Shape-Based Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron

Journal Article · Wed Sep 18 00:00:00 EDT 2019 · IEEE Transactions on Electron Devices

DOI:https://doi.org/10.1109/TED.2019.2938952· OSTI ID:1581982

Brigner, Wesley H.  ^[1]; Friedman, Joseph S.  ^[1]; Hassan, Naimul ^[1]; Jiang-Wei, Lucian ^[1]; Hu, Xuan  ^[1]; Saha, Diptish  ^[1]; Bennett, Christopher H.  ^[2]; Marinella, Matthew J.  ^[2]; Incorvia, Jean Anne C.  ^[3]; Garcia-Sanchez, Felipe  ^[4]

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1. Univ. of Texas at Dallas, Richardson, TX (United States)
2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
3. Univ. of Texas, Austin, TX (United States)
4. Istituto Nazionale di Ricerca Metrologica, Turin (Italy)

Spintronic devices based on domain wall (DW) motion through ferromagnetic nanowire tracks have received great interest as components of neuromorphic information processing systems. Prior proposals for spintronic artificial neurons required external stimuli to perform the leaking functionality, one of the three fundamental functions of a leaky integrate-and-fire (LIF) neuron. The use of this external magnetic field or electrical current stimulus results in either a decrease in energy efficiency or an increase in fabrication complexity. Here, we modify the shape of previously demonstrated three-terminal magnetic tunnel junction neurons to perform the leaking operation without any external stimuli. The trapezoidal structure causes a shape-based DW drift, thus intrinsically providing the leaking functionality with no hardware cost. This LIF neuron, thus, promises to advance the development of spintronic neural network crossbar arrays.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1581982
• Report Number(s): SAND-2019-8380J; SAND-2019-5379J; 677637
• Journal Information: IEEE Transactions on Electron Devices, Vol. 66, Issue 11; ISSN 0018-9383
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
Artificial neuron
leaky integrate-and-fire (LIF) neuron
magnetic domain wall (DW)
neural network crossbar
neuromorphic computing
three-terminal magnetic tunnel junction (3T-MTJ)