Independent Gate Control of Injected and Detected Spin Currents in Graphene

  • Authors:
    Steven Koester (Univ. of Minnesota), Yoska Anugrah (Univ. of Minnesota), Jiaxi Hu (Univ. of Minnesota), Gordon Stecklein (Univ. of Minnesota), Paul Crowell (Univ. of Minnesota)
    Publication ID:
    P090348
    Publication Type:
    Paper
    Received Date:
    16-Feb-2017
    Last Edit Date:
    14-Jul-2017
    Research:
    2381.004 (University of Minnesota)

Abstract

Graphene is an ideal material for spintronic devices due to its low spin-orbit coupling and high mobility. One of the most important potential applications of graphene spintronics is for use in neuromorphic computing systems, where the tunable spin resistance of graphene can be used to apply analog weighting factors. However, a key capability needed to achieve spin-based neuromorphic computing systems is to achieve distinct regions of control, where injected and detected spin currents can be tuned independently of one another. Here, we demonstrate the ability to achieve such independent control using a graphene spin valve geometry where the injector and detector regions are modulated by two separate bottom gate electrodes. The spin transport parameters and their dependence on each gate voltage are extracted from Hanle precession measurements. From this analysis, local spin transport parameters and their dependence on the local gate voltage are found, which provide a basis for a spatially-resolved spin resistance network that simulates the device. Together, the data and model are used to calculate the spin currents flowing into, through, and out of the graphene channel. We show that the spin current flowing through the graphene channel can be modulated by 30% using one gate voltage and that the spin current absorbed by the detector contact can be modulated by 50% using the other gate voltage. This result demonstrates that spin currents can be controlled by locally tuning the spin resistance of graphene. These structures could find applications in graphene all-spin logic devices or spin neural networks.

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