Spin Orbit Torque Domain Wall-Magnetic Tunnel Junction Devices and Circuits for In-Memory and Neuromorphic Computing

ORAL

Abstract

Domain wall-magnetic tunnel junction (DW-MTJ) in-memory computing devices can address major data processing bottlenecks with traditional computing, especially for accomplishing data-intensive and real-time tasks. We propose three-terminal DW-MTJ in-memory computing devices that resolve challenges with traditional DW-MTJs by using perpendicular magnetic anisotropy (PMA) with more robustness to thermal fluctuations than in-plane magnetic anisotropy, spin-orbit torque switching that requires less switching current than spin transfer torque, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 164%, close to the expected highest TMR seen in PMA MTJs, and resistance-area product RA = 31 Ω.μm2 , close to the RA of the unpatterned film. A two-device circuit shows bit propagation between devices. Switching voltage cycle-to-cycle variation is measured as a tunable probabilistic function and is shown to be curtailed to 7% by controlling the DW initial position, which we show corresponds to 96% accuracy in a DW-MTJ full adder simulation. These results make major strides in using DW-MTJs for in-memory and neuromorphic computing applications.

*The authors acknowledge funding from Sandia National Laboratories Laboratory Directed Research and Development.

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Presenters

  • Mahshid Alamdar

    • Electrical and Computer Engineering Dept., University of Texas at Austin, Austin TX USA

Authors

  • Mahshid Alamdar

    • Electrical and Computer Engineering Dept., University of Texas at Austin, Austin TX USA

  • Thomas Leonard

    • Electrical and Computer Engineering Dept., University of Texas at Austin, Austin TX USA

  • Can Cui

    • Electrical and Computer Engineering, University of Texas at Austin
    • ECE, The University of Texas at Austin
    • Electrical and Computer Engineering Dept., University of Texas at Austin, Austin TX USA

  • Bishweshwor P. Rimal

    • Electrical and Computer Engineering Dept., University of Texas at Austin, Austin TX USA

  • Lin Xue

    • Applied Materials, Santa Clara CA USA

  • Otitoaleke G. Akinola

    • Electrical and Computer Engineering, University of Texas at Austin
    • Electrical and Computer Engineering Dept., University of Texas at Austin, Austin TX USA

  • Tianyao Patrick Xiao

    • Sandia National Laboratories, Albuquerque NM USA

  • Joseph S. Friedman

    • Electrical and Computer Engineering, University of Texas at Dallas
    • University of Texas at Dallas
    • Electrical and Computer Engineering Dept., University of Texas at Dallas, Richardson TX USA
    • Electrical & Computer Engineering, University of Texas at Dallas
    • Department of Electrical and Computer Engineering, University of Texas at Dallas

  • Christopher H. Bennet

    • Sandia National Laboratories
    • Sandia National Laboratories, Albuquerque NM USA

  • Matthew J. Marinella

    • Sandia National Laboratories
    • Sandia National Laboratories, Albuquerque NM USA

  • Jean Anne C. Incorvia

    • Electrical and Computer Engineering, University of Texas at Austin
    • University of Texas at Austin
    • ECE, The University of Texas at Austin
    • Electrical and Computer Engineering Dept., University of Texas at Austin, Austin TX USA