Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Pattern recognition with TiOx-based memristive devices

1 Nanoelektronik, Technische Fakultät der Christian-Albrechts-Universität zu Kiel, 24143 Kiel, Germany;
2 Theoretische Elektrotechnik, Fakultät für Elektrotechnik und Informationstechnik, Ruhr-Universität Bochum, 44801 Bochum, Germany

Special Issues: Nanomaterials for Cognitive Technology

We report on the development of TiOx-based memristive devices for bio-inspired neuromorphic systems. In particular, capacitor like structures of Al/AlOx/TiOx/Al with, respectively 20 nm and 50 nm thick TiOx-layers were fabricated and analyzed in terms of their use in neural network circuits. Therefore, an equivalent circuit model is presented which mimics the observed device properties on a qualitative level and relies on mobile oxygen ions by taking electronic transport through local conducting filaments and hopping between TiOx defect states into account. The model also comprises back diffusion of oxygen ions and allows for a realistic description of the experimental recorded device characteristics. The in Refs. [1-3] reported computing paradigms for pattern recognition have been used as guidelines for a device performance investigation at the network level. In particular, simulations of a spiking neural network are presented which allows for pattern recognition. As input patterns hand written digits taken from the MNIST Data base have been used. Within the network the memristive devices are arranged in a cross-bar array connected by 196 input neurons and ten output neurons. While, each input neuron corresponds to a specific pixel of the image of the input pattern, the output neurons were implemented as spiking neurons. In addition, the output neurons were inhibitory linked within an winner-take-it-all network and consist of a homeostasis-like behavior for their spiking thresholds. Based on the network simulation essential requirements for the development of optimal memristive device for neuromorphic circuits are discussed.
  Article Metrics


1. Querlioz D, Bichler O, Gamrat C (2011) Simulation of a Memristor-Based Spiking Neural Network Immune to Device Variations. Int Jt Conf Neural Netw (IJCNN) 1775-1781.

2. Querlioz D, Bichler O, Dollfus P (2013) Immunity to Device Variations in a Spiking Neural Network with Memristive Nanodevices, IEEE Transactions on Nanotechnology 12: 288-295

3. Sheridan P, Ma W, Lu W (2014) Pattern Recognition with Memristor Networks, Circuits and Systems (ISCAS) 1078-1081.

4. Shanahan T (2004) The Evolution of Darwinism, Selection Adaption and progress in Evolutionary Biology, Cambridge.

5. Liu SC, Kramer J, Indiveri G, et al. (2002) Analog VLSI: Circuits and Principles, MIT Press Massachusetts.

6. Chicca E, Stefanini F, Bartolozzi C, et al. (2014) Neuromorphic Elecroncis Circuits for Building Autonomous Cognitive Systems. Proc IEEE 102: 1397-1388.    

7. Würtz RP (2008) Organic Computing, Understanding Complex Systems. Springer Berlin-Heidelberg.

8. Amit D J (1989) Modeling Brain Function, The world of attractor neural networks. Cambridge University Press.

9. Strukov DB, Snider GS, Stewart DR, et al. (2008), The missing memristor found. Nature 453: 80-83.

10. Chua OL (1971) Memristor-The Missing Circuit Element. IEEE Trans. On Cir Theo 18: 507-519.    

11. Itoh K, Horiguchi M, Tanaka H (2006) Ultra-low Voltage Nano-Scale memories. Springer Science and Business

12. Nawathe UG (2010) Design of High Performance Low Power Microprocessors, In Iniewski K, CMOS Processors and Memories, Springer Science and Business, 3-27

13. Tetzlaff R (2014) Memristors and memristive Systems. Springer New York.

14. Ziegler M, Soni R, Patelczyk T, et al. (2012) An electronic version of Pavlov's dog. Adv Func Mat 22: 2744-2749.    

15. Ziegler M, Ochs K, Hansen M, et al., (2014) An electronic implementation of amoeba anticipation: Appl Phys A 114: 565-570.

16. Pershin YV, La Fontaine S, Di Ventra M (2009) Memristive model of amoeba learning. Phys Rev E 80: 021926.

17. Bichler O, Zhao W, Alibart F, et al. (2013) Pavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors. Neural Comput 25: 549.    

18. Yu S, Gao B, Fang Z, et al. (2013) Stochastic learning in oxide binary synaptic device for neuromorphic computing. Front Neurosci 7: 186.

19. Yu S, Gao B, Fang Z, et al. (2013) A Low Energy Oxide-Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation. Adv Mater 25: 1774-1779.    

20. Stewart TC, Choo FX, Eliasmith C (2012) Spaun: A perception-cognition-action model using spiking neurons. Proc 34th Ann Conf Cognitive Sci Soci.

21. Waser R, Dittmann R, Staikov G, et al. (2009) Redox-Based Resistive Switching Memories - Nanoionic Mechanisms, Prospects, and Challenges. Adv Mater 21: 2632-2663.    

22. Szot K, Speier W, Bihlmayer G, et al. (2006) Switching the electrical resistance of individual dislocations in single-crystalline SrTiO3. Nat Mater 5: 312-320.    

23. Kozma R, Robinso EP, Pazienza GE (2012) Advances in Neuromorphic Memristor Science and Applications. Springer Dordrecht, Heidelberg London New York.

24. Adamatzky A, Chua L (2014) Memristor Networks, Springer Heidelberg, NewYork Dordrecht London.

25. Biolek Z, Biolek D, Biolkova V (2009) SPICE model of memristor with nonlinear dopant drift. Radioengineering 18: 210-214.

26. Jeong HY, Lee JY, Choi SY, et al. (2009) Microscopic origin of bipolar resistive switching of nanoscale titanium oxide thin films. Appl Phys Lett 95:162108

27. Choi BJ, Jeong DS, Kim SK, et al. (2005) Resistive switching mechanism of TiO2 thin films grown by atomic-layer deposition. J Appl Phys 98: 033715.    

28. Yang JJ, Pickett MD, Li X, et al. (2008) Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotechnol 3: 429-433.    

29. Zamarreño-Ramos C, Camuñas-Mesa LA, Pérez-Carrasco JA, et al. (2011) On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. Front Neurosci 5: 1-22.

30. Ziegler M, Riggert C, Hansen M, et al. (2015) Memristive Hebbian Plasticity Model: Device Requirements for the Emulation of Hebbian Plasticity Based on Memristive Devices. IEEE Trans Biomed Circuits Syst 9: 197-206.    

31. Caporale N, Dan Y (2008) Spike Timing-Dependent Plasticity: A Hebbian Learning Rule. Ann Rev Neurosci 31: 25-46.    

32. Byrne JH, Heidelberger R, Waxham MN (Eds.) (2014) From molecules to networks: an introduction to cellular and molecular neuroscience. Academic Press.

33. Gerstner W, Kistler WM (2002) Spiking neuron models: Single neurons, populations, plasticity. Cambridge university press.

34. LeCun Y, Bottou L, Bengio Y, et al. (1998) Gradient-based learning applied to document recognition. Proc IEEE 86: 2278-2324.    

Copyright Info: © 2015, Martin Ziegler, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved