# Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope

## TL;DR

This paper introduces a correlative SEM method combining EDS and EBSD data with weighted PCA to improve detection and characterization of minor phases in microstructures, enhancing signal clarity and phase identification.

## Contribution

It develops a novel weighted PCA approach that integrates EDS and EBSD signals for better microstructural phase analysis in SEM.

## Key findings

- Enhanced detection of small phases in microstructures.
- Improved phase classification accuracy.
- Effective noise reduction in EDS and EBSD signals.

## Abstract

The routine and unique determination of minor phases in microstructures is critical to materials science. In metallurgy alone, applications include alloy and process development and the understanding of degradation in service. We develop a correlative method, exploring superalloy microstructures which are examined in the scanning electron microscope (SEM) using simultaneous energy dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD). This is performed at an appropriate length scale for characterisation of carbide phases' shape, size, location, and distribution. EDS and EBSD data are generated using two different physical processes, but each provide a signature of the material interacting with the incoming electron beam. Recent advances in post-processing, driven by "big data" approaches, include use of principal component analysis (PCA). Components are subsequently characterised to assign labels to a mapped region. To provide physically meaningful signals, the principal components may be rotated to control the distribution of variance. In this work, we develop this method further through a weighted PCA approach. We use the EDS and EBSD signals concurrently, thereby labelling each region using both EDS (chemistry) and EBSD (crystal structure) information. This provides a new method of amplifying signal-to-noise for very small phases in mapped regions, especially where the EDS or EBSD signal is not unique enough alone for classification.

---
Source: https://tomesphere.com/paper/1908.04084