Browsing by Author "Wu, Wenjing"

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    Manufacturing Chip-Scale 2D Monolayer Single Crystals and Engineering Quantum Emission in 2D Materials
    (2024-08-05) Wu, Wenjing; Huang, Shengxi; Kon, Junichiro
    Two-dimensional (2D) materials and their van der Waals (vdW) heterostructures continue to reveal unconventional electronic, optical, and magnetic phenomena closely tied to their dimensionality. In the first part of this thesis, we have demonstrated a facile method for producing uniform, large-area, and crack-free single-crystal transition metal dichalcogenide (TMD) monolayers and artificial structures: wafer-bonder-assisted transfer (WBAT). Compared with single-crystal monolayers produced via traditional Scotch tape exfoliation, the WBAT method can produce flakes that are larger in area by > 10^6 times with almost no cracks. In the second part, we focus on the creation of single photon emitters in the WSe2 and WS2 thin flakes, with defect and strain engineering. Our results show a nearly ideal single-photon purity with g^2(0) = 0.03 through effective spectral background suppression.
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    Measuring complex refractive index through deep-learning-enabled optical reflectometry
    (IOP Publishing, 2023) Wang, Ziyang; Lin, Yuxuan Cosmi; Zhang, Kunyan; Wu, Wenjing; Huang, Shengxi
    Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are well suited for interpreting high-dimensional and complicated spectroscopy data. They offer great opportunities to extract subtle and deep information about optical properties of materials with simpler optical setups, which would otherwise require sophisticated instrumentation. In this work, we propose a computational approach based on a conventional tabletop optical microscope and a deep learning model called ReflectoNet. Without any prior knowledge about the multilayer substrates, ReflectoNet can predict the complex refractive indices of thin films and 2D materials on top of these nontrivial substrates from experimentally measured optical reflectance spectra with high accuracies. This task was not feasible previously with traditional reflectometry or ellipsometry methods. Fundamental physical principles, such as the Kramers–Kronig relations, are spontaneously learned by the model without any further training. This approach enables in-operando optical characterization of functional materials and 2D materials within complex photonic structures or optoelectronic devices.