Frontiers of Mechanical Engineering

, Volume 12, Issue 2, pp 215–223 | Cite as

A decomposition approach to the design of a multiferroic memory bit

  • Ruben Acevedo
  • Cheng-Yen Liang
  • Gregory P. Carman
  • Abdon E. Sepulveda
Research Article

Abstract

The objective of this paper is to present a methodology for the design of a memory bit to minimize the energy required to write data at the bit level. By straining a ferromagnetic nickel nano-dot by means of a piezoelectric substrate, its magnetization vector rotates between two stable states defined as a 1 and 0 for digital memory. The memory bit geometry, actuation mechanism and voltage control law were used as design variables. The approach used was to decompose the overall design process into simpler sub-problems whose structure can be exploited for a more efficient solution. This method minimizes the number of fully dynamic coupled finite element analyses required to converge to a near optimal design, thus decreasing the computational time for the design process. An in-plane sample design problem is presented to illustrate the advantages and flexibility of the procedure.

Keywords

multiferroics nano memory piezoelectric optimization 

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Notes

Acknowledgements

This work was supported by both UCLA’s Center for Excellence in Engineering and Diversity (CEED) Research Intensive Series in Engineering for Underrepresented Populations (RISE-UP) scholarship funded by the Semiconductor Research Cooperation (SRC) Education Alliance (Grant No. 2009-UR-2035-G), and by the National Science Foundation (NSF) Nanosystems Engineering Research Center for Translational Applications of Nanoscale Multiferroic Systems (TANMS) Cooperative Agreement Award EEC-1160504.

References

  1. 1.
    Wang K L, Alzate J G, Khalili Amiri P. Low-power non-volatile spintronic memory: STT-RAM and beyond. Journal of Physics D: Applied Physics, 2013, 46(7): 074003CrossRefGoogle Scholar
  2. 2.
    Pertsev N A, Kohlstedt H. Resistive switching via the converse magnetoelectric effect in ferromagnetic multilayers on ferroelectric substrates. Nanotechnology, 2010, 21(47): 475202CrossRefGoogle Scholar
  3. 3.
    Tiercelin N, Dusch Y, Preobrazhensky V, et al. Magnetoelectric memory using orthogonal magnetization states and magnetoelastic switching. Journal of Applied Physics, 2011, 109(7): 07D726CrossRefGoogle Scholar
  4. 4.
    Dusch Y, Tiercelin N, Klimov A, et al. Stress-mediated magnetoelectric memory effect with uni-axial TbCo2/FeCo multilayer on 011-cut PMN-PT ferroelectric relaxor. Journal of Applied Physics, 2013, 113(17): 17C719CrossRefGoogle Scholar
  5. 5.
    Cui J, Hockel J L, Nordeen P K, et al. A method to control magnetism in individual strain-mediated magnetoelectric islands. Applied Physics Letters, 2013, 103(23): 232905CrossRefGoogle Scholar
  6. 6.
    Gibiansky L V, Torquato S. Optimal design of 1-3 composite piezoelectrics. Structural Optimization, 1997, 13(1): 23–28CrossRefMATHGoogle Scholar
  7. 7.
    Ruiz D, Bellido J C, Donoso A. Design of piezoelectric modal filters by simultaneously optimizing the structure layout and the electrode profile. Structural and Multidisciplinary Optimization, 2016, 53(4): 715–730MathSciNetCrossRefGoogle Scholar
  8. 8.
    Donoso A, Bellido J C. Systematic design of distributed piezoelectric modal sensors/actuators for rectangular plates by optimizing the polarization profile. Structural and Multidisciplinary Optimization, 2009, 38(4): 347–356MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Zhang X, Kang Z, Li M. Topology optimization of electrode coverage of piezoelectric thin-walled structures with CGVF control for minimizing sound radiation. Structural and Multidisciplinary Optimization, 2014, 50(5): 799–814MathSciNetCrossRefGoogle Scholar
  10. 10.
    Schmit L A, Farshi B. Some approximation concepts for structural synthesis. AIAA Journal, 1974, 12(5): 692–699CrossRefGoogle Scholar
  11. 11.
    Schmit L A, Miura H. Approximation Concepts for Efficient Structural Analysis. NASA Contractor Report 2552. 1976Google Scholar
  12. 12.
    Barthelemy J F, Haftka R T. Approximation concepts for optimum structural design—A review. Structural Optimization, 1993, 5(3): 129–144CrossRefGoogle Scholar
  13. 13.
    Toropov V V, Filatov A A, Polynkin A A. Multiparameter structural optimization using FEM and multipoint explicit approximations. Structural Optimization, 1993, 6(1): 7–14CrossRefGoogle Scholar
  14. 14.
    Sepulveda A E, Schmit L A. Approximation-based global optimization strategy for structural synthesis. AIAA Journal, 1993, 31(1): 180–188CrossRefMATHGoogle Scholar
  15. 15.
    Park Y S, Lee S H, Park G J. A study of direct vs. approximation methods in structural optimization. Structural Optimization, 1995, 10(1): 64–66CrossRefGoogle Scholar
  16. 16.
    Sepulveda A E, Thomas H. Global optimization using accurate approximations in design synthesis. Structural Optimization, 1996, 12(4): 251–256CrossRefGoogle Scholar
  17. 17.
    Abspoel S J, Etman L F P, Vervoort J, et al. Simulation based optimization of stochastic systems with integer design variables by sequential multipoint linear approximation. Structural and Multidisciplinary Optimization, 2001, 22(2): 125–139CrossRefGoogle Scholar
  18. 18.
    Shu Y C, Lin M P, Wu K C. Micromagnetic modeling of magnetostrictive materials under intrinsic stress. Mechanics of Materials, 2004, 36(10): 975–997CrossRefGoogle Scholar
  19. 19.
    Zhang J X, Chen L Q. Phase-field microelasticity theory and micromagnetic simulations of domain structures in giant magnetostrictive materials. Acta Materialia, 2005, 53(9): 2845–2855CrossRefGoogle Scholar
  20. 20.
    Cullity B D, Graham C D. Introduction to Magnetic Materials. 2nd ed. Hoboken: Wiley-IEEE Press, 2009Google Scholar
  21. 21.
    O’Handley R C. Modern Magnetic Materials: Principles and Applications. New York: Wiley, 1999Google Scholar
  22. 22.
    Banas L U. Adaptive techniques for Landau-Lifshitz-Gilbert equation with magnetostriction. Journal of Computational and Applied Mathematics, 2008, 215(2): 304–310MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Gilbert T L. A phenomenological theory of damping in ferromagnetic materials. IEEE Transactions on Magnetics, 2004, 40(6): 3443–3449CrossRefGoogle Scholar
  24. 24.
    Fredkin D R, Koehler T R. Hybrid method for computing demagnetizing fields. IEEE Transactions on Magnetics, 1990, 26(2): 415–417CrossRefGoogle Scholar
  25. 25.
    Szambolics H, Toussaint J C, Buda-Prejbeanu L D, et al. Innovative weak formulation for the Landau-Lifshitz-Gilbert equations. IEEE Transactions on Magnetics, 2008, 44(11): 3153–3156CrossRefMATHGoogle Scholar
  26. 26.
    Liang C Y, Keller S M, Sepulveda A E, et al. Electrical control of a single magnetoelastic domain structure on a clamped piezoelectric thin film—Analysis. Journal of Applied Physics, 2014, 116(12): 123909CrossRefGoogle Scholar
  27. 27.
    Biswas A K, Bandyopadhyay S, Atulasimha J. Complete magnetization reversal in a magnetostrictive nanomagnet with voltagegenerated stress: A reliable energy-efficient non-volatile magnetoelastic memory. Applied Physics Letters, 2014, 105(7): 072408CrossRefGoogle Scholar
  28. 28.
    Biswas A K, Bandyopadhyay S, Atulasimha J. Energy-efficient magnetoelastic non-volatile memory. Applied Physics Letters, 2014, 104(23): 232403CrossRefGoogle Scholar
  29. 29.
    Stoner E C, Wohlfarth E P. A mechanism of magnetic hysteresis in heterogeneous alloys. Philosophical Transactions of the Royal Society A: Mathematical, 1948, 240(826): 599–642CrossRefMATHGoogle Scholar
  30. 30.
    COMSOL Multiphysics. 2017. Retrieved from http://www.comsol. com/Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Ruben Acevedo
    • 1
  • Cheng-Yen Liang
    • 1
  • Gregory P. Carman
    • 1
  • Abdon E. Sepulveda
    • 1
  1. 1.Mechanical and Aerospace Engineering DepartmentUniversity of California Los AngelesLos AngelesUSA

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