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Electron counting detectors in scanning transmission electron microscopy via hardware signal processing
Jonathan J P Peters1,2, Tiarnan Mullarkey3,4, Emma Hedley5
1Advanced Microscopy Laboratory (AML), Trinity College Dublin, the University of Dublin, Dublin, Ireland. jonathan.peters@tcd.ie.
This study introduces hardware signal processing to enable electron detectors in scanning transmission electron microscopy (STEM) to count single electrons. This overcomes limitations in quantification and simulation comparison for materials and biological sciences.
Area of Science:
- Materials Science
- Biological Sciences
- Microscopy
Background:
- Scanning transmission electron microscopy (STEM) is crucial for materials and biological sciences, offering local structural and spectroscopic insights.
- Current electron detectors in STEM often lack quantification capabilities, providing arbitrary signal values without extensive calibration.
- This limitation hinders accurate data analysis and comparison with simulations.
Purpose of the Study:
- To develop and optimize a hardware signal processing method for electron detectors.
- To enable single electron counting in STEM for enhanced quantification.
- To improve the reliability and comparability of STEM data.
Main Methods:
- Implemented a hardware signal processing approach to augment existing electron detectors.
- Optimized the signal processing for accurate single electron detection.
- Tested the augmented detectors for their ability to count individual electrons.
Main Results:
- Successfully demonstrated a hardware-based solution for single electron counting in STEM detectors.
- The optimized approach provides quantified electron counts, overcoming previous limitations.
- The new method offers faster response times compared to traditional calibration methods.
Conclusions:
- Hardware-based single electron counting significantly enhances the quantitative capabilities of STEM detectors.
- This advancement allows for more accurate data analysis and direct comparison with theoretical simulations.
- The developed method is pivotal for advancing research in materials and biological sciences using STEM.
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