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Su, Cong

Publications and source records attributed to Su, Cong.

Three-dimensional structure of buried heterointerfaces revealed by multislice ptychography

Here, we report on the three-dimensional (3D) structure determination of a twisted hexagonal boron nitride (h-BN) heterointerface from a single-view dataset using multislice ptychography. We identify the buried heterointerface between two twisted h-BN flakes with a lateral resolution of 0.57 Å and a depth resolution of 2.5 nm. The latter represents a significant improvement (∼2.7 times) over the aperture-limited depth resolution of incoherent imaging modes, such as annular-dark-field scanning transmission electron microscopy. This improvement is attributed to the diffraction signal extending beyond the aperture edge, with the depth resolution set by the curvature of the Ewald sphere. Future advancements in this approach could enhance the depth resolution to the subnanometer level and enable the identification of individual dopants, defects, and color centers in twisted heterointerfaces and other materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Fundamentals and emerging optical applications of hexagonal boron nitride: a tutorial

Hexagonal boron nitride (hBN), also known as white graphite, is a transparent layered crystal with a wide bandgap. Its crystal structure resembles graphite, featuring layers composed of honeycomb lattices held together through van der Waals forces. The layered crystal structure of hBN facilitates exfoliation into thinner flakes and makes it highly anisotropic in in-plane and out-of-plane directions. Unlike graphite, hBN is both insulating and transparent, making it an ideal material for isolating devices from the environment and acting as a waveguide. As a result, hBN has found extensive applications in optical devices, electronic devices, and quantum photonic devices. This comprehensive tutorial aims to provide readers with a thorough understanding of hBN, covering its synthesis, lattice and spectroscopic characterization, and various applications in optoelectronic and quantum photonic devices. This tutorial is designed for both readers without prior experience in hBN and those with expertise in specific fields seeking to understand its relevance and connections to others.

36 MATERIALS SCIENCE↗

Imaging the electron charge density in monolayer MoS 2 at the Ångstrom scale

Four-dimensional scanning transmission electron microscopy (4D-STEM) has recently gained widespread attention for its ability to image atomic electric fields with sub-Ångstrom spatial resolution. These electric field maps represent the integrated effect of the nucleus, core electrons and valence electrons, and separating their contributions is non-trivial. In this paper, we utilized simultaneously acquired 4D-STEM center of mass (CoM) images and annular dark field (ADF) images to determine the projected electron charge density in monolayer MoS 2 . We evaluate the contributions of both the core electrons and the valence electrons to the derived electron charge density; however, due to blurring by the probe shape, the valence electron contribution forms a nearly featureless background while most of the spatial modulation comes from the core electrons. Our findings highlight the importance of probe shape in interpreting charge densities derived from 4D-STEM and the need for smaller electron probes.

36 MATERIALS SCIENCE↗

Deep-Learning Electron Diffractive Imaging

Here, we report the development of deep-learning coherent electron diffractive imaging at subangstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying the trained CNNs to recover the phase images from electron diffraction patterns of twisted hexagonal boron nitride, monolayer graphene, and a gold nanoparticle with comparable quality to those reconstructed by a conventional ptychographic algorithm. Fourier ring correlation between the CNN and ptychographic images indicates the achievement of a resolution in the range of 0.70 and 0.55 Å. We further develop CNNs to recover the probe function from the experimental data. The ability to replace iterative algorithms with CNNs and perform real-time atomic imaging from coherent diffraction patterns is expected to find applications in the physical and biological sciences.

47 OTHER INSTRUMENTATION↗