J. C. Gallagher

U.S. Naval Research Laboratory
  • May 12, 2022 // 3:20pm

    18.6 Chemical Mechanical Polishing of β-Ga2O3

    M.S. Goorsky, University of California, Los Angeles
    M. E. Liao, University of California, Los Angeles, CA USA
    K. Huynh, University of California, Los Angeles
    W. Olsen, University of California, Los Angeles, CA USA
    X. Huang, Argonne National Laboratory
    M. Wojcik, Argonne National Laboratory
    J. C. Gallagher, U.S. Naval Research Laboratory
    Y. Wang, University of California, Los Angeles, CA USA

    Student Presentation

    Abstract

    Download Paper

  • 9.4.2023 Scalable Manufacturing of Planar, Large-Area 1.2kV and 3.3kV Vertical GaN PiN Diodes

    Alan Jacobs, U.S. Naval Research Laboratory
    Mona Ebrish, NRC Postdoc Fellow Residing at the U.S. Naval Research Laboratory
    James Gallagher, U.S. Naval Research Laboratory
    Marko J. Tadjer, U.S. Naval Research Laboratory
    James Spencer Lundh, National Research Council Postdoctoral Fellow, Residing at NRL
    Jennifer K. Hite, Naval Research Laboratory
    N. Mahadik, U.S. Naval Research Laboratory
    Robert Kaplar, Sandia National Labs, Albuquerque, NM
    O. Aktas, Sandia National Labs, Albuquerque, NM

    9.4.2023_Anderson

  • 10.5.2023 Accuracy of Machine Learning Models on Predicting the Properties of Vertical GaN Diodes

    James Gallagher, U.S. Naval Research Laboratory
    Michael A. Mastro, U.S. Naval Research Laboratory
    Mona Ebrish, Vanderbilt University, Nashville, TN
    Alan Jacobs, U.S. Naval Research Laboratory
    Brendan. P. Gunning, Sandia National Labs, Albuquerque, NM
    Robert Kaplar, Sandia National Labs, Albuquerque, NM

    10.5.2023_Gallagher

  • 3B.5 – Stability of 3.3 kV Planar GaN Diodes with Nitrogen Implanted Termination under High Temperature Reverse Bias Stressing

    Alan Jacobs, U.S. Naval Research Laboratory
    James Spencer Lundh, National Research Council Postdoctoral Fellow, Residing at NRL
    Travis J. Anderson, U.S. Naval Research Laboratory
    Geoffrey M. Foster, U.S. Naval Research Laboratory
    Andrew Koehler, U. S. Naval Research Laboratory
    J. C. Gallagher, U.S. Naval Research Laboratory
    Brendan. P. Gunning, Sandia National Labs, Albuquerque, NM
    Robert Kaplar, Sandia National Labs, Albuquerque, NM
    Karl D. Hobart, U.S. Naval Research Laboratory
    M.A. Mastro, U.S. Naval Research Laboratory

    3B.5 Final.2025

    ABSTRACT
    Planar vertical gallium nitride devices are capable of utilizing the beneficial material properties inherent to bulk GaN without the interference of surface leakage pathways or passivation failures inherent to lateral devices, however, the stability and long-term viability of implanted termination necessitates study. Here we show  stressing of 3.3kV vertical GaN diodes with nitrogen implanted termination at over 80% of the breakdown voltage and at up to 200°C for over 400 hours. Some diodes exhibit a burn-in effect with small changes to the breakdown voltage and leakage at breakdown while others exhibit robust and nearly invariant behavior to the limits of testing. Additionally, thermal stressing of a cohort of devices without bias shows an increased degradation of breakdown voltage above 300°C and differentiation of devices within the cohort beyond 350°C enabling further study of the degradation mechanisms.

  • 10B.1 – Mapping Defects in SiC Wafers Using a Multi-Channel Convolutional Neural Network

    James Gallagher, U.S. Naval Research Laboratory
    N. Mahadik, U.S. Naval Research Laboratory
    R. E. Stahlbush, U.S. Naval Research Laboratory
    Karl D. Hobart, U.S. Naval Research Laboratory
    M.A. Mastro, U.S. Naval Research Laboratory

    10B.1 Final.2025

    Abstract
    Though wide bandgap semiconductors offer superior performance to its Si based counterpart, the current state of the art manufacturing technology produces several defects preventing devices from performing optimally. Particularly in SiC, the methods for detecting extended defects such as threading edge dislocations (TED), threading screw dislocations (TSD), basel plane dislocations (BPD), stacking faults, and polytype inclusions are well established; however, automated quantitative analysis is challenging due to the variable size, shape, and intensity of these numerous defects. This study focuses on developing machine learning models using multiple measurements with different techniques including x-ray topography (XRT) and ultraviolet photoluminescence (UVPL) to locate and quantify the microscopic defects on a macroscopic scale.