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Home
Auto-thresholding For Unbiased Electron Counting.

Home
Auto-thresholding For Unbiased Electron Counting.

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Published on: July 6, 2018

Auto-thresholding for unbiased electron counting.

Julie Marie Bekkevold^1,2, Jonathan J P Peters^1,2, Ryo Ishikawa³

¹School of Physics, Trinity College Dublin, College Green, Dublin D02 PN40, Ireland.

Microscopy (Oxford, England)

|May 19, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces an automated method for setting thresholds in electron counting for scanning transmission electron microscopy. This new approach enhances detector performance and removes human bias for faster, more accurate material characterization.

Keywords:

electron counting scanning transmission, electron microscopy single electron events threshold optimization

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Published on: July 3, 2021

Area of Science:

Materials Science
Electron Microscopy
Detector Physics

Background:

Advancements in scanning transmission electron microscopy (STEM) necessitate faster and more precise electron detection.
Electron counting techniques are crucial for structural and functional material characterization.
Existing retrofittable signal digitizers enhance detector performance but rely on manual thresholding, introducing operator bias.

Purpose of the Study:

To develop an automated thresholding method for electron counting in STEM.
To eliminate human bias and improve the efficiency of electron detection.
To enhance the performance of existing monolithic or segmented electron detectors.

Main Methods:

An auto-thresholding algorithm was developed to optimize signal digitizer thresholds.

The method identifies the optimal threshold by maximizing the distinction between electron event signals and noise.

This approach is applied to retrofittable signal digitizers for electron detectors.

Main Results:

The auto-thresholding approach successfully determines optimal thresholds for electron counting.
Implementation leads to easier operation and increased throughput for electron detection.
Human bias in signal digitisation is effectively eliminated.

Conclusions:

Automated thresholding provides a more objective and efficient method for electron counting.
This technique improves the accessibility and performance of fast scanning transmission electron microscopy.
The developed method supports advanced materials characterization at high speeds.