The convergent-beam low energy electron diffraction technique has been proposed as a novel method to gather local structural and electronic information from crystalline surfaces during low-energy electron microscopy. However, the approach suffers from high complexity of the resulting diffraction patterns. We show that Convolutional Autoencoders trained on CBLEED patterns achieve a highly structured latent space. The latent space is then used to estimate structural parameters with sub-angstrom accuracy. The low complexity of the neural networks enables real time application of the approach during experiments with low latency.
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| http://dx.doi.org/10.1016/j.ultramic.2024.114021 | DOI Listing |
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