JOURNAL ARTICLE

Scanning electron microscopy imaging of multilayer-doped GaN: Effects of surface band bending, surface roughness, and contamination layers on doping contrast.

  • Published In: Journal of Vacuum Science & Technology: Part A-Vacuums, Surfaces & Films, 2024, v. 42, n. 6. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Wang, Siyuan; Zhang, Kai; Zhai, Le; Huang, Li 3 of 3

Abstract

This article investigates the effects of plasma and wet chemical surface treatments on doping contrast in multilayered p-n gallium nitride (GaN) semiconductors using scanning electron microscopy (SEM) and angle-resolved x-ray photoelectron spectroscopy (ARXPS). It finds that nitrogen (N₂) and air plasma treatments reduce surface band bending but fail to remove surface contamination layers such as gallium oxides and hydrocarbons, resulting in weakened doping contrast. Conversely, wet chemical treatments generally remove these contamination layers more effectively but increase surface roughness, which can also diminish doping contrast; notably, ammonium fluoride (NH₄F) treatment improves doping contrast despite a slight increase in roughness. These results highlight the complex interplay between surface band bending, contamination, and roughness in influencing SEM doping contrast and provide guidance for optimizing semiconductor surface preparation for accurate dopant profiling.

Additional Information

  • Source:Journal of Vacuum Science & Technology: Part A-Vacuums, Surfaces & Films. 2024/12, Vol. 42, Issue 6, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:07342101
  • DOI:10.1116/6.0003824
  • Accession Number:181207973
  • Copyright Statement:Copyright of Journal of Vacuum Science & Technology: Part A-Vacuums, Surfaces & Films is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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