Phase Object Reconstruction for 4D-STEM using Deep Learning

Abstract

In this study we explore the possibility to use deep learning for the reconstruction of phase images from 4D scanning transmission electron microscopy (4D-STEM) data. The process can be divided into two main steps. First, the complex electron wave function is recovered for a convergent beam electron diffraction pattern (CBED) using a convolutional neural network (CNN). Subsequently a corresponding patch of the phase object is recovered using the phase object approximation (POA). Repeating this for each scan position in a 4D-STEM dataset and combining the patches by complex summation yields the full phase object. Each patch is recovered from a kernel of 3x3 adjacent CBEDs only, which eliminates common, large memory requirements and enables live processing during an experiment. The machine learning pipeline, data generation and the reconstruction algorithm are presented. We demonstrate that the CNN can retrieve phase information beyond the aperture angle, enabling super-resolution imaging. The image contrast formation is evaluated showing a dependence on thickness and atomic column type. Columns containing light and heavy elements can be imaged simultaneously and are distinguishable. The combination of super-resolution, good noise robustness and intuitive image contrast characteristics makes the approach unique among live imaging methods in 4D-STEM.

References in corpus (2)

• Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwell time and faster scans for high precision and low dose applications
• Live processing of momentum-resolved STEM data for first moment imaging and ptychography

Cited by in corpus (5)

• Solving Complex Nanostructures With Ptychographic Atomic Electron Tomography
• Removing constraints of 4D-STEM with a framework for event-driven acquisition and processing
• Machine learning of phases and structures for model systems in physics
• Unsupervised Multi-Clustering and Decision-Making Strategies for 4D-STEM Orientation Mapping
• Large-Angle Convergent-Beam Electron Diffraction Patterns via Conditional Generative Adversarial Networks