Channeling-in channeling-out revisited: selected area electron channeling and electron backscatter diffraction

TL;DR

This study investigates how electron channeling-in influences electron backscatter diffraction signals in SEM, revealing significant effects that impact data interpretation and proposing a framework to control these effects.

Contribution

It introduces a combined SA-ECP and EBSD approach to visualize and understand channeling-in/channeling-out effects, highlighting their relevance in routine SEM analyses.

Findings

Channeling-in significantly biases EBSD quality metrics.

Wide-angle channeling features are visible in routine EBSD maps.

The combined approach offers a way to control channeling effects in SEM.

Abstract

Scanning electron microscopy combined with electron backscatter diffraction (EBSD) and electron channeling provides rich crystallographic contrast, but the mutual influence of channeling-in and channeling-out is often simplified or neglected in quantitative analyses. In this work, we use selected-area electron channeling patterns (SA-ECPs) acquired from a single-crystal silicon wafer while recording an EBSD pattern at every incident beam direction, thereby directly probing how channeling-in affects the EBSD signal. We show that common Hough-based quality metrics (pattern quality, band contrast, and band slope), pattern-matching cross-correlation coefficients, and Fourier-based signal-to-noise ratios all exhibit strong crystallographic modulations that follow the underlying ECP, in both raw and background-corrected patterns. Similar wide-angle channeling features are also visible in conventional, low-magnification EBSD maps, indicating that channeling-in effects are relevant under routine mapping conditions and not only in specialized ECP experiments. These observations highlight that channeling-in can significantly bias quality-based interpretation of EBSD data, with consequences for methods such as pattern blurring analysis, high-resolution strain mapping, and emerging statistical or machine-learning approaches that rely on subtle variations in diffraction patterns. The combined SA-ECP and EBSD strategy presented here offers a practical framework to visualize and potentially control channeling-in/channeling-out coupling in the SEM, suggesting new routes to design experiments and detector configurations that either mitigate or intentionally exploit these dynamical effects.