arXiv subjects

Chu-Ping Yu

Publications and source records attributed to Chu-Ping Yu.

5 recordsLinked to original sources

arXiv2023

Quantum Wavefront Shaping with a 48-element Programmable Phase Plate for Electrons

We present a 48-element programmable phase plate for coherent electron waves produced by a combination of photolithography and focused ion beam. This brings the highly successful concept of wavefront shaping from light optics into the realm of electron optics and provides an important new degree of freedom to prepare electron quantum states. The phase plate chip is mounted on an aperture rod placed in the C2 plane of a transmission electron microscope operating in the 100-300 kV range. The phase plate's behavior is characterized by a Gerchberg-Saxton algorithm, showing a phase sensitivity of 0.075rad/mV at 300kV, with a phase resolution of approximately $3\cdot10^{-3}\pi$. In addition, we provide a brief overview of possible use cases and support it with both simulated and experimental results.

physics.ins-det↗

arXiv2022

Phase Object Reconstruction for 4D-STEM using Deep Learning

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.

cond-mat.mtrl-sci↗

arXiv2021

Real Time Integration Centre of Mass (riCOM) Reconstruction for 4D-STEM

A real-time image reconstruction method for scanning transmission electron microscopy (STEM) is proposed. With an algorithm requiring only the center of mass (COM) of the diffraction pattern at one probe position at a time, it is able to update the resulting image each time a new probe position is visited without storing any intermediate diffraction patterns. The results show clear features at higher spatial frequency, such as atomic column positions. It is also demonstrated that some common post processing methods, such as band pass filtering, can be directly integrated in the real time processing flow. Compared with other reconstruction methods, the proposed method produces high quality reconstructions with good noise robustness at extremely low memory and computational requirements. An efficient, interactive open source implementation of the concept is further presented, which is compatible with frame-based, as well as event-based camera/file types. This method provides the attractive feature of immediate feedback that microscope operators have become used to, e.g. conventional high angle annular dark field STEM imaging, allowing for rapid decision making and fine tuning to obtain the best possible images for beam sensitive samples at the lowest possible dose.

cond-mat.mtrl-sci↗

arXiv2021

Phase retrieval from 4-dimensional electron diffraction datasets

We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.

cond-mat.mtrl-sci↗

arXiv2019

Crossover from 2D metal to 3D Dirac semimetal in metallic PtTe2 films with local Rashba effect

PtTe2 and PtSe2 with trigonal structure have attracted extensive research interests since the discovery of type-II Dirac fermions in the bulk crystals. The evolution of the electronic structure from bulk 3D topological semimetal to 2D atomic thin films is an important scientific question. While a transition from 3D type-II Dirac semimetal in the bulk to 2D semiconductor in monolayer (ML) film has been reported for PtSe2, so far the evolution of electronic structure of atomically thin PtTe2 films still remains unexplored. Here we report a systematic angle-resolved photoemission spectroscopy (ARPES) study of the electronic structure of high quality PtTe2 films grown by molecular beam epitaxy with thickness from 2 ML to 6 ML. ARPES measurements show that PtTe2 films still remain metallic even down to 2 ML thickness, which is in sharp contrast to the semiconducting property of few layer PtSe2 film. Moreover, a transition from 2D metal to 3D type-II Dirac semimetal occurs at film thickness of 4-6 ML. In addition, Spin-ARPES measurements reveal helical spin textures induced by local Rashba effect in the bulk PtTe2 crystal, suggesting that similar hidden spin is also expected in few monolayer PtTe2 films. Our work reveals the transition from 2D metal to 3D topological semimetal and provides new opportunities for investigating metallic 2D films with local Rashba effect.

cond-mat.mtrl-sci↗