MEASUREMENTS
T. Berzina, S. Erokhina, P. Camorani, O. Konovalov, V. Erokhin, and M.P. Fontana, ACS Appl. Mater. Interfaces, 1, 2115-2118 (2009).
Experimental set-up for X-ray fluorescence measurements
Fluorescence spectrum of the sample, acquired during the device functioning (a); temporal behavior of the normalized rubidium fluorescence (b); drain current and
transferred ionic charge (c) of the structure
Conductivity of the device is directly connected to the
transferred ionic charge
v R(w)i
dw
dtiionic
Bernard Widrow’s memistor = 3-terminal memristor
“Like the transistor, the memistor is a 3-terminal element. The conductance between two of the terminals is controlled by the time integral of the current in the third, rather than its instantaneous value as in the transistor.”
-Widrow et al.1 (1961)
1Widrow et al., “Birth, Life, and Death in Microelectronic Systems,” Office of Naval Research Technical Report 1552-2/1851-1, May 30,1961
From the presentation of Blaise Mouttet, Paris 2010, ISCAS 2010
MODEL ADAPTIVE NETWORK
Out 1 (nA) Out 2 (nA)
Before training 120 32After training 65 124
Training by applying –0.5V between 1-st input and 1-st output; +1.2V between 1-st input and 2-nd output
V. Erokhin, T. Berzina, and M.P. Fontana, Cryst. Rep., 52, 159-166 (2007)
Adaptive network with 8 organic memristors fabricated on flexible support
Evaluating the trainingEvaluating the training
Output 1
Output 2
Output 3
Input 3
Input 2
Input 1
GAIN: how well the selected inputs and outputs are connected
G ≡ min(I32/I31, I32/I33)
REVERSE GAIN: how well the selected output is isolated from other inputs
R ≡ min(I32/I12, I32/I22)
Training - path creationTraining - path creation
Homo- (a) and hetero- (b)Synaptic junctions
Model of learning for LimneaStagnalis
Bio-inspired circuits
ARTIFICIAL CIRCUITS WITH HOMO- AND HETEROSYNAPTIC JUNCTIONS
Complex Networks Assembly
Formation of the network by statistical assembling of electrochemical junctions
Realization of fibrillar structuresSelf-assembling with phase separation
• PANI fibers were formed on PEO fibrillar matrix by dropping 0.1-0.2 ml of PANI solution on it, placing the structure into the vacuum chamber, and pumping again for 15-20 min till 10-2 Torr.
• The formed fibers of different diameter of both PEO and PANI (from less than one micron up to tens of microns) and length (up to some millimeters) are clearly visible, as well as the 3D morphology.
PEO –PANI fibrillar networks after vacuum treatment
Optical microphotograph (image size 0.6 x 0.5 mm).
V. Erokhin, T. Berzina, P. Camorani, and M.P. Fontana, Soft Matter., 2, 870 (2006).
FIBRILLAR STRUCTURE WITH 3 ELECTRODES
Is the formed structure complex enough in order to provide by the statistically distributed PANI-PEO fiber interconnections the pathways similar to those directly fabricated in the discrete deterministic device?
In other words, whether some parts of the structure have Ag wire – PEO – PANI heterojunctions?
The third electrode (Ag wire) was inserted into the drop of PEO before vacuum evaporation. Thus, after the formation of PEO and PANI fibers, the wire would be retained in the middle of the fibrillar structure to maintain ground potential level in PEO-PANI junctions in the central part of the structure.
Question:
V. Erokhin, T. Berzina, P. Camorani, and M.P. Fontana, Soft Matter, 2, 870-874 (2006).
0
200
400
600
800
-1 -0.5 0 0.5 1
Voltage (V)
Cu
rre
nt
(nA
)
increase
decrease
V/I characteristics measured in on the drain electrode in 3 electrode circuit.
Non linear electrical characteristics were found, implying the substantial presence of nodes similar to the fabricated device
Clearly visible rectifying behavior of the curve confirms the success of the Clearly visible rectifying behavior of the curve confirms the success of the realization of the desirable heterojunctions in some areas of the formed realization of the desirable heterojunctions in some areas of the formed fibrillar networkfibrillar network
Learning capabilities of the statistically formed network of polymer fibers
Adaptive network composed of conducting/ionic polymers – gold nanoparticles composite structure
Very low stability!
COPOLYMER-PEO-PANI-Au NANOPARTICLES COMPOSITE
Phase separation and formation of 3D structures
Sequential training: red pair then blue pairSimultaneous training: voltages of opposite polarityAre applied to red and blue pairs
In
In
Out
Out
SEQUENTIAL TRAINING RESULTS
Long-term sequential training results in the formation of stable signal pathways with no possibility of next adaptations (baby learning)
SIMULTANEOUS TRAINING RESULTS
Simultaneous training of the 3D statistical network allows multiple adaptations
CONCLUSIONS
• Demonstration of the possibility to realize adaptive network based on electrochemically controlled polymeric structures (organic memristors).
• Connection to biological systems: demonstration of synaptic activity (indicative of learning and memory) in simple material (i.e. Molecular electronic) structures.
• Non-conventional approaches to fabrication of adaptive networks
BION: Synthetic pathways to bio-inspired information processing
• We acknowledge the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under the FET-Open grant agreement BION, number 213219.
PARMA University• Prof. Marco P. Fontana
• Dr. Tatiana Berzina
• Dr. Anteo Smerieri
• Dr. Paolo Camorani
• Dr. Svetlana Erokhina
• Konstantin Gorshkov
PISA University
• Prof. Giacomo Ruggieri
• Dr. Andrea Pucci
Max-Planck Institute Tubingen
• Prof. Valentino Braitenberg
• Prof. Almut Schuz
• Dr. Rodrigo Sigala
WARWICK University
• Prof. Jianfeng Feng
• Dr. Dimitris Vaoulis
Pictures: Filippo Romani