BION: SYNTHETIC PATHWAYS TO BIO-INSPIRED INFORMATION PROCESSING

1. Organic Memristive Device and its Application to the Information Processing Victor Erokhin IPCF, CNR Rome, Italy Department of Physics, University of Parma BION: SYNTHETIC PATHWAYS TO BIO-INSPIRED INFORMATION PROCESSING Micro-phase separated, self-assembled 3Dsystem TECHNOLOGY AND CHARACTERIZATION ICECS 2010 December 15, 2010 Athens

2. COMPUTER BRAIN PROCESSOR MEMORY PROCESSOR AND MEMORY NEW SYSTEMS WITH LEARNING AND DECISION MAKING CAPABILITIES REQUIRE NEW ELEMENTS

3. 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”

4. 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

5. IG G S D PEO PANI ID SYNAPSES ANALOG: ELECTROCHEMICAL ELEMENT (ORGANIC MEMRISTOR) E.T. Kang, K.G. Neoh, and K.L. Tan, Progr. Polymer Sci., 23, 277-324 (1998) Empty squares – increasing V Filled squares – decreasing V Gate current Differential current V. Erokhin, T. Berzina and M.P. Fontana, J. Appl. Phys., 97, 064501 (2005)

6. ELECTROCHEMICAL NONLINEAR ELEMENT (adaptive behavior) Kinetics of drain current variation at positive (+ 0.6 V) bias Kinetics of drain current variation at negative (- 0.1 V) bias When biased negatively, Li+ ions penetrate on practically whole depth of active PANI layer, transferring it into insulator (reduction)

7. CONFIGURATION OF DEVICE FOR X-RAY FLUORESCENCE MEASUREMENTS Experimental set-up for 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).

8. 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

9. 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

10. MODEL ADAPTIVE NETWORK 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)

11. Adaptive network with 8 organic memristors fabricated on flexible support

12. Evaluating the training Input 3 Output 1 Input 2 Output 2 Input 1 Output 3 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)

13. Training - path creation

14. Bio-inspired circuits Homo- (a) and hetero- (b) Synaptic junctions Model of learning for Limnea Stagnalis

15. ARTIFICIAL CIRCUITS WITH HOMO- AND HETEROSYNAPTIC JUNCTIONS

16. Complex Networks Assembly Formation of the network by statistical assembling of electrochemical junctions Realization of fibrillar structures Self-assembling with phase separation

17. PEO –PANI fibrillar networks after vacuum treatment • 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. 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).

18. FIBRILLAR STRUCTURE WITH 3 ELECTRODES 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: 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? V. Erokhin, T. Berzina, P. Camorani, and M.P. Fontana, Soft Matter, 2, 870-874 (2006).

19. Non linear electrical characteristics were found, implying the substantial presence of nodes similar to the fabricated device V/I characteristics measured in on the drain electrode in 3 electrode circuit. Clearly visible rectifying behavior of the curve confirms the success of the realization of the desirable heterojunctions in some areas of the formed fibrillar network

20. Learning capabilities of the statistically formed network of polymer fibers Adaptive network composed of conducting/ionic polymers – gold nanoparticles composite structure Very low stability!

21. COPOLYMER-PEO-PANI-Au NANOPARTICLES COMPOSITE Phase separation and formation of 3D structures

22. Out Out In In Sequential training: red pair then blue pair Simultaneous training: voltages of opposite polarity Are applied to red and blue pairs

23. SEQUENTIAL TRAINING RESULTS Long-term sequential training results in the formation of stable signal pathways with no possibility of next adaptations (baby learning)

24. SIMULTANEOUS TRAINING RESULTS Simultaneous training of the 3D statistical network allows multiple adaptations

25. 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

26. 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.

27. PARMA University Max-Planck Institute Tubingen • Prof. Valentino Braitenberg • Prof. Almut Schuz • Dr. Rodrigo Sigala • Prof. Marco P. Fontana • Dr. Tatiana Berzina • Dr. Anteo Smerieri • Dr. Paolo Camorani • Dr. Svetlana Erokhina • Konstantin Gorshkov WARWICK University PISA University • Prof. Giacomo Ruggieri • Dr. Andrea Pucci • Prof. Jianfeng Feng • Dr. Dimitris Vaoulis Pictures: Filippo Romani

28. THANK YOU