Improving Polytype Identification of Silicon Carbide Using Dictionary Indexing

Abstract

Two of silicon carbides most prominent polytypes (6H and 4H) are difficult to differentiate between experimentally. The typical techniques for identifying the polytypes are X-ray diffraction and Raman spectroscopy. While both are useful for this task, they lack the spatial resolution to analyze sub-micron features in detail. Utilizing electron backscatter diffraction improves the resolution but the polytype identification can be difficult. For this work two indexing methods are compared, the traditional Hough-style indexing and a new dictionary indexing. Increasing the number of bands used in the Hough method improves the confidence index but the dictionary method shows the best results.

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Document Details

Document Type
Technical Report
Publication Date
Oct 19, 2023
Accession Number
AD1214125

Entities

People

  • Jonathan Ligda

Organizations

  • United States Army Research Laboratory

Tags

Readers

  • Computational Linguistics
  • Computer Vision.
  • Semiconductor Device Technology

Technology Areas

  • Microelectronics
  • Microelectronics - Graphene
  • Microelectronics - Microelectromechanical Systems