Removing constraints of 4D-STEM with a framework for event-driven acquisition and processing

TL;DR

This paper introduces a framework for event-driven 4D-STEM data acquisition and processing, significantly reducing data size and computational demands, enabling real-time analysis and broadening the technique's applicability.

Contribution

It presents a novel framework that optimizes the entire 4D-STEM pipeline for event-driven data, improving efficiency and enabling live processing of electron microscopy data.

Findings

Event-driven data reduces memory and bandwidth requirements.

The framework enables real-time processing including analytical ptychography.

Sparse data representations are more efficient than dense ones in 4D-STEM.

Abstract

Pixelated detectors in scanning transmission electron microscopy (STEM) generate large volumes of data, often tens to hundreds of GB per scan. However, to make current advancements scalable and enable widespread adoption, it is essential to use the most efficient representation of an electron's information. Event-driven direct electron detectors, such as those based on the Timepix3 chip, offer significant potential for electron microscopy, particularly for low-dose experiments and real-time data processing. In this study, we compare sparse and dense data representations in terms of their size and computational requirements across various 4D-STEM scenarios, including high-resolution imaging and nano-beam electron diffraction. The advantages of performing 4D-STEM in an event-driven mode - such as reduced requirements in memory, bandwidth, and computational demands - can only be fully leveraged if the entire acquisition and processing pipeline is optimized to work directly with the event format, avoiding intermediate dense representations. We introduce a framework designed for acquisition and processing based on this event format, and demonstrate live processing of event-driven 4D-STEM, including analytical ptychography.