Engineering topics

Liu, Mengkun

Publications and source records attributed to Liu, Mengkun.

27 records · Page 2

DOE OSTI2022

Scattering-type scanning near-field optical microscopy with Akiyama piezo-probes

Recent developments of the scattering-type scanning near-field optical microscope at cryogenic temperatures (cryogenic s-SNOM or cryo-SNOM) have led to many breakthroughs in the studies of low energy excitations in quantum materials. However, the simultaneous demands on vibration isolation, low base temperature, precise nano-positioning, and optical access make the construction of a cryo-SNOM a daunting task. Adding to the overhead space required for a cryo-SNOM is the atomic force microscopy control, which predominantly utilizes a laser-based detection scheme for determining the cantilever tapping motion. In this work, we provide an alternative and straightforward route to performing s-SNOM using metal-coated Akiyama probes, where the cantilever tapping motion is detected through a piezoelectric signal. We show that the Akiyama-based cryo-SNOM attains high spatial resolution, good near-field contrast, and is able to perform imaging with a significantly more compact system compared to other cryo-SNOM implementations. Our results firmly establish the potential of s-SNOM based on self-sensing piezo-probes, which can easily accommodate far-infrared wavelengths and high magnetic fields in the future.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Rapid simulations of hyperspectral near-field images of three-dimensional heterogeneous surfaces

The scattering-type scanning near-field optical microscope (s-SNOM) has emerged as a powerful tool for resolving nanoscale inhomogeneities in laterally heterogeneous samples. However, most analytical models used to predict the scattering near-field signals are assuming homogenous landscapes (bulk materials), resulting in inconsistencies when applied to samples with more complex configurations. In this work, we combine the point-dipole model (PDM) to the finite-element method (FEM) to account for the lateral and vertical heterogeneities while keeping the computation time manageable. Full images, spectra, or hyperspectral line profiles can be simulated by calculating the self-consistent dipole radiation demodulated at higher harmonics of the tip oscillation, mimicking real experimental procedures. Using this formalism, we clarify several important yet puzzling experimental observations in near-field images on samples with rich typography and complex material compositions, heterostructures of two-dimensional material flakes, and plasmonic antennas. The developed method serves as a basis for future investigations of nano-systems with nontrivial topography.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Deep Learning Analysis of Polaritonic Wave Images

Deep learning (DL) is an emerging analysis tool across the sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nanoscale deeply subdiffractional images of propagating polaritonic waves in complex materials. Utilizing the convolutional neural network (CNN), we developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves. Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and material parameters in a time scale that is at least 3 orders of magnitude faster than common fitting/processing procedures. The CNN-based analysis was validated by examining the experimental near-field images of charge-transfer plasmon polaritons at graphene/α-RuCl3 interfaces. Our work provides a general framework for extracting quantitative information from images generated with a variety of scanning probe methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Laser-Induced Cooperative Transition in Molecular Electronic Crystal

The competing and non-equilibrium phase transitions, involving dynamic tunability of cooperative electronic and magnetic states in strongly correlated materials, show great promise in quantum sensing and information technology. To date, the stabilization of transient states is still in the preliminary stage, particularly with respect to molecular electronic solids. Here, a dynamic and cooperative phase in potassium-7,7,8,8-tetracyanoquinodimethane (K-TCNQ) with the control of pulsed electromagnetic excitation is demonstrated. Simultaneous dynamic and coherent lattice perturbation with 8 ns pulsed laser (532 nm, 15 MW cm -2 , 10 Hz) in such a molecular electronic crystal initiates a stable long-lived (over 400 days) conducting paramagnetic state (≈42 Ωcm), showing the charge–spin bistability over a broad temperature range from 2 to 360 K. Comprehensive noise spectroscopy, in situ high-pressure measurements, electron spin resonance (ESR), theoretical model, and scanning tunneling microscopy/spectroscopy (STM/STS) studies provide further evidence that such a transition is cooperative, requiring a dedicated charge–spin–lattice decoupling to activate and subsequently stabilize nonequilibrium phase. Finally, the cooperativity triggered by ultrahigh-strain-rate (above 10 6 s -1 ) pulsed excitation offers a collective control toward the generation and stabilization of strongly correlated electronic and magnetic orders in molecular electronic solids and offers unique electro-magnetic phases with technological promises.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Probing subwavelength in-plane anisotropy with antenna-assisted infrared nano-spectroscopy

Infrared nano-spectroscopy based on scattering-type scanning near-field optical microscopy (s-SNOM) is commonly employed to probe the vibrational fingerprints of materials at the nanometer length scale. However, due to the elongated and axisymmetric tip shank, s-SNOM is less sensitive to the in-plane sample anisotropy in general. In this article, we report an easy-to-implement method to probe the in-plane dielectric responses of materials with the assistance of a metallic disk micro-antenna. As a proof-of-concept demonstration, we investigate here the in-plane phonon responses of two prototypical samples, i.e. in (100) sapphire and x-cut lithium niobate (LiNbO 3 ). In particular, the sapphire in-plane vibrations between 350 cm–1 to 800 cm–1 that correspond to LO phonon modes along the crystal b- and c-axis are determined with a spatial resolution of < λ/10, without needing any fitting parameters. In LiNbO 3 , we identify the in-plane orientation of its optical axis via the phonon modes, demonstrating that our method can be applied without prior knowledge of the crystal orientation. Furthermore, our method can be elegantly adapted to retrieve the in-plane anisotropic response of a broad range of materials, i.e. subwavelength microcrystals, van-der-Waals materials, or topological insulators.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Anomalous contrast in broadband THz near-field imaging of gold microstructures

THz scattering-type scanning near-field microscopy (s-SNOM) has become a powerful technique for measuring carrier dynamics in nanoscale materials and structures. Changes in a material’s local THz reflection or transmission can be correlated to changes in electrical conductivity. Here, we perform tip-based THz nano-imaging of subwavelength gold nanostructures and demonstrate image contrast unrelated to any spatially varying material properties. We show that the specific physical configuration of the gold structures can have a strong influence on local excitations which can obscure the sample’s true dielectric response, even in cases where the relevant structures are far outside of the spatial region probed by the AFM tip.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Effect of sample anisotropy on scanning near-field optical microscope images

Scattering-type scanning near-field optical microscopy (s-SNOM) has been widely used to characterize strongly correlated electronic, two dimensional, and plasmonic materials, and it has enormous potential for biological applications. Many of these materials exhibit anisotropic responses that complicate the extraction of dielectric constants from s-SNOM measurements. Here, we generalize our recently developed approach for retrieving the near-field scattering signal from isotropic systems and apply it to anisotropic dielectrics. Specifically, we compare our theoretical results with experimental measurements on modestly anisotropic sapphire that exhibit strong resonances at the infrared frequency range. Good agreement with the experimental result is found. Finally, our result is important for understanding the near-field response of low damping, anisotropic polaritonic states in dielectric media.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data

Scattering-type scanning near-field optical microscope (s-SNOM) is a modern technique for subdiffractional optical imaging and spectroscopy. Over the past two decades, tremendous efforts have been devoted to modeling complex tip-sample interactions in s-SNOM, aimed at understanding the electrodynamics of materials at the nanoscale. However, due to complexities in analytical methods and the limited computation power for fully numerical simulations, compromises must be made to facilitate the modeling of tip-sample interaction, such as using quasistatic approximation or unrealistic tip geometries. Here, we apply a variety of widely utilized machine-learning methods, including k nearest neighbor and feedforward neural network etc. to study the phase-resolved spectroscopic near-field response. With only a small set of training data, which is simulated using the finite-dipole model, we demonstrate that the relation between the experimental near-field signal and sample optical constant can be one to one mapped without the need for tip modeling: for a given material with a moderate dielectric function, its complex near-field spectrum can be accurately determined within the mid-IR spectral range, and vice versa. Our preliminary study sets the stage for future exploration using real experimental data. Our method is beneficial for processing the increasing amount of data accumulated across many research groups and especially useful for user facilities such as synchrotron-based national laboratories where a large amount of data is generated on a daily basis.

36 MATERIALS SCIENCE↗