Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning

Abstract

Hybrid Pixel Detectors (HPDs) are highly suitable in diffraction-based electron microscopy due to their high frame rates (> 1 kHz), high dynamic range, and good radiation hardness. However, their use in imaging applications has been limited by their relatively large pixel size (≥ 55 μm) and high-energy (>80 keV) electrons scattering over multiple pixels in the sensor layer. To realize the full potential of fast, radiation-hard HPDs across electron microscopy modalities, we developed deep learning techniques to precisely localize the impact point of incident electrons in MÖNCH, a charge integrating HPD with 25 μm pixel size. With neural network models trained using labeled data via simulations and experimental measurements, the best spatial resolution obtained, defined in terms of the root mean squared error, was 0.60 pixels for 200 keV electrons, a three-fold improvement over a simple charge centroid method. This article presents the training sample generation, deep learning model design, training results, and imaging outcomes for a sample containing gold nanoparticles.