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ANN Circuit Application of Complementary Resistive Switches

Year 2019, Volume: 7 Issue: 1, 34 - 43, 31.01.2019
https://doi.org/10.17694/bajece.457902

Abstract

Artificial neural networks are successfully used for classification,
prediction, estimation, modeling and system control. However, artificial neural
networks integrated circuits are expensive and not matured enough. Memristors
or memristive systems which show a nonvolatile memory behavior has a high
potential for use in artificial neural network circuit applications. Some
memristive synapse or memristive neural network applications already exist in
literature. The complementary memristor or resistive switch memories have been
suggested as an alternative to one-cell memristor memories. Their sensing is
more difficult and complex than the others. The complementary memristor memory
topologies with a sensing node are also inspected in literature. To the best of
our knowledge, a neural network circuit which is based on the complementary
resistive switches with a sensing/writing node does not exist in literature
yet.  In this paper, several neural
network circuits which are based on the complementary resistive switches with a
sensing/writing node have been designed and examined for the first time in
literature. Their analysis are given and simulations are performed to verify
their operation. We expect that such a complementary resistive switch
implementation may find use in artificial neural networks chips in the future.

References

  • [1] Dan W. Patterson, Artificial neural networks: theory and applications. Prentice Hall PTR, 1998.
  • [2] M. Janardan, I. Saha, "Artificial neural networks in hardware: A survey of two decades of progress", Neurocomputing Vol:74, No:1, 2010, pp.239-255.
  • [3] D. F. Morgado, A. Antunes, A. M. Mota, "Artificial neural networks: a review of commercial hardware", Engineering Applications of Artificial Intelligence, Vol:17, No:8, 2004, pp.945-952.
  • [4] D.B. Strukov, G.S. Snider, , D.R. Stewart, R.S. Williams, “The missing memristor found”, Nature, Vol: 453, 2008, pp. 80-83.
  • [5] L.O. Chua, “Memristor - the missing circuit element”, IEEE Trans Circuit Theory, Vol.18, 1971, pp. 507-519.
  • [6] T. Prodromakis, C. Toumazou “A Review on Memristive Devices and Applications“ Electronics, Circuits, and Systems (ICECS), 17th IEEE International Conference on, 2010, pp. 934 – 937.
  • [7] L. Chua, "Resistance switching memories are memristors." Applied Physics A, Vol.102, No.4, 2011, pp. 765-783.
  • [8] E. Linn, R. Rosezin, C. Kügeler, R. Waser, “Complementary resistive switches for passive nanocrossbar memories” Nature Mater., vol.9, 2010, pp. 403-406.
  • [9] M. A. Zidan, H. H. Fahmy, M. M. Hussain, K.N. Salama, “Memristor-based memory: The sneak paths problem and solutions” Microelectronics Journal, Vol.44, No.2, 2012, pp. 176-183.
  • [10] R. Rosezin, E. Linn, L. Nielen, C. Kügeler, R. Bruchhaus, R. Waser, “Integrated Complementary Resistive Switches for Passive High-Density Nanocrossbar Arrays”, Electron Device Letters, vol. 32, No.2, 2011, pp. 191-193.
  • [11] A. Fabien, E. Zamanidoost, D. B. Strukov. "Pattern classification by memristive crossbar circuits using ex situ and in situ training", Nature communications, Vol. 4, 2013, p.2072.
  • [12] L. Wang, D. Meitao, D. Shukai, "Memristive perceptron for combinational logic classification", Mathematical Problems in Engineering, 2013.[13] B. Li, Y. Wang, Y. Wang, Y. Chen, H. Yang, “Training itself: Mixed-signal training acceleration for memristor-based neural network”, In Design Automation Conference (ASP-DAC), 2014 19th Asia and South Pacific, 2014, pp. 361-366.
  • [14] http://www.technologyreview.com/news/537211/a-better-way-to-build-brain-inspired-chips/
  • [15] E. Karakulak, R. Mutlu, E. Uçar, “Reconstructive sensing circuit for complementary resistive switches based crossbar memories”, Turk J Elec Eng & Comp Sci, Vol. 24, 2016,pp. 1371-1383.
  • [16] Y. Yuchao, P. Sheridan, W. Lu, "Complementary resistive switching in tantalum oxide-based resistive memory devices." Applied Physics Letters, Vol.100 No.20, 2012, p.203112.
  • [17] C. Yang, "Nanoscale bipolar and complementary resistive switching memory based on amorphous carbon", Electron Devices, IEEE Transactions on Vl.58 No.11, 2011, pp. 3933–3939.
  • [18] J.J. Hopfield, D. W. Tank, “Neural” computation of decisions in optimization problems." Biological cybernetics, Vol.52 No.3, 1985, pp. 141–152.
  • [19] J.J. Hopfield, "Neurons with graded response have collective computational properties like those of two-state neurons." Proceedings of the national academy of sciences, Vol.81, No.10, 1984, pp. 3088-3092.
  • [20] R. Berdan, T. Prodromakis, C. Toumazou, "High precision analogue memristor state tuning." Electronics letters, Vol. 48, No.18, 2012, pp. 1105-1107.
  • [21] W. Yi, F. Perner, M.S. Qureshi, H. Abdalla, M.D. Pickett, J.J. Yang, R.S. Williams, “Feedback write scheme for memristive switching devices”, Applied Physics A, Vol. 102, No. 4, pp. 973-982.
Year 2019, Volume: 7 Issue: 1, 34 - 43, 31.01.2019
https://doi.org/10.17694/bajece.457902

Abstract

References

  • [1] Dan W. Patterson, Artificial neural networks: theory and applications. Prentice Hall PTR, 1998.
  • [2] M. Janardan, I. Saha, "Artificial neural networks in hardware: A survey of two decades of progress", Neurocomputing Vol:74, No:1, 2010, pp.239-255.
  • [3] D. F. Morgado, A. Antunes, A. M. Mota, "Artificial neural networks: a review of commercial hardware", Engineering Applications of Artificial Intelligence, Vol:17, No:8, 2004, pp.945-952.
  • [4] D.B. Strukov, G.S. Snider, , D.R. Stewart, R.S. Williams, “The missing memristor found”, Nature, Vol: 453, 2008, pp. 80-83.
  • [5] L.O. Chua, “Memristor - the missing circuit element”, IEEE Trans Circuit Theory, Vol.18, 1971, pp. 507-519.
  • [6] T. Prodromakis, C. Toumazou “A Review on Memristive Devices and Applications“ Electronics, Circuits, and Systems (ICECS), 17th IEEE International Conference on, 2010, pp. 934 – 937.
  • [7] L. Chua, "Resistance switching memories are memristors." Applied Physics A, Vol.102, No.4, 2011, pp. 765-783.
  • [8] E. Linn, R. Rosezin, C. Kügeler, R. Waser, “Complementary resistive switches for passive nanocrossbar memories” Nature Mater., vol.9, 2010, pp. 403-406.
  • [9] M. A. Zidan, H. H. Fahmy, M. M. Hussain, K.N. Salama, “Memristor-based memory: The sneak paths problem and solutions” Microelectronics Journal, Vol.44, No.2, 2012, pp. 176-183.
  • [10] R. Rosezin, E. Linn, L. Nielen, C. Kügeler, R. Bruchhaus, R. Waser, “Integrated Complementary Resistive Switches for Passive High-Density Nanocrossbar Arrays”, Electron Device Letters, vol. 32, No.2, 2011, pp. 191-193.
  • [11] A. Fabien, E. Zamanidoost, D. B. Strukov. "Pattern classification by memristive crossbar circuits using ex situ and in situ training", Nature communications, Vol. 4, 2013, p.2072.
  • [12] L. Wang, D. Meitao, D. Shukai, "Memristive perceptron for combinational logic classification", Mathematical Problems in Engineering, 2013.[13] B. Li, Y. Wang, Y. Wang, Y. Chen, H. Yang, “Training itself: Mixed-signal training acceleration for memristor-based neural network”, In Design Automation Conference (ASP-DAC), 2014 19th Asia and South Pacific, 2014, pp. 361-366.
  • [14] http://www.technologyreview.com/news/537211/a-better-way-to-build-brain-inspired-chips/
  • [15] E. Karakulak, R. Mutlu, E. Uçar, “Reconstructive sensing circuit for complementary resistive switches based crossbar memories”, Turk J Elec Eng & Comp Sci, Vol. 24, 2016,pp. 1371-1383.
  • [16] Y. Yuchao, P. Sheridan, W. Lu, "Complementary resistive switching in tantalum oxide-based resistive memory devices." Applied Physics Letters, Vol.100 No.20, 2012, p.203112.
  • [17] C. Yang, "Nanoscale bipolar and complementary resistive switching memory based on amorphous carbon", Electron Devices, IEEE Transactions on Vl.58 No.11, 2011, pp. 3933–3939.
  • [18] J.J. Hopfield, D. W. Tank, “Neural” computation of decisions in optimization problems." Biological cybernetics, Vol.52 No.3, 1985, pp. 141–152.
  • [19] J.J. Hopfield, "Neurons with graded response have collective computational properties like those of two-state neurons." Proceedings of the national academy of sciences, Vol.81, No.10, 1984, pp. 3088-3092.
  • [20] R. Berdan, T. Prodromakis, C. Toumazou, "High precision analogue memristor state tuning." Electronics letters, Vol. 48, No.18, 2012, pp. 1105-1107.
  • [21] W. Yi, F. Perner, M.S. Qureshi, H. Abdalla, M.D. Pickett, J.J. Yang, R.S. Williams, “Feedback write scheme for memristive switching devices”, Applied Physics A, Vol. 102, No. 4, pp. 973-982.
There are 20 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Articlessi
Authors

Erdem Uçar

Ertuğrul Karakulak

Reşat Mutlu

Publication Date January 31, 2019
Published in Issue Year 2019 Volume: 7 Issue: 1

Cite

APA Uçar, E., Karakulak, E., & Mutlu, R. (2019). ANN Circuit Application of Complementary Resistive Switches. Balkan Journal of Electrical and Computer Engineering, 7(1), 34-43. https://doi.org/10.17694/bajece.457902

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