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Introduction to Alternating Bias Assisted Annealing of Aluminum Oxide Tunnel barriers

ORAL

Abstract

Aluminum oxide Josephson junctions are key components in superconducting qubits due to their high non-linearity in the few-photon regime and ease of fabrication. However, due to the ultra-thin nature of the barrier, inhomogeneities and defects can occur in the as-grown structures, resulting in significant frequency variations and anomalous spectroscopic defects of the qubits. In this work, we present a robust solution to the junction reproducibility problem with a transformational technique alternating-bias assisted annealing (ABAA) for controllably tuning the electrical properties of the junctions. We use a relatively low-voltage, compared to the breakdown voltage, that is periodically poled at low frequency, on the order of 1 Hz. Resistance trimming up to more than 70% can be achieved with high accuracy, with indications of improved coherence and reduction of spectroscopic defects. Transmission electron microscopy of the treated junctions show an amorphous structure of the barrier that appears to have a more homogeneous coordination of the aluminum, indicating that the process tends to mix the barrier at the atomic scale.

Publication: Pappas, D.P., Field, M., Kopas, C.J. et al. Alternating-bias assisted annealing of amorphous oxide tunnel junctions. Nature Commun Mater 5, 150 (2024). https://doi.org/10.1038/s43246-024-00596-z

Presenters

  • David P Pappas

    Rigetti Computing

Authors

  • David P Pappas

    Rigetti Computing

  • Mark Field

    Rigetti Computing

  • Cameron J Kopas

    Rigetti Computing

  • Joel A Howard

    Rigetti Computing

  • Xiqiao Wang

    Rigetti Computing

  • Ella O Lachman

    Rigetti Computing

  • Jinsu Oh Oh

    Ames National Lab, Ames National Laboratory

  • Lin Zhou

    Ames National Laboratory

  • Alysson Gold

    Rigetti Computing

  • Gregory M Stiehl

    Rigetti Computing

  • Kameshwar Yadavalli

    Rigetti Computing

  • Eyob A Sete

    Rigetti Computing

  • Andrew Bestwick

    Rigetti Computing

  • Matthew J Kramer

    Ames National Laboratory

  • Joshua Y Mutus

    Rigetti Computing