With the increase of research interest on memristors, various single or multiple memristor configurations have been integrated with advanced complementary metal–oxide–semiconducor technology, which promises efficient implementations of synaptic connections in neuromorphic computing systems, or computing elements in signal processing systems. In this study, multiple memristors, both in series and parallel connections, and their characteristics are further studied including the transient behaviours when asynchronous change happens and the composite electric properties in steady state etc. Particularly, the specific conditions to reach steady state and produce composite memristive effects are presented in detail. Furthermore, several synaptic memristor circuits based on series and parallel connections are also discussed.

References

1. 1)
  - 8. Pickett, M.D., Strukov, D.B., Borghetti, J.L., et al: ‘Switching dynamics in titanium dioxide memristive devices’, J. Appl. Phys., 2009, 106, (7), p. 074508 (doi: 10.1063/1.3236506).
2. 2)
  - 27. Wang, H., Duan, S., Huang, T., et al: ‘Exponential stability of complex-valued memristive recurrent neural networks’, IEEE Trans. Neural Netw. Learn. Syst., doi: 10.1109/TNNLS.2015.2513001.
3. 3)
  - 28. Duan, S., Wang, H., Wan, L., et al: ‘Impulsive effects and stability analysis on memristive neural networks with variable delays’, IEEE Trans. Neural Netw. Learn. Syst., doi: 10.1109/TNNLS.2015.2497319.
4. 4)
  - 23. Itoh, M., Chua, L.O.: ‘Memristor cellular automata and memristor discrete-time cellular neural networks’, Int. J. Bifurcation Chaos, 2009, 19, (11), pp. 3605–3656 (doi: 10.1142/S0218127409025031).
5. 5)
  - 18. Budhathoki, R.K., Sah, M.P., Adhikari, S.P., et al: ‘Composite behavior of multiple memristor circuits’, IEEE Trans. Circuits Syst., 2013, 60, (10), pp. 2688–2700 (doi: 10.1109/TCSI.2013.2244320).
6. 6)
  - 20. Hu, X., Feng, G., Li, H., et al: ‘An adjustable memristor model and its application in small-world neural networks’. Int. Joint Conf. on Neural Networks, Beijing, China, July 2014, pp. 7–14.
7. 7)
  - 24. Wang, L., Wang, X., Duan, S., et al: ‘A spintronic memristor bridge synapse circuit and the application in memrisitive cellular automata’, Neurocomputing, 2015, 167, pp. 346–351 (doi: 10.1016/j.neucom.2015.04.061).
8. 8)
  - 3. Alibart, F., Zamanidoost, E., Strukov, D.B.: ‘Pattern classification by memristive crossbar circuits with ex-situ and in-situ training’, Nat. Commun., 2013, 4, (2072) (doi: 10.1038/ncomms3072).
9. 9)
  - 9. Thomas, A.: ‘Memristor-based neural networks’, J. Phys. D, Appl. Phys., 2013, 46, (9), p. 093001 (doi: 10.1088/0022-3727/46/9/093001).
10. 10)
  - 1. Chua, L.: ‘Memristor-the missing circuit element’, IEEE Trans. Circuit Theory, 1971, 18, (5), pp. 507–519 (doi: 10.1109/TCT.1971.1083337).
11. 11)
  - 25. Kim, H., Sah, M.P., Yang, C., et al: ‘Memristor bridge synapses’, Proc. IEEE, 2012, 100, (6), pp. 2061–2070 (doi: 10.1109/JPROC.2011.2166749).
12. 12)
  - 4. Williams, R.S.: ‘How we found the missing memristor’, IEEE Spectr., 2008, 45, (12), pp. 28–35 (doi: 10.1109/MSPEC.2008.4687366).
13. 13)
  - 2. Chua, L., Kang, S.M.: ‘Memristive devices and systems’, Proc. IEEE, 1976, 64, (2), pp. 209–223 (doi: 10.1109/PROC.1976.10092).
14. 14)
  - 7. Alibart, F., Gao, L., Hoskins, B.D., et al: ‘High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm’, Nanotechnology, 2012, 23, (7), p. 075201 (doi: 10.1088/0957-4484/23/7/075201).
15. 15)
  - 9. Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: ‘The missing memristor found’, Nature, 2008, 453, (7191), pp. 80–83 (doi: 10.1038/nature06932).
16. 16)
  - 13. Mouttet, B.: ‘Proposal for memristors in signal processing’. Proc. Third Int. ICST Conf., NanoNet 2008, Boston, May 2009, pp. 11–13.
17. 17)
  - 6. Shin, S., Kim, K., Kang, S.-M.: ‘Resistive computing: memristors enabled signal multiplication’, IEEE Trans. Circuits Syst., 2013, 60, (5), pp. 1241–1249 (doi: 10.1109/TCSI.2013.2244434).
18. 18)
  - 7. Jo, S.H., Chang, T., Ebong, I., Bhadviya, B.B., Mazumder, P., Lu, W.: ‘Nanoscale memristor device as synapse in neuromorphic systems’, Nano Lett., 2010, 10, (4), pp. 1297–1301 (doi: 10.1021/nl904092h).
19. 19)
  - 21. Zhang, Y., Wu, L., Wang, S., et al: ‘Color image enhancement based on HVS and PCNN’, Sci. China Inf. Sci., 2010, 53, (10), pp. 1963–1976 (doi: 10.1007/s11432-010-4075-9).
20. 20)
  - 12. Hu, X., Duan, S., Wang, L., et al: ‘Memristive crossbar array with applications in image processing’, Sci. China Inf. Sci., 2012, 55, (2), pp. 461–472 (doi: 10.1007/s11432-011-4410-9).
21. 21)
  - 26. Kim, H., Sah, M.P., Yang, C., et al: ‘Neural synaptic weighting with a pulse-based memristor circuit’, IEEE Trans. Circuits Syst., 2012, 59, (1), pp. 148–158 (doi: 10.1109/TCSI.2011.2161360).
22. 22)
  - 11. Duan, S., Hu, X., Wang, L., et al: ‘Analog memristive memory with applications in audio signal processing’, Sci. China Inf. Sci., 2014, 57, (4), p. 042406 (doi: 10.1007/s11432-013-4864-z).
23. 23)
  - 12. Adhikari, S.P., Yang, C., Kim, H., Chua, L.O.: ‘Memristor bridge synapse-based neural network and its learning’, IEEE Trans. Neural Netw. Learn. Syst., 2012, 23, (9), pp. 1426–1435 (doi: 10.1109/TNNLS.2012.2204770).
24. 24)
  - 10. Duan, S., Hu, X., Dong, Z., et al: ‘Memristor-based cellular nonlinear/neural network: design, analysis, and applications’, IEEE Trans. Neural Netw. Learn. Syst., 2014, 26, (6), pp. 1202–1213 (doi: 10.1109/TNNLS.2014.2334701).
25. 25)
  - 16. Buscarino, A., Fortuna, L., Frasca, M., et al: ‘A chaotic circuit based on Hewlett-Packard memristor’, Chaos: Interdiscip. J. Nonlinear Sci., 2012, 22, (2), p. 023136 (doi: 10.1063/1.4729135).
26. 26)
  - 14. Wang, X., Chen, Y., Xi, H., et al: ‘Spintronic memristor through spin-torque-induced magnetization motion’, IEEE Electron Device Lett., 2009, 30, (3), pp. 294–297 (doi: 10.1109/LED.2008.2012270).
27. 27)
  - 15. Li, Y., Dou, G.: ‘Towards the implementation of memristor: a study of the electric properties of Ba0.77Sr0.23TiO3 material’, Int. J. Bifurcation Chaos, 2013, 23, (12), p. 1350204 (doi: 10.1142/S0218127413502040).
28. 28)
  - 19. Biolek, Z., Biolek, D., Biolkov'a, V.: ‘SPICE model of memristor with nonlinear dopant drift’, Radio Eng., 2009, 18, pp. 210–214.

Multiple memristor series–parallel connections with use in synaptic circuit design

References

Related content