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Organic Memristive Devices and Organic Electrochemical Transistors as Promising Elements for Bio-inspired Systems

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Memristor Computing Systems

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Abstract

Information processing in the brain takes place in a dense network of neurons connected through synapses. The collaborative work between these two components (Synapses and Neurons) allows basic brain functions such as learning and memorization. An efficient emulation of these computational concepts is possible only by overcoming the so-called von Neumann bottleneck, which limits the information processing capability of conventional systems. To this end, the mimicking of the neuronal architectures with silicon-based circuits, on which neuromorphic engineering is based, is accompanied by the development of new devices with neuromorphic functionalities. Several devices are reported to be suitable for this purpose among which organic-based elements are considered particularly attractive for their low fabrication costs, the easy tunability of their electronic properties and the possibility to directly interface biologic systems. This chapter is devoted to the description of some neuromorphic applications of two types of organic electronic devices based on conductive polymers.

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Notes

  1. 1.

    https://www.nature.com/subjects/synaptic-plasticity#:~:text=Synaptic%20plasticity%20is%20the%20biological,the%20expression%20of%20synaptic%20plasticity.

References

  1. Meisel M, Pappas V, Zhang L (2010) A taxonomy of biologically inspired research in computer networking. Comput Netw 54(6):901–916

    Article  MATH  Google Scholar 

  2. Dressler F, Akan OB (2010) A survey on bio-inspired networking. Comput Netw 54(6):881–900

    Article  MATH  Google Scholar 

  3. Rathore H (2016) Mapping biological systems to network systems. Springer

    Google Scholar 

  4. Azevedo FA, Carvalho LR, Grinberg LT, Farfel JM, EL Ferretti R, Leite REP, Filho WJ, Lent R, Herculano-Houzel S (2009) Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J Comput Neurol 513(5):532–541 (2009)

    Google Scholar 

  5. Pakkenberg B et al (2003) Aging and the human neocortex. Exp Gerontol 38(1–2):95–99

    Article  Google Scholar 

  6. Purves D et al (2008) Cognitive neuroscience. Sinauer Associates Inc, Sunderland

    Google Scholar 

  7. Purves D (2012) Neuroscience. Oxford University Press

    Google Scholar 

  8. Grollier J, Querlioz D, Stiles MD (2016) Spintronic nanodevices for bioinspired computing. Proc IEEE 104(10):2024–2039

    Article  Google Scholar 

  9. Chanthbouala A et al (2012) A ferroelectric memristor. Nat Mater 11(10):860–864

    Article  Google Scholar 

  10. Grollier J et al (2020) Neuromorphic spintronics. Nat Electron 1–11

    Google Scholar 

  11. Prezioso M et al (2015) Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521(7550):61–64

    Article  Google Scholar 

  12. Strukov D.B et al (2008) The missing memristor found. Nature 453(7191):80–83

    Google Scholar 

  13. Yang JJ et al (2008) Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotechnol 3(7):429–433

    Article  Google Scholar 

  14. Chang CF et al (2017) Direct observation of dual-filament switching behaviors in Ta2O5-Based memristors. Small 13(15):1603116

    Article  Google Scholar 

  15. Jo SH et al (2010) Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett 10(4):1297–1301

    Article  Google Scholar 

  16. Russo U et al (2007) Conductive-filament switching analysis and self-accelerated thermal dissolution model for reset in NiO-based RRAM. In: 2007 IEEE International Electron Devices Meeting. IEEE

    Google Scholar 

  17. Battistoni S, Dimonte A, Erokhin V (2017) Organic memristor based elements for bio-inspired computing. Advances in Unconventional Computing. Springer, pp 469–496

    Chapter  Google Scholar 

  18. Battistoni S, Erokhin V, Iannotta S (2018) Organic memristive devices for perceptron applications. J Phys D: Appl Phys 51(28):284002

    Google Scholar 

  19. Zhong YN et al (2018) Synapse-Like organic thin film memristors. Adv Func Mater 28(22):1800854

    Article  Google Scholar 

  20. Novembre C et al (2008) Gold nanoparticle-pentacene memory transistors. Appl Phys Lett 92(10):94

    Article  Google Scholar 

  21. Goswami S et al (2017) Robust resistive memory devices using solution-processable metal-coordinated azo aromatics. Nat Mater 16(12):1216–1224

    Article  Google Scholar 

  22. Minnekhanov AA et al (2019) Parylene based memristive devices with multilevel resistive switching for neuromorphic applications. Sci Rep 9(1):1–9

    Article  Google Scholar 

  23. Choi HY et al (2017) Organic electronic synapses with pinched hystereses based on graphene quantum-dot nanocomposites. NPG Asia Mater 9(7):e413–e413

    Article  Google Scholar 

  24. Murgunde B, Rabinal M (2017) Solution processed bilayer junction of silk fibroin and semiconductor quantum dots as multilevel memristor devices. Org Electron 48:276–284

    Article  Google Scholar 

  25. Kim SG et al (2018) Recent advances in memristive materials for artificial synapses. Adv Mater Technol 3(12):1800457

    Article  Google Scholar 

  26. Fu T et al (2020) Bioinspired bio-voltage memristors. Nat Commun 11(1):1–10

    Article  Google Scholar 

  27. van De Burgt Y et al (2018) Organic electronics for neuromorphic computing. Nat Electron 1 (2018)

    Google Scholar 

  28. Heremans P et al (2011) Polymer and organic nonvolatile memory devices. Chem Mater 23(3):341–358

    Article  Google Scholar 

  29. Scott JC, Bozano LD (2007) Nonvolatile memory elements based on organic materials. Adv Mater 19(11):1452–1463

    Article  Google Scholar 

  30. van de Burgt Y et al (2017) A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat Mater 16(4):414

    Article  Google Scholar 

  31. Lai Q et al (2010) Ionic/electronic hybrid materials integrated in a synaptic transistor with signal processing and learning functions. Adv Mater 22(22):2448–2453

    Article  Google Scholar 

  32. Chua L (1971) Memristor-the missing circuit element. IEEE Trans Circuit Theory 18(5):507–519

    Article  Google Scholar 

  33. Chua LO, Kang SM (1976) Memristive devices and systems. Proc IEEE 64(2):209–223

    Article  MathSciNet  Google Scholar 

  34. Erokhin V, Berzina T, Fontana MP (2005) Hybrid electronic device based on polyaniline-polyethyleneoxide junction. J Appl Phys 97(6):064501 (2005)

    Google Scholar 

  35. Battistoni S, Dimonte A, Erokhin V (2016) Spectrophotometric characterization of organic memristive devices. Org Electron 38:79–83

    Article  Google Scholar 

  36. Lapkin D et al (2018) Polyaniline-based memristive microdevice with high switching rate and endurance. Appl Phys Lett 112(4):043302 (2018)

    Google Scholar 

  37. Battistoni S et al, On the interpretation of hysteresis loop for electronic and ionic currents in organic memristive devices. Physica Status Solidi (a) n/a(n/a):1900985

    Google Scholar 

  38. Demin V et al (2014) Electrochemical model of the polyaniline based organic memristive device. J Appl Phys 116(6):064507

    Google Scholar 

  39. Baldi G et al (2014) Logic with memory: and gates made of organic and inorganic memristive devices. Semicond Sci Technol 29(10):104009

    Google Scholar 

  40. Erokhin V, Howard GD, Adamatzky A (2012) Organic memristor devices for logic elements with memory. Int J Bifurc Chaos 22(11):1250283

    Article  MathSciNet  Google Scholar 

  41. Battistoni S, Erokhin V, Iannotta S (2019) Frequency driven organic memristive devices for neuromorphic short term and long term plasticity. Org Electron 65:434–438

    Article  Google Scholar 

  42. Smerieri A.et al (2008) Polymeric electrochemical element for adaptive networks: pulse mode. J Appl Phys 104(11):114513

    Google Scholar 

  43. Davis RL, Zhong Y (2017) The biology of forgetting—a perspective. Neuron 95(3):490–503

    Article  Google Scholar 

  44. Prudnikov N et al (2020) Associative STDP-like learning of neuromorphic circuits based on polyaniline memristive microdevices. J Phys D: Appl Phys

    Google Scholar 

  45. Erokhin V, Berzina T, Fontana M (2007) Polymeric elements for adaptive networks. Crystallogr Rep 52(1):159–166

    Article  Google Scholar 

  46. Erokhin V et al (2011) Material memristive device circuits with synaptic plasticity: learning and memory. BioNanoScience 1(1–2):24–30

    Article  Google Scholar 

  47. Bayat FM et al (2018) Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits. Nat Commun 9(1):2331

    Article  Google Scholar 

  48. Alibart F, Zamanidoost E, Strukov DB (2013) Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat Commun 4:2072

    Article  Google Scholar 

  49. Demin V et al (2015) Hardware elementary perceptron based on polyaniline memristive devices. Org Electron 25:16–20

    Article  Google Scholar 

  50. Emelyanov A et al (2016) First steps towards the realization of a double layer perceptron based on organic memristive devices. Aip Adv 6(11):111301

    Google Scholar 

  51. Eryilmaz SB et al (2014) Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front Neurosci 8:205

    Article  Google Scholar 

  52. Kaneko Y, Nishitani Y, Ueda M (2014) Ferroelectric artificial synapses for recognition of a multishaded image. IEEE Trans Electron Dev 61(8):2827–2833

    Article  Google Scholar 

  53. Li C et al (2018) Analogue signal and image processing with large memristor crossbars. Nat Electron 1(1):52

    Article  Google Scholar 

  54. Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386

    Article  Google Scholar 

  55. Talanov M, Gerasimov Y, Erokhin V (2018) Electronic schematic for bio-plausible dopamine neuromodulation of eSTDP and iSTDP. arXiv:1806.04703

  56. Talanov M et al (2017) Dopamine Modulation via Memristive Schematic. arXiv:1709.06325

  57. Dimonte A, Berzina T, Erokhin V (2015) Physarum Polycephalum changes polyaniline properties. In: Artificial life conference proceedings, vol 13. MIT Press

    Google Scholar 

  58. Romeo A et al (2015) A bio-inspired memory device based on interfacing Physarum polycephalum with an organic semiconductor. APL Mater 3(1):014909

    Google Scholar 

  59. Juzekaeva E et al (2019) Coupling cortical neurons through electronic memristive synapse. Adv Mater Technol 4(1):1800350

    Article  Google Scholar 

  60. Inal S, Malliaras GG, Rivnay J (2017) Benchmarking organic mixed conductors for transistors. Nat Commun 8(1):1–7

    Article  Google Scholar 

  61. Sun J, Fu Y, Wan Q (2018) Organic synaptic devices for neuromorphic systems. J Phys D: Appl Phys 51(31):314004

    Google Scholar 

  62. Rivnay J et al (2018) Organic electrochemical transistors

    Google Scholar 

  63. Malliaras G et al (2018) Organic electrochemical transistors

    Google Scholar 

  64. Gkoupidenis P et al (2015) Neuromorphic functions in PEDOT:PSS organic electrochemical transistors. Adv Mater 27(44):7176–7180

    Article  Google Scholar 

  65. Di Lauro M et al (2020) Tunable short-term plasticity response in three-terminal organic neuromorphic devices. ACS Appl Electron Mater

    Google Scholar 

  66. Yamamoto S, Malliaras GG (2020) Controlling neuromorphic behavior of organic electrochemical transistors by blending mixed and ion conductors. ACS Appl Electron Mater

    Google Scholar 

  67. Ling H et al (2019) Dynamically reconfigurable short-term synapse with millivolt stimulus resolution based on organic electrochemical transistors. Adv Mater Technol 4(9):1900471

    Article  Google Scholar 

  68. Qian C et al (2017) Multi-gate organic neuron transistors for spatiotemporal information processing. Appl Phys Lett 110(8):083302

    Google Scholar 

  69. Winther-Jensen B, Kolodziejczyk B, Winther-Jensen O (2015) New one-pot poly (3, 4-ethylenedioxythiophene): poly (tetrahydrofuran) memory material for facile fabrication of memory organic electrochemical transistors. APL Mater 3(1), 014903

    Google Scholar 

  70. Gkoupidenis P et al (2015) Synaptic plasticity functions in an organic electrochemical transistor. Appl Phys Lett 107(26):263302

    Google Scholar 

  71. Battistoni S et al (2019) Synaptic response in organic electrochemical transistor gated by a graphene electrode. Flex Printed Electron 4(4):044002

    Google Scholar 

  72. Gkoupidenis P, Koutsouras DA, Malliaras GG (2017) Neuromorphic device architectures with global connectivity through electrolyte gating. Nat Commun 8(1):1–8

    Article  Google Scholar 

  73. Koutsouras DA et al (2019) Functional connectivity of organic neuromorphic devices by global voltage oscillations. Adv Intell Syst 1(1):1900013

    Article  Google Scholar 

  74. Pecqueur S, Vuillaume D, Alibart F (2018) Perspective: organic electronic materials and devices for neuromorphic engineering. J Appl Phys 124(15):151902

    Google Scholar 

  75. Pecqueur S et al (2018) Neuromorphic time-dependent pattern classification with organic electrochemical transistor arrays. Adv Electron Mater 4(9):1800166

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Materials for Electronics and Magnetism, Italian National Council of Research, IMEM-CNR, Parco Area delle Scienze 37A, 43124, Parma, Italy

    Silvia Battistoni

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  1. Silvia Battistoni
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Correspondence to Silvia Battistoni .

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Editors and Affiliations

  1. Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkley, CA, USA

    Leon O. Chua

  2. Fundamentals of Electrical Engineering, TU Dresden, Dresden, Germany

    Ronald Tetzlaff

  3. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Angela Slavova

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Battistoni, S. (2022). Organic Memristive Devices and Organic Electrochemical Transistors as Promising Elements for Bio-inspired Systems. In: Chua, L.O., Tetzlaff, R., Slavova, A. (eds) Memristor Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-90582-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-90582-8_12

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  • Online ISBN: 978-3-030-90582-8

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