Improving the temporal resolution of event-based electron detectors using neural network cluster analysis

• Published in: Ultramicroscopy 2024-02, Vol.256, p.113881-113881, Article 113881
• Main Authors: Schröder, Alexander, Rathje, Christopher, van Velzen, Leon, Kelder, Maurits, Schäfer, Sascha
• Format: Article
• Language: English
• Subjects: Cluster analysis; Event-based electron detectors; Femtosecond electron pulses; Neural network; Ultrafast transmission electron microscopy
• ISSN: 0304-3991; 1879-2723
• DOI: 10.1016/j.ultramic.2023.113881

•Neural network cluster analysis enhances temporal accuracy of event-based electron detectors.•Correlations within event clusters triggered by incident electrons are leveraged to improve timing precision.•A neural network is trained by experimental fs-electron pulse data.•The proposed method achieves a 2 ns rms temporal resolution, significantly narrowing the timing distribution. Novel event-based electron detector platforms provide an avenue to extend the temporal resolution of electron microscopy into the ultrafast domain. Here, we characterize the timing accuracy of a detector based on a TimePix3 architecture using femtosecond electron pulse trains as a reference. With a large dataset of event clusters triggered by individual incident electrons, a neural network is trained to predict the electron arrival time. Corrected timings of event clusters show a temporal resolution of 2 ns, a 1.6-fold improvement over cluster-averaged timings. This method is applicable to other fast electron detectors down to sub-nanosecond temporal resolutions, offering a promising solution to enhance the precision of electron timing for various electron microscopy applications.