Memristor Crossbar Array for Image Storing

  • Ling Chen
  • Chuandong Li
  • Tingwen Huang
  • Shiping Wen
  • Yiran Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)


This letter uses image overlay technique on memristor crossbar array (MCA) structure for image storing. Different programming circuits with time slot techniques are designed for the MCA consisting of the nonlinear HP memristor (HPMCA) and the MCA composed of the piece-wise linear threshold memristor (TMCA). The experiment results indicate that the HPMCA has a better performance, the TMCA is more practical in the industrial implementation. As a conclusion, the MCA made up of the memristor with both the nonlinear drift boundary property and the threshold property is preferred for image overlay.


Memristor CMOS Unit Time Slot Image Overlay 


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  1. 1.
    Chua, L.O.: Memristor: the missing circuit element. IEEE Transactionson Circuit Theory 18, 507–519 (1971)CrossRefGoogle Scholar
  2. 2.
    Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memriastor found. Nature 452, 80–83 (2008)CrossRefGoogle Scholar
  3. 3.
    Wen, S.P., Zeng, Z.G., Huang, T.W., Zhang, Y.D.: Exponential lag adaptive synchronization of memristive neural networks and applications in Pseudo-random generators. IEEE Transactions on Fuzzy Systems 22(6), 1704–1713 (2014)CrossRefGoogle Scholar
  4. 4.
    Sun, J.W., Shen, Y.: Quasi-ideal memory system (2014)Google Scholar
  5. 5.
    Bao, H.B., Cao, J.D.: Projective synchronization of fractional-order memristor-based neural networks. Neural Networks 63, 1–9 (2015)CrossRefGoogle Scholar
  6. 6.
    Dong, Z., Duan, S., Hu, X., Wang, L., Li, H.: A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control. The Scientific World Journal (2014)Google Scholar
  7. 7.
    Prodromakis, T., Toumazou, C.: A review on memristive devices and applications. In: 2010 17th IEEE International Conference on Electronics, Circuits, and Systems, Athens, pp. 934–937 (2010)Google Scholar
  8. 8.
    Wen, S.P., Zeng, Z.G., Huang, T.W., Meng, Q.G.: Lag synchronization of switched neural networks via neural activation function and applications in image encryption. IEEE Transactions on Neural Networks and Learning Systems (2014). doi:10.1109/TNNLS.2014.2387355Google Scholar
  9. 9.
    Chen, L., Li, C.D., Huang, T.W., Chen, Y.R., Wang, X.: Memristor crossbar-based unsupervised image learning. Neural Computing and Applications 25(2), 393–400 (2014)CrossRefGoogle Scholar
  10. 10.
    Wen, S.P., Huang, T.W., Zeng, Z.G., Chen, Y.R., Li, P.: Circuit design and exponential stabilization of memristive neural networks. Neural Networks 63, 48–56 (2015)CrossRefGoogle Scholar
  11. 11.
    Pershin, Y.V., Ventra, M.D.: Experimental demonstration of associative memory with memristive neural networks. Neural Networks 23, 881–886 (2010)CrossRefGoogle Scholar
  12. 12.
    Yang, J.J., Strukov, D.B., Stewart, D.R.: Memristive devices for computing. Nature Nanotechnology (2012). doi:10.1038/NNANO.2012.240Google Scholar
  13. 13.
    Kvatinsky, S., Friedman, E.G., Kolodny, A., Weiser, U.C.: TEAM: ThrEshold Adaptive Memristor Model. IEEE Transactions on Circuits and Systems-I 60, 211–221 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Biolek, Z., Biolek, D., Biolkova, V.: SPICE model of memristor with nonlinear dopant drift. Radio Engeering 18, 210–214 (2009)Google Scholar
  15. 15.
    Chen, L., Li, C., Huang, T., Chen, Y., Wen, S., Qi, J.: A synapse memristor model with forgetting effect. Physics Letters A 377(45), 3260–3265 (2013)CrossRefGoogle Scholar
  16. 16.
    Kim, K.H., Gaba, S., Wheeler, D., Cruz-Albrecht, J.M., Hussain, T., Srinivasa, N., Lu, W.: A Functional Hybrid Memristor Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications. Nano Lett. 12, 389–395 (2011)CrossRefGoogle Scholar
  17. 17.
    Bayat, F.M., Shouraki, S.B.: Programming of memristor crossbars by using genetic algorithm. Procedia Computer Science 3, 232–237 (2011)CrossRefGoogle Scholar
  18. 18.
    Hu, X., Duan, S., Wang, L., Liao, X.: Memristive crossbar array with applications in image processing. Science China Information Sciences 55(2), 461–472 (2012)CrossRefGoogle Scholar
  19. 19.
    Li, H.Q., Liao, X.F., Li, C.D., Huang, H.Y., Li, C.J.: Edge detection of noisy images based on cellular neural networks. Commun. Nonlinear Sci. Numer. Simul. 16(9), 3746–3759 (2011)MathSciNetCrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Ling Chen
    • 1
  • Chuandong Li
    • 1
  • Tingwen Huang
    • 2
  • Shiping Wen
    • 3
  • Yiran Chen
    • 4
  1. 1.The College of Electronic and Information EngineeringSouthwest UniversityChongqingChina
  2. 2.Texas A & M University at QatarDohaQatar
  3. 3.School of AutomationHuazhong University of Science and TechnologyWuhanChina
  4. 4.Electrical and Computer EngineeringUniversity of PittsburghPittsburghUSA

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