Optical Contrast and Raman Spectroscopy Techniques Applied to Few-Layer 2D Hexagonal Boron Nitride

Area of Science:

• Materials Science
• Condensed Matter Physics
• Nanotechnology

Background:

• Accurate thickness determination of few-layer hexagonal boron nitride (hBN) is crucial for integrating it into two-dimensional (2D) material-based devices.
• Existing methods like optical contrast and Raman spectroscopy are established for 2D semiconductors but not widely applied to 2D insulators like hBN.

Purpose of the Study:

• To evaluate optical contrast techniques for non-destructively quantifying the thickness of few-layer hexagonal boron nitride (hBN) on SiO₂/Si substrates.
• To compare the effectiveness of optical contrast with atomic force microscopy and micro-Raman spectroscopy for hBN thickness estimation.

Main Methods:

• Optical contrast measurements on few-layer hBN on SiO₂/Si substrates, with and without bandpass filters (500-650 nm).
• Atomic force microscopy (AFM) for direct thickness measurement and validation.
• Micro-Raman spectroscopy to analyze the thickness dependence of the in-plane E_2g phonon mode.
• Ab initio calculations to support experimental findings on phonon modes.

Main Results:

• Optical contrast of few-layer hBN on SiO₂/Si substrates shows a linear correlation with the number of monolayers.
• Bandpass filters enhance the accuracy of optical contrast for thickness estimation.
• The in-plane E_2g phonon mode in hBN exhibits weak thickness dependence, consistent with ab initio calculations.
• Optical contrast is identified as the most suitable technique for rapid hBN thickness quantification.

Conclusions:

• Optical contrast is a fast, non-destructive, and effective method for determining the thickness of few-layer hexagonal boron nitride (hBN) nanosheets.
• This technique provides a practical solution for quality control and device integration in 2D material research.
• Raman spectroscopy, while informative, is less sensitive to thickness variations in hBN compared to optical contrast.