Alexander Clausen

In four-dimensional scanning transmission electron microscopy (4D STEM) a focused beam is scanned over a specimen and a diffraction pattern is recorded at each position using a pixelated detector. During the experiment, it must be ensured that the scan coordinate system of the beam is correctly calibrated relative to the detector coordinate system. Various simplified and approximate models are used implicitly and explicitly for understanding and analyzing the recorded data, requiring translation between the physical reality of the instrument and the abstractions used in data interpretation. Here, we introduce a calibration method where interactive live data processing in combination with a digital twin is used to match a set of models and their parameters with the action of a real-world instrument.

We demonstrate live-updating ptychographic reconstruction with the extended ptychographical iterative engine, an iterative ptychography method, during ongoing data acquisition. The reconstruction starts with a small subset of the total data, and as the acquisition proceeds the data used for reconstruction are extended. This creates a live-updating view of object and illumination that allows monitoring the ongoing experiment and adjusting parameters with quick turn around. This is particularly advantageous for long-running acquisitions. We show that such a gradual reconstruction yields interpretable results already with a small subset of the data. We show simulated live processing with various scan patterns, parallelized reconstruction, and real-world live processing at the hard X-ray ptychographic nanoanalytical microscope PtyNAMi at the PETRA III beamline.

We demonstrate live-updating ptychographic reconstruction with ePIE, an iterative ptychography method, during ongoing data acquisition. The reconstruction starts with a small subset of the total data, and as the acquisition proceeds the data used for reconstruction is extended. This creates a live-updating view of object and illumination that allows monitoring the ongoing experiment and adjusting parameters with quick turn-around. This is particularly advantageous for long-running acquisitions. We show that such a gradual reconstruction yields interpretable results already with a small subset of the data. We show simulated live processing with various scan patterns, parallelized reconstruction, and real-world live processing at the hard X-ray ptychographic nanoanalytical microscope PtyNAMi at the PETRA III beamline.

Position averaged convergent beam electron diffraction (PACBED) is one of the most convenient and precise thickness determination techniques available in a scanning transmission electron microscope. The thickness is determined by finding the best match of the recorded PACBED pattern with a series of simulated diffraction patterns by visual inspection. The automatization of this process can be enhanced by convolutional neural networks (CNNs), making the method fast and easy to apply. However, the simulation of a synthetic dataset and the training of the CNNs carry a high computational cost. With the aim to simplify this process, we propose to build a server-based database of pretrained CNN models that is accessed by the user via a web service directly from the data acquisition and analysis software. We demonstrate a working prototype comprised of a shared CNN database containing three material systems. By this, the microscope operator can determine the specimen thickness by PACBED within a few seconds in a reproducible way during a microscope session, without any prior knowledge about machine learning or multislice modeling. Furthermore, the service is integrated into other software and workflows through the API.

We propose algorithms based on an optimisation method for inverse multislice ptychography in, e.g. electron microscopy. The multislice method is widely used to model the interaction between relativistic electrons and thick specimens. Since only the intensity of diffraction patterns can be recorded, the challenge in applying inverse multislice ptychography is to uniquely reconstruct the electrostatic potential in each slice up to some ambiguities. In this conceptual study, we show that a unique separation of atomic layers for simulated data is possible when considering a low acceleration voltage. We also introduce an adaptation for estimating the illuminating probe. For the sake of practical application, we finally present slice reconstructions using experimental 4D scanning transmission electron microscopy (STEM) data.

We report on the automatic alignment of a transmission electron microscope equipped with an orbital angular momentum sorter using a convolutional neural network. The neural network is able to control all relevant parameters of both the electron-optical setup of the microscope and the external voltage source of the sorter without input from the user. It can compensate for mechanical and optical misalignments of the sorter, in order to optimize its spectral resolution. The alignment is completed over a few frames and can be kept stable by making use of the fast fitting time of the neural network.