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First demonstration of “Leaky Integrate and Fire” artificial neuron behavior on (V0.95Cr0.05)2O3 thin film

Published online by Cambridge University Press:  15 May 2018

Coline Adda*
Affiliation:
Institut des Matériaux Jean Rouxel (IMN), Université de Nantes, CNRS, 2 rue de la Houssinière, BP 32229, 44322 Nantes Cedex 3, France CIC nanoGUNE, Tolosa Hiribidea 76, 20018 Donostia-San Sebastian, Spain
Laurent Cario*
Affiliation:
Institut des Matériaux Jean Rouxel (IMN), Université de Nantes, CNRS, 2 rue de la Houssinière, BP 32229, 44322 Nantes Cedex 3, France
Julien Tranchant
Affiliation:
Institut des Matériaux Jean Rouxel (IMN), Université de Nantes, CNRS, 2 rue de la Houssinière, BP 32229, 44322 Nantes Cedex 3, France
Etienne Janod
Affiliation:
Institut des Matériaux Jean Rouxel (IMN), Université de Nantes, CNRS, 2 rue de la Houssinière, BP 32229, 44322 Nantes Cedex 3, France
Marie-Paule Besland
Affiliation:
Institut des Matériaux Jean Rouxel (IMN), Université de Nantes, CNRS, 2 rue de la Houssinière, BP 32229, 44322 Nantes Cedex 3, France
Marcelo Rozenberg
Affiliation:
LPS, CNRS-UMR8502, Université de Paris-Sud, Orsay 91405, France
Pablo Stoliar
Affiliation:
CIC nanoGUNE, Tolosa Hiribidea 76, 20018 Donostia-San Sebastian, Spain
Benoit Corraze
Affiliation:
Institut des Matériaux Jean Rouxel (IMN), Université de Nantes, CNRS, 2 rue de la Houssinière, BP 32229, 44322 Nantes Cedex 3, France
*
Address all correspondence to Laurent Cario at [email protected], [email protected]
Address all correspondence to Laurent Cario at [email protected], [email protected]
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Abstract

A great challenge in the field of neurocomputing is to mimic the brain behavior by implementing artificial synapses and neurons directly in hardware. This work shows that a Leaky Integrate and Fire (LIF) artificial neuron can be realized with a two-terminal device made of Mott insulator thin films. Polycrystalline thin films of the well-known Mott insulator oxide (V0.95Cr0.05)2O3 were deposited by magnetron sputtering and patterned with micron-scale TiN electrodes. These devices exhibit a volatile resistive switching and a remarkable LIF behavior under a train of pulses suggesting that LIF artificial neurons may be realized from (V0.95Cr0.05)2O3 thin films.

Type
Research Letters
Copyright
Copyright © Materials Research Society 2018 

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References

1.Jain, A.K., Mao, J., and Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer (Long. Beach. Calif) 29, 31 (1996).Google Scholar
2.Alvado, L., Tomas, J., Saïghi, S., Renaud, S., Bal, T., Destexhe, A., and Le Masson, G.: Hardware Computation of Conductance-Based Neuron Models. Neurocomputing 58–60, 109 (2004).Google Scholar
3.Baptista, D., Abreu, S., Freitas, F., Vasconcelos, R., and Morgado-Dias, F.: A survey of software and hardware use in artificial neural networks. Neural Comput. Appl. 23, 591 (2013).Google Scholar
4.Yang, J.J., Strukov, D.B., and Stewart, D.R.: Memristive devices for computing. Nat. Nanotechnol. 8, 13 (2013).Google Scholar
5.Merolla, P.A., Arthur, J.V., Alvarez-Icaza, R., Cassidy, A.S., Sawada, J., Akopyan, F., Jackson, B.L., Imam, N., Guo, C., Nakamura, Y., Brezzo, B., Vo, I., Esser, S.K., Appuswamy, R., Taba, B., Amir, A., Flickner, M.D., Risk, W.P., Manohar, R., and Modha, D.S.: Artificial brains. a million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668 (2014).Google Scholar
6.Indiveri, G., Linares-Barranco, B., Hamilton, T.J., van Schaik, A., Etienne-Cummings, R., Delbruck, T., Liu, S.-C., Dudek, P., Häfliger, P., Renaud, S., Schemmel, J., Cauwenberghs, G., Arthur, J., Hynna, K., Folowosele, F., Saighi, S., Serrano-Gotarredona, T., Wijekoon, J., Wang, Y., and Boahen, K.: Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 0 (2011).Google Scholar
7.Strukov, D.B., Snider, G.S., Stewart, D.R., and Williams, R.S.: The missing Memristor found. Nature 453, 80 (2008).Google Scholar
8.Chua, L.: Resistance switching memories are memristors. Appl. Phys. A 102, 765 (2011).Google Scholar
9.Jo, S.H., Chang, T., Ebong, I., Bhadviya, B.B., Mazumder, P., and Lu, W.: Nanoscale Memristor device as synapse in neuromorphic systems. Nano Lett. 10, 1297 (2010).Google Scholar
10.Kuzum, D., Yu, S., and Philip Wong, H.-S.: Synaptic electronics: materials, devices and applications. Nanotechnology 24, 382001 (2013).Google Scholar
11.Yang, Y. and Lu, W.: Nanoscale resistive switching devices: mechanisms and modeling. Nanoscale 5, 10076 (2013).Google Scholar
12.Jeong, D.S., Kim, I., Ziegler, M., and Kohlstedt, H.: Towards artificial neurons and synapses: a materials point of view. RSC Adv. 3, 3169 (2013).Google Scholar
13.Eyert, V.: The metal-insulator transition of NbO2: an embedded Peierls instability. EPL Europhys. Lett. 58, 851 (2002).Google Scholar
14.Valov, I.: Redox-based resistive switching memories (ReRAMs): electrochemical systems at the atomic scale. ChemElectroChem 1, 26 (2014).Google Scholar
15.Pickett, M.D., Medeiros-Ribeiro, G., and Williams, R.S.: A scalable Neuristor built with Mott Memristors. Nat. Mater. 12, 114 (2012).Google Scholar
16.Mehonic, A. and Kenyon, A.J.: Emulating the electrical activity of the neuron using a silicon oxide RRAM cell. Front. Neurosci. 10, 57 (2016).Google Scholar
17.Torrejon, J., Riou, M., Araujo, F.A., Tsunegi, S., Khalsa, G., Querlioz, D., Bortolotti, P., Cros, V., Yakushiji, K., Fukushima, A., Kubota, H., Yuasa, S., Stiles, M.D., and Grollier, J.: Neuromorphic computing with nanoscale spintronic oscillators. Nature 547, 428 (2017).Google Scholar
18.Stoliar, P., Tranchant, J., Corraze, B., Janod, E., Besland, M.-P., Tesler, F., Rozenberg, M., and Cario, L.: A leaky-integrate-and-fire neuron analog realized with a Mott insulator. Adv. Funct. Mater. 27, 1604740 (2017).Google Scholar
19.Querré, M., Janod, E., Cario, L., Tranchant, J., Corraze, B., Bouquet, V., Deputier, S., Cordier, S., Guilloux-Viry, M., and Besland, M.-P.: Metal–insulator transitions in (V1−XCrx)2O3 thin films deposited by reactive direct current magnetron co-sputtering. Thin Solid Films 617, 56 (2016).Google Scholar
20.Querré, M., Tranchant, J., Corraze, B., Cordier, S., Bouquet, V., Députier, S., Guilloux-Viry, M., Besland, M.-P., Janod, E., and Cario, L.: Non-volatile resistive switching in the Mott insulator (V1−xCrx)2O3. Phys. B, Condens. Matter 536, 327 (2017).Google Scholar
21.Guiot, V., Cario, L., Janod, E., Corraze, B., Ta Phuoc, V., Rozenberg, M., Stoliar, P., Cren, T., and Roditchev, D.: Avalanche breakdown in GaTa4Se8−xTex narrow-gap Mott insulators. Nat. Commun. 4, 1722 (2013).Google Scholar
22.Pan, F., Gao, S., Chen, C., Song, C., and Zeng, F.: Recent progress in resistive random access memories: materials, switching mechanisms, and performance. Mater. Sci. Eng. R, Rep. 83, 1 (2014).Google Scholar
23.Stoliar, P., Cario, L., Janod, E., Corraze, B., Guillot-Deudon, C., Salmon-Bourmand, S., Guiot, V., Tranchant, J., and Rozenberg, M.: Universal electric-field-driven resistive transition in narrow-gap Mott insulators. Adv. Mater. 25, 3222 (2013).Google Scholar
24.Janod, E., Tranchant, J., Corraze, B., Querré, M., Stoliar, P., Rozenberg, M., Cren, T., Roditchev, D., Phuoc, V.T., Besland, M.-P., and Cario, L.: Resistive switching in Mott insulators and correlated systems. Adv. Funct. Mater. 25, 6287 (2015).Google Scholar
25.Cario, L., Vaju, C., Corraze, B., Guiot, V., and Janod, E.: Electric-field-induced resistive switching in a family of Mott insulators: towards non-volatile Mott-RRAM memories. Adv. Mater. 22, 5193 (2010).Google Scholar
26.Stoliar, P., Diener, P., Tranchant, J., Corraze, B., Brière, B., Ta-Phuoc, V., Bellec, N., Fourmigué, M., Lorcy, D., Janod, E., and Cario, L.: Resistive switching induced by electric pulses in a single-component molecular Mott insulator. J. Phys. Chem. C 119, 2983 (2015).Google Scholar
27.Brunel, N. and van Rossum, M.C.W.: Lapicque's 1907 paper: from frogs to integrate-and-fire. Biol. Cybern. 97, 337 (2007).Google Scholar
28.Lapicque, L.: Quantitative investigations of electrical nerve excitation treated as polarization. 1907. Biol. Cybern. 97, 341 (2007).Google Scholar
29.Stoliar, P., Rozenberg, M., Janod, E., Corraze, B., Tranchant, J., and Cario, L.: Nonthermal and purely electronic resistive switching in a Mott memory. Phys. Rev. B 90, 045146 (2014).Google Scholar
30.Vaju, C., Cario, L., Corraze, B., Janod, E., Dubost, V., Cren, T., Roditchev, D., Braithwaite, D., and Chauvet, O.: Electric-pulse-driven electronic phase separation, insulator–metal transition, and possible superconductivity in a Mott insulator. Adv. Mater. 20, 2760 (2008).Google Scholar
31.Tranchant, J., Janod, E., Corraze, B., Stoliar, P., Rozenberg, M., Besland, M.-P., and Cario, L.: Control of resistive switching in AM4Q8 narrow Gap Mott insulators: a first step towards neuromorphic applications: control of resistive switching in AM4Q8 narrow gap Mott insulators. Phys. Status Solidi A 212, 239 (2015).Google Scholar