Signal Processing Challenges and Examples for {\it in-situ} Transmission Electron Microscopy

TL;DR

This paper discusses the challenges and opportunities in signal processing for high-speed, high-volume data in transmission electron microscopy, emphasizing the integration of advanced data analysis techniques to enhance material characterization.

Contribution

It highlights recent advances in TEM data collection and explores how signal processing can improve analysis, offering a future outlook on automated TEM data processing.

Findings

High-resolution TEM data can be processed in real-time with advanced signal techniques.

Integration of signal processing enhances material analysis capabilities.

Future automated analysis methods are promising for TEM data.

Abstract

Transmission Electron Microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry. Recent advances in data collection technology for TEM have enabled high-volume and high-resolution data collection at a microsecond frame rate. Taking advantage of these advances in data collection rates requires the development and application of data processing tools, including image analysis, feature extraction, and streaming data processing techniques. In this paper, we highlight a few areas in materials science that have benefited from combining signal processing and statistical analysis with data collection capabilities in TEM and present a future outlook on opportunities of integrating signal processing with automated TEM data analysis.