N. Mahadik

U.S. Naval Research Laboratory

  • 16.3 Evaluation of GaN Device Structures on 150 mm GaN on Engineered Substrates

    Karl D. Hobart, U.S. Naval Research Laboratory
    Andrew Koehler, U. S. Naval Research Laboratory
    Anindya Nath, George Mason University
    Jennifer Hite, U.S. Naval Research Laboratory
    N. Mahadik, U.S. Naval Research Laboratory
    Fritz Kub, Naval Research Laboratory
    Ozgur Aktas, QROMIS, USA
    Vladimir Odnoblyudov, QROMIS, USA
    Cem Basceri, QROMIS, USA
    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

  • 11.3.2023 Structural and Electrical Characterization of Schottky Barrier Diodes on 100 mm HVPE β-Ga2O3 Epiwafer Technology

    Marko J. Tadjer, U.S. Naval Research Laboratory
    James Gallagher, ASEE Postdoctoral Fellow Residing at NRL
    N. Mahadik, U.S. Naval Research Laboratory
    Hannah N. Masten, National Research Council Postdoctoral Fellow, Residing at NRL
    James Spencer Lundh, National Research Council Postdoctoral Fellow, Residing at NRL
    Akito Kuramata, Novel Crystal Technology, Inc

    11.3.2023_CS_MANTECH-2023_extended-abstract_Tadjer final

  • 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.