Composition quantification of electron-transparent samples by backscattered electron imaging in scanning electron...

Area of Science:

• Materials Science
• Analytical Chemistry
• Surface Science

Background:

• Backscattered electron (BSE) imaging in scanning electron microscopy (SEM) is sensitive to material composition.
• Quantifying material composition using BSE contrast typically requires prior knowledge of the material system.
• Thin, electron-transparent specimens offer improved spatial resolution compared to bulk samples.

Purpose of the Study:

• To develop and validate a method for accurate composition quantification of thin In_xGa_1-xAs layers within a GaAs matrix using SEM.
• To enhance the spatial resolution of BSE imaging for analyzing embedded thin films.
• To establish a calibration-free quantification approach by comparing experimental and calculated BSE intensities.

Main Methods:

• Utilizing scanning electron microscopy (SEM) with an annular semiconductor detector for BSE imaging.
• Preparing thin, electron-transparent specimens to improve spatial resolution.
• Applying an analytical model for calculating BSE intensities, incorporating single electron scattering and electron diffusion.
• Calibrating measured intensities within a single image to account for detection and amplification variations.

Main Results:

• Demonstrated the feasibility of quantifying In-concentration in thin In_xGa_1-xAs layers embedded in GaAs.
• Achieved improved spatial resolution by analyzing thin specimens.
• Successfully calibrated BSE intensities within a single image, eliminating the need for external calibration standards.
• Validated the analytical model against experimental data for accurate composition determination.

Conclusions:

• The developed SEM-based BSE imaging technique, coupled with an analytical model, provides an efficient and accurate method for composition quantification in thin films.
• The approach offers a viable alternative to time-consuming Monte Carlo simulations for material analysis.
• This method is particularly useful for analyzing the composition and local thickness of embedded nanostructures.