Computer Vision Applied to In-Situ Specimen Orientation Adjustment for Quantitative SEM Analysis

TL;DR

This paper introduces a computer vision-based method using SIFT for precise in-situ specimen orientation in SEM, enabling detailed statistical analysis of uncertainties and improving accuracy over traditional stereoscopic techniques.

Contribution

It presents a novel computer vision approach with SIFT for automating SEM specimen orientation and provides the first detailed statistical uncertainty analysis of such methods.

Findings

High-precision orientation of flat surfaces in SEM

Quantitative analysis of errors in standard SEM operations

Automation of measurements using computer vision

Abstract

Quantitative analysis methods based on the usage of a scanning electron microscope (SEM), such as energy dispersive x-ray spectroscopy, often require specimens to have a flat surface oriented normal to the electron beam. In-situ procedures for putting microscopic flat surfaces into this orientation generally rely on stereoscopic methods that measure the change in surface vector projections when the surface is tilted by some known angle. Although these methods have been used in the past, there is no detailed statistical analysis of the uncertainties involved in such methods, which leaves an uncertainty in how precisely a specimen can be oriented. Here, we present a first principles derivation of a specimen orientation method and apply our method to a flat sample to demonstrate it. Unlike previous works, we develop a computer vision program using the Scale Invariant Feature Transform to automate and expedite the process of making measurements on our SEM images, thus enabling a detailed statistical analysis of the method with a large sample size. We find that our specimen orientation method is able to orient flat surfaces with high precision and can further provide insight into errors involved in the standard SEM rotation and tilt operations.