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